Podcasts about Hortonworks

Our guest for Episode 69 is Chris Taylor, Founder and President, OneMove Advisory. Before launching his own company, Chris held leadership roles at Databricks, Hortonworks, and TIBCO. He brings more than 30 years of experience to the conversation. In this episode, Ross and Chris share three tips for achieving execution excellence: structure, champion building, and teamwork.

If you're in SF, join us tomorrow for a fun meetup at CodeGen Night!If you're in NYC, join us for AI Engineer Summit! The Agent Engineering track is now sold out, but 25 tickets remain for AI Leadership and 5 tickets for the workshops. You can see the full schedule of speakers and workshops at https://ai.engineer!It's exceedingly hard to introduce someone like Bret Taylor. We could recite his Wikipedia page, or his extensive work history through Silicon Valley's greatest companies, but everyone else already does that.As a podcast by AI engineers for AI engineers, we had the opportunity to do something a little different. We wanted to dig into what Bret sees from his vantage point at the top of our industry for the last 2 decades, and how that explains the rise of the AI Architect at Sierra, the leading conversational AI/CX platform.“Across our customer base, we are seeing a new role emerge - the role of the AI architect. These leaders are responsible for helping define, manage and evolve their company's AI agent over time. They come from a variety of both technical and business backgrounds, and we think that every company will have one or many AI architects managing their AI agent and related experience.”In our conversation, Bret Taylor confirms the Paul Buchheit legend that he rewrote Google Maps in a weekend, armed with only the help of a then-nascent Google Closure Compiler and no other modern tooling. But what we find remarkable is that he was the PM of Maps, not an engineer, though of course he still identifies as one. We find this theme recurring throughout Bret's career and worldview. We think it is plain as day that AI leadership will have to be hands-on and technical, especially when the ground is shifting as quickly as it is today:“There's a lot of power in combining product and engineering into as few people as possible… few great things have been created by committee.”“If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a maniacal focus on outcomes.”“And I think the reason why is if you look at like software as a service five years ago, maybe you can have a separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of technological breakthroughs required for most business applications. And if you're making expense reporting software or whatever, it's useful… You kind of know how databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem. "When you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it and the capabilities of the technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself.”This is the first time the difference between technical leadership for “normal” software and for “AI” software was articulated this clearly for us, and we'll be thinking a lot about this going forward. We left a lot of nuggets in the conversation, so we hope you'll just dive in with us (and thank Bret for joining the pod!)Timestamps* 00:00:02 Introductions and Bret Taylor's background* 00:01:23 Bret's experience at Stanford and the dot-com era* 00:04:04 The story of rewriting Google Maps backend* 00:11:06 Early days of interactive web applications at Google* 00:15:26 Discussion on product management and engineering roles* 00:21:00 AI and the future of software development* 00:26:42 Bret's approach to identifying customer needs and building AI companies* 00:32:09 The evolution of business models in the AI era* 00:41:00 The future of programming languages and software development* 00:49:38 Challenges in precisely communicating human intent to machines* 00:56:44 Discussion on Artificial General Intelligence (AGI) and its impact* 01:08:51 The future of agent-to-agent communication* 01:14:03 Bret's involvement in the OpenAI leadership crisis* 01:22:11 OpenAI's relationship with Microsoft* 01:23:23 OpenAI's mission and priorities* 01:27:40 Bret's guiding principles for career choices* 01:29:12 Brief discussion on pasta-making* 01:30:47 How Bret keeps up with AI developments* 01:32:15 Exciting research directions in AI* 01:35:19 Closing remarks and hiring at Sierra Transcript[00:02:05] Introduction and Guest Welcome[00:02:05] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host swyx, founder of smol.ai.[00:02:17] swyx: Hey, and today we're super excited to have Bret Taylor join us. Welcome. Thanks for having me. It's a little unreal to have you in the studio.[00:02:25] swyx: I've read about you so much over the years, like even before. Open AI effectively. I mean, I use Google Maps to get here. So like, thank you for everything that you've done. Like, like your story history, like, you know, I think people can find out what your greatest hits have been.[00:02:40] Bret Taylor's Early Career and Education[00:02:40] swyx: How do you usually like to introduce yourself when, you know, you talk about, you summarize your career, like, how do you look at yourself?[00:02:47] Bret: Yeah, it's a great question. You know, we, before we went on the mics here, we're talking about the audience for this podcast being more engineering. And I do think depending on the audience, I'll introduce myself differently because I've had a lot of [00:03:00] corporate and board roles. I probably self identify as an engineer more than anything else though.[00:03:04] Bret: So even when I was. Salesforce, I was coding on the weekends. So I think of myself as an engineer and then all the roles that I do in my career sort of start with that just because I do feel like engineering is sort of a mindset and how I approach most of my life. So I'm an engineer first and that's how I describe myself.[00:03:24] Bret: You majored in computer[00:03:25] swyx: science, like 1998. And, and I was high[00:03:28] Bret: school, actually my, my college degree was Oh, two undergrad. Oh, three masters. Right. That old.[00:03:33] swyx: Yeah. I mean, no, I was going, I was going like 1998 to 2003, but like engineering wasn't as, wasn't a thing back then. Like we didn't have the title of senior engineer, you know, kind of like, it was just.[00:03:44] swyx: You were a programmer, you were a developer, maybe. What was it like in Stanford? Like, what was that feeling like? You know, was it, were you feeling like on the cusp of a great computer revolution? Or was it just like a niche, you know, interest at the time?[00:03:57] Stanford and the Dot-Com Bubble[00:03:57] Bret: Well, I was at Stanford, as you said, from 1998 to [00:04:00] 2002.[00:04:02] Bret: 1998 was near the peak of the dot com bubble. So. This is back in the day where most people that they're coding in the computer lab, just because there was these sun microsystems, Unix boxes there that most of us had to do our assignments on. And every single day there was a. com like buying pizza for everybody.[00:04:20] Bret: I didn't have to like, I got. Free food, like my first two years of university and then the dot com bubble burst in the middle of my college career. And so by the end there was like tumbleweed going to the job fair, you know, it was like, cause it was hard to describe unless you were there at the time, the like level of hype and being a computer science major at Stanford was like, A thousand opportunities.[00:04:45] Bret: And then, and then when I left, it was like Microsoft, IBM.[00:04:49] Joining Google and Early Projects[00:04:49] Bret: And then the two startups that I applied to were VMware and Google. And I ended up going to Google in large part because a woman named Marissa Meyer, who had been a teaching [00:05:00] assistant when I was, what was called a section leader, which was like a junior teaching assistant kind of for one of the big interest.[00:05:05] Bret: Yes. Classes. She had gone there. And she was recruiting me and I knew her and it was sort of felt safe, you know, like, I don't know. I thought about it much, but it turned out to be a real blessing. I realized like, you know, you always want to think you'd pick Google if given the option, but no one knew at the time.[00:05:20] Bret: And I wonder if I'd graduated in like 1999 where I've been like, mom, I just got a job at pets. com. It's good. But you know, at the end I just didn't have any options. So I was like, do I want to go like make kernel software at VMware? Do I want to go build search at Google? And I chose Google. 50, 50 ball.[00:05:36] Bret: I'm not really a 50, 50 ball. So I feel very fortunate in retrospect that the economy collapsed because in some ways it forced me into like one of the greatest companies of all time, but I kind of lucked into it, I think.[00:05:47] The Google Maps Rewrite Story[00:05:47] Alessio: So the famous story about Google is that you rewrote the Google maps back in, in one week after the map quest quest maps acquisition, what was the story there?[00:05:57] Alessio: Is it. Actually true. Is it [00:06:00] being glorified? Like how, how did that come to be? And is there any detail that maybe Paul hasn't shared before?[00:06:06] Bret: It's largely true, but I'll give the color commentary. So it was actually the front end, not the back end, but it turns out for Google maps, the front end was sort of the hard part just because Google maps was.[00:06:17] Bret: Largely the first ish kind of really interactive web application, say first ish. I think Gmail certainly was though Gmail, probably a lot of people then who weren't engineers probably didn't appreciate its level of interactivity. It was just fast, but. Google maps, because you could drag the map and it was sort of graphical.[00:06:38] Bret: My, it really in the mainstream, I think, was it a map[00:06:41] swyx: quest back then that was, you had the arrows up and down, it[00:06:44] Bret: was up and down arrows. Each map was a single image and you just click left and then wait for a few seconds to the new map to let it was really small too, because generating a big image was kind of expensive on computers that day.[00:06:57] Bret: So Google maps was truly innovative in that [00:07:00] regard. The story on it. There was a small company called where two technologies started by two Danish brothers, Lars and Jens Rasmussen, who are two of my closest friends now. They had made a windows app called expedition, which had beautiful maps. Even in 2000.[00:07:18] Bret: For whenever we acquired or sort of acquired their company, Windows software was not particularly fashionable, but they were really passionate about mapping and we had made a local search product that was kind of middling in terms of popularity, sort of like a yellow page of search product. So we wanted to really go into mapping.[00:07:36] Bret: We'd started working on it. Their small team seemed passionate about it. So we're like, come join us. We can build this together.[00:07:42] Technical Challenges and Innovations[00:07:42] Bret: It turned out to be a great blessing that they had built a windows app because you're less technically constrained when you're doing native code than you are building a web browser, particularly back then when there weren't really interactive web apps and it ended up.[00:07:56] Bret: Changing the level of quality that we [00:08:00] wanted to hit with the app because we were shooting for something that felt like a native windows application. So it was a really good fortune that we sort of, you know, their unusual technical choices turned out to be the greatest blessing. So we spent a lot of time basically saying, how can you make a interactive draggable map in a web browser?[00:08:18] Bret: How do you progressively load, you know, new map tiles, you know, as you're dragging even things like down in the weeds of the browser at the time, most browsers like Internet Explorer, which was dominant at the time would only load two images at a time from the same domain. So we ended up making our map tile servers have like.[00:08:37] Bret: Forty different subdomains so we could load maps and parallels like lots of hacks. I'm happy to go into as much as like[00:08:44] swyx: HTTP connections and stuff.[00:08:46] Bret: They just like, there was just maximum parallelism of two. And so if you had a map, set of map tiles, like eight of them, so So we just, we were down in the weeds of the browser anyway.[00:08:56] Bret: So it was lots of plumbing. I can, I know a lot more about browsers than [00:09:00] most people, but then by the end of it, it was fairly, it was a lot of duct tape on that code. If you've ever done an engineering project where you're not really sure the path from point A to point B, it's almost like. Building a house by building one room at a time.[00:09:14] Bret: The, there's not a lot of architectural cohesion at the end. And then we acquired a company called Keyhole, which became Google earth, which was like that three, it was a native windows app as well, separate app, great app, but with that, we got licenses to all this satellite imagery. And so in August of 2005, we added.[00:09:33] Bret: Satellite imagery to Google Maps, which added even more complexity in the code base. And then we decided we wanted to support Safari. There was no mobile phones yet. So Safari was this like nascent browser on, on the Mac. And it turns out there's like a lot of decisions behind the scenes, sort of inspired by this windows app, like heavy use of XML and XSLT and all these like.[00:09:54] Bret: Technologies that were like briefly fashionable in the early two thousands and everyone hates now for good [00:10:00] reason. And it turns out that all of the XML functionality and Internet Explorer wasn't supporting Safari. So people are like re implementing like XML parsers. And it was just like this like pile of s**t.[00:10:11] Bret: And I had to say a s**t on your part. Yeah, of[00:10:12] Alessio: course.[00:10:13] Bret: So. It went from this like beautifully elegant application that everyone was proud of to something that probably had hundreds of K of JavaScript, which sounds like nothing. Now we're talking like people have modems, you know, not all modems, but it was a big deal.[00:10:29] Bret: So it was like slow. It took a while to load and just, it wasn't like a great code base. Like everything was fragile. So I just got. Super frustrated by it. And then one weekend I did rewrite all of it. And at the time the word JSON hadn't been coined yet too, just to give you a sense. So it's all XML.[00:10:47] swyx: Yeah.[00:10:47] Bret: So we used what is now you would call JSON, but I just said like, let's use eval so that we can parse the data fast. And, and again, that's, it would literally as JSON, but at the time there was no name for it. So we [00:11:00] just said, let's. Pass on JavaScript from the server and eval it. And then somebody just refactored the whole thing.[00:11:05] Bret: And, and it wasn't like I was some genius. It was just like, you know, if you knew everything you wished you had known at the beginning and I knew all the functionality, cause I was the primary, one of the primary authors of the JavaScript. And I just like, I just drank a lot of coffee and just stayed up all weekend.[00:11:22] Bret: And then I, I guess I developed a bit of reputation and no one knew about this for a long time. And then Paul who created Gmail and I ended up starting a company with him too, after all of this told this on a podcast and now it's large, but it's largely true. I did rewrite it and it, my proudest thing.[00:11:38] Bret: And I think JavaScript people appreciate this. Like the un G zipped bundle size for all of Google maps. When I rewrote, it was 20 K G zipped. It was like much smaller for the entire application. It went down by like 10 X. So. What happened on Google? Google is a pretty mainstream company. And so like our usage is shot up because it turns out like it's faster.[00:11:57] Bret: Just being faster is worth a lot of [00:12:00] percentage points of growth at a scale of Google. So how[00:12:03] swyx: much modern tooling did you have? Like test suites no compilers.[00:12:07] Bret: Actually, that's not true. We did it one thing. So I actually think Google, I, you can. Download it. There's a, Google has a closure compiler, a closure compiler.[00:12:15] Bret: I don't know if anyone still uses it. It's gone. Yeah. Yeah. It's sort of gone out of favor. Yeah. Well, even until recently it was better than most JavaScript minifiers because it was more like it did a lot more renaming of variables and things. Most people use ES build now just cause it's fast and closure compilers built on Java and super slow and stuff like that.[00:12:37] Bret: But, so we did have that, that was it. Okay.[00:12:39] The Evolution of Web Applications[00:12:39] Bret: So and that was treated internally, you know, it was a really interesting time at Google at the time because there's a lot of teams working on fairly advanced JavaScript when no one was. So Google suggest, which Kevin Gibbs was the tech lead for, was the first kind of type ahead, autocomplete, I believe in a web browser, and now it's just pervasive in search boxes that you sort of [00:13:00] see a type ahead there.[00:13:01] Bret: I mean, chat, dbt[00:13:01] swyx: just added it. It's kind of like a round trip.[00:13:03] Bret: Totally. No, it's now pervasive as a UI affordance, but that was like Kevin's 20 percent project. And then Gmail, Paul you know, he tells the story better than anyone, but he's like, you know, basically was scratching his own itch, but what was really neat about it is email, because it's such a productivity tool, just needed to be faster.[00:13:21] Bret: So, you know, he was scratching his own itch of just making more stuff work on the client side. And then we, because of Lars and Yen sort of like setting the bar of this windows app or like we need our maps to be draggable. So we ended up. Not only innovate in terms of having a big sync, what would be called a single page application today, but also all the graphical stuff you know, we were crashing Firefox, like it was going out of style because, you know, when you make a document object model with the idea that it's a document and then you layer on some JavaScript and then we're essentially abusing all of this, it just was running into code paths that were not.[00:13:56] Bret: Well, it's rotten, you know, at this time. And so it was [00:14:00] super fun. And, and, you know, in the building you had, so you had compilers, people helping minify JavaScript just practically, but there is a great engineering team. So they were like, that's why Closure Compiler is so good. It was like a. Person who actually knew about programming languages doing it, not just, you know, writing regular expressions.[00:14:17] Bret: And then the team that is now the Chrome team believe, and I, I don't know this for a fact, but I'm pretty sure Google is the main contributor to Firefox for a long time in terms of code. And a lot of browser people were there. So every time we would crash Firefox, we'd like walk up two floors and say like, what the hell is going on here?[00:14:35] Bret: And they would load their browser, like in a debugger. And we could like figure out exactly what was breaking. And you can't change the code, right? Cause it's the browser. It's like slow, right? I mean, slow to update. So, but we could figure out exactly where the bug was and then work around it in our JavaScript.[00:14:52] Bret: So it was just like new territory. Like so super, super fun time, just like a lot of, a lot of great engineers figuring out [00:15:00] new things. And And now, you know, the word, this term is no longer in fashion, but the word Ajax, which was asynchronous JavaScript and XML cause I'm telling you XML, but see the word XML there, to be fair, the way you made HTTP requests from a client to server was this.[00:15:18] Bret: Object called XML HTTP request because Microsoft and making Outlook web access back in the day made this and it turns out to have nothing to do with XML. It's just a way of making HTTP requests because XML was like the fashionable thing. It was like that was the way you, you know, you did it. But the JSON came out of that, you know, and then a lot of the best practices around building JavaScript applications is pre React.[00:15:44] Bret: I think React was probably the big conceptual step forward that we needed. Even my first social network after Google, we used a lot of like HTML injection and. Making real time updates was still very hand coded and it's really neat when you [00:16:00] see conceptual breakthroughs like react because it's, I just love those things where it's like obvious once you see it, but it's so not obvious until you do.[00:16:07] Bret: And actually, well, I'm sure we'll get into AI, but I, I sort of feel like we'll go through that evolution with AI agents as well that I feel like we're missing a lot of the core abstractions that I think in 10 years we'll be like, gosh, how'd you make agents? Before that, you know, but it was kind of that early days of web applications.[00:16:22] swyx: There's a lot of contenders for the reactive jobs of of AI, but no clear winner yet. I would say one thing I was there for, I mean, there's so much we can go into there. You just covered so much.[00:16:32] Product Management and Engineering Synergy[00:16:32] swyx: One thing I just, I just observe is that I think the early Google days had this interesting mix of PM and engineer, which I think you are, you didn't, you didn't wait for PM to tell you these are my, this is my PRD.[00:16:42] swyx: This is my requirements.[00:16:44] mix: Oh,[00:16:44] Bret: okay.[00:16:45] swyx: I wasn't technically a software engineer. I mean,[00:16:48] Bret: by title, obviously. Right, right, right.[00:16:51] swyx: It's like a blend. And I feel like these days, product is its own discipline and its own lore and own industry and engineering is its own thing. And there's this process [00:17:00] that happens and they're kind of separated, but you don't produce as good of a product as if they were the same person.[00:17:06] swyx: And I'm curious, you know, if, if that, if that sort of resonates in, in, in terms of like comparing early Google versus modern startups that you see out there,[00:17:16] Bret: I certainly like wear a lot of hats. So, you know, sort of biased in this, but I really agree that there's a lot of power and combining product design engineering into as few people as possible because, you know few great things have been created by committee, you know, and so.[00:17:33] Bret: If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a. Maniacal focus on outcomes.[00:17:53] Bret: And I think the reason why it's, I think for some areas, if you look at like software as a service five years ago, maybe you can have a [00:18:00] separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of like. Technological breakthroughs required for most, you know, business applications.[00:18:11] Bret: And if you're making expense reporting software or whatever, it's useful. I don't mean to be dismissive of expense reporting software, but you probably just want to understand like, what are the requirements of the finance department? What are the requirements of an individual file expense report? Okay.[00:18:25] Bret: Go implement that. And you kind of know how web applications are implemented. You kind of know how to. How databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem when you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it.[00:18:58] Bret: And the capabilities of the [00:19:00] technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself. And that's why I use the word conversation. It's not literal. That's sort of funny to use that word in the age of conversational AI.[00:19:15] Bret: You're constantly sort of saying, like, ideally, you could sprinkle some magic AI pixie dust and solve all the world's problems, but it's not the way it works. And it turns out that actually, I'll just give an interesting example.[00:19:26] AI Agents and Modern Tooling[00:19:26] Bret: I think most people listening probably use co pilots to code like Cursor or Devon or Microsoft Copilot or whatever.[00:19:34] Bret: Most of those tools are, they're remarkable. I'm, I couldn't, you know, imagine development without them now, but they're not autonomous yet. Like I wouldn't let it just write most code without my interactively inspecting it. We just are somewhere between it's an amazing co pilot and it's an autonomous software engineer.[00:19:53] Bret: As a product manager, like your aspirations for what the product is are like kind of meaningful. But [00:20:00] if you're a product person, yeah, of course you'd say it should be autonomous. You should click a button and program should come out the other side. The requirements meaningless. Like what matters is like, what is based on the like very nuanced limitations of the technology.[00:20:14] Bret: What is it capable of? And then how do you maximize the leverage? It gives a software engineering team, given those very nuanced trade offs. Coupled with the fact that those nuanced trade offs are changing more rapidly than any technology in my memory, meaning every few months you'll have new models with new capabilities.[00:20:34] Bret: So how do you construct a product that can absorb those new capabilities as rapidly as possible as well? That requires such a combination of technical depth and understanding the customer that you really need more integration. Of product design and engineering. And so I think it's why with these big technology waves, I think startups have a bit of a leg up relative to incumbents because they [00:21:00] tend to be sort of more self actualized in terms of just like bringing those disciplines closer together.[00:21:06] Bret: And in particular, I think entrepreneurs, the proverbial full stack engineers, you know, have a leg up as well because. I think most breakthroughs happen when you have someone who can understand those extremely nuanced technical trade offs, have a vision for a product. And then in the process of building it, have that, as I said, like metaphorical conversation with the technology, right?[00:21:30] Bret: Gosh, I ran into a technical limit that I didn't expect. It's not just like changing that feature. You might need to refactor the whole product based on that. And I think that's, that it's particularly important right now. So I don't, you know, if you, if you're building a big ERP system, probably there's a great reason to have product and engineering.[00:21:51] Bret: I think in general, the disciplines are there for a reason. I think when you're dealing with something as nuanced as the like technologies, like large language models today, there's a ton of [00:22:00] advantage of having. Individuals or organizations that integrate the disciplines more formally.[00:22:05] Alessio: That makes a lot of sense.[00:22:06] Alessio: I've run a lot of engineering teams in the past, and I think the product versus engineering tension has always been more about effort than like whether or not the feature is buildable. But I think, yeah, today you see a lot more of like. Models actually cannot do that. And I think the most interesting thing is on the startup side, people don't yet know where a lot of the AI value is going to accrue.[00:22:26] Alessio: So you have this rush of people building frameworks, building infrastructure, layered things, but we don't really know the shape of the compute. I'm curious that Sierra, like how you thought about building an house, a lot of the tooling for evals or like just, you know, building the agents and all of that.[00:22:41] Alessio: Versus how you see some of the startup opportunities that is maybe still out there.[00:22:46] Bret: We build most of our tooling in house at Sierra, not all. It's, we don't, it's not like not invented here syndrome necessarily, though, maybe slightly guilty of that in some ways, but because we're trying to build a platform [00:23:00] that's in Dorian, you know, we really want to have control over our own destiny.[00:23:03] Bret: And you had made a comment earlier that like. We're still trying to figure out who like the reactive agents are and the jury is still out. I would argue it hasn't been created yet. I don't think the jury is still out to go use that metaphor. We're sort of in the jQuery era of agents, not the react era.[00:23:19] Bret: And, and that's like a throwback for people listening,[00:23:22] swyx: we shouldn't rush it. You know?[00:23:23] Bret: No, yeah, that's my point is. And so. Because we're trying to create an enduring company at Sierra that outlives us, you know, I'm not sure we want to like attach our cart to some like to a horse where it's not clear that like we've figured out and I actually want as a company, we're trying to enable just at a high level and I'll, I'll quickly go back to tech at Sierra, we help consumer brands build customer facing AI agents.[00:23:48] Bret: So. Everyone from Sonos to ADT home security to Sirius XM, you know, if you call them on the phone and AI will pick up with you, you know, chat with them on the Sirius XM homepage. It's an AI agent called Harmony [00:24:00] that they've built on our platform. We're what are the contours of what it means for someone to build an end to end complete customer experience with AI with conversational AI.[00:24:09] Bret: You know, we really want to dive into the deep end of, of all the trade offs to do it. You know, where do you use fine tuning? Where do you string models together? You know, where do you use reasoning? Where do you use generation? How do you use reasoning? How do you express the guardrails of an agentic process?[00:24:25] Bret: How do you impose determinism on a fundamentally non deterministic technology? There's just a lot of really like as an important design space. And I could sit here and tell you, we have the best approach. Every entrepreneur will, you know. But I hope that in two years, we look back at our platform and laugh at how naive we were, because that's the pace of change broadly.[00:24:45] Bret: If you talk about like the startup opportunities, I'm not wholly skeptical of tools companies, but I'm fairly skeptical. There's always an exception for every role, but I believe that certainly there's a big market for [00:25:00] frontier models, but largely for companies with huge CapEx budgets. So. Open AI and Microsoft's Anthropic and Amazon Web Services, Google Cloud XAI, which is very well capitalized now, but I think the, the idea that a company can make money sort of pre training a foundation model is probably not true.[00:25:20] Bret: It's hard to, you're competing with just, you know, unreasonably large CapEx budgets. And I just like the cloud infrastructure market, I think will be largely there. I also really believe in the applications of AI. And I define that not as like building agents or things like that. I define it much more as like, you're actually solving a problem for a business.[00:25:40] Bret: So it's what Harvey is doing in legal profession or what cursor is doing for software engineering or what we're doing for customer experience and customer service. The reason I believe in that is I do think that in the age of AI, what's really interesting about software is it can actually complete a task.[00:25:56] Bret: It can actually do a job, which is very different than the value proposition of [00:26:00] software was to ancient history two years ago. And as a consequence, I think the way you build a solution and For a domain is very different than you would have before, which means that it's not obvious, like the incumbent incumbents have like a leg up, you know, necessarily, they certainly have some advantages, but there's just such a different form factor, you know, for providing a solution and it's just really valuable.[00:26:23] Bret: You know, it's. Like just think of how much money cursor is saving software engineering teams or the alternative, how much revenue it can produce tool making is really challenging. If you look at the cloud market, just as a analog, there are a lot of like interesting tools, companies, you know, Confluent, Monetized Kafka, Snowflake, Hortonworks, you know, there's a, there's a bunch of them.[00:26:48] Bret: A lot of them, you know, have that mix of sort of like like confluence or have the open source or open core or whatever you call it. I, I, I'm not an expert in this area. You know, I do think [00:27:00] that developers are fickle. I think that in the tool space, I probably like. Default towards open source being like the area that will win.[00:27:09] Bret: It's hard to build a company around this and then you end up with companies sort of built around open source to that can work. Don't get me wrong, but I just think that it's nowadays the tools are changing so rapidly that I'm like, not totally skeptical of tool makers, but I just think that open source will broadly win, but I think that the CapEx required for building frontier models is such that it will go to a handful of big companies.[00:27:33] Bret: And then I really believe in agents for specific domains which I think will, it's sort of the analog to software as a service in this new era. You know, it's like, if you just think of the cloud. You can lease a server. It's just a low level primitive, or you can buy an app like you know, Shopify or whatever.[00:27:51] Bret: And most people building a storefront would prefer Shopify over hand rolling their e commerce storefront. I think the same thing will be true of AI. So [00:28:00] I've. I tend to like, if I have a, like an entrepreneur asked me for advice, I'm like, you know, move up the stack as far as you can towards a customer need.[00:28:09] Bret: Broadly, but I, but it doesn't reduce my excitement about what is the reactive building agents kind of thing, just because it is, it is the right question to ask, but I think we'll probably play out probably an open source space more than anything else.[00:28:21] swyx: Yeah, and it's not a priority for you. There's a lot in there.[00:28:24] swyx: I'm kind of curious about your idea maze towards, there are many customer needs. You happen to identify customer experience as yours, but it could equally have been coding assistance or whatever. I think for some, I'm just kind of curious at the top down, how do you look at the world in terms of the potential problem space?[00:28:44] swyx: Because there are many people out there who are very smart and pick the wrong problem.[00:28:47] Bret: Yeah, that's a great question.[00:28:48] Future of Software Development[00:28:48] Bret: By the way, I would love to talk about the future of software, too, because despite the fact it didn't pick coding, I have a lot of that, but I can talk to I can answer your question, though, you know I think when a technology is as [00:29:00] cool as large language models.[00:29:02] Bret: You just see a lot of people starting from the technology and searching for a problem to solve. And I think it's why you see a lot of tools companies, because as a software engineer, you start building an app or a demo and you, you encounter some pain points. You're like,[00:29:17] swyx: a lot of[00:29:17] Bret: people are experiencing the same pain point.[00:29:19] Bret: What if I make it? That it's just very incremental. And you know, I always like to use the metaphor, like you can sell coffee beans, roasted coffee beans. You can add some value. You took coffee beans and you roasted them and roasted coffee beans largely, you know, are priced relative to the cost of the beans.[00:29:39] Bret: Or you can sell a latte and a latte. Is rarely priced directly like as a percentage of coffee bean prices. In fact, if you buy a latte at the airport, it's a captive audience. So it's a really expensive latte. And there's just a lot that goes into like. How much does a latte cost? And I bring it up because there's a supply chain from growing [00:30:00] coffee beans to roasting coffee beans to like, you know, you could make one at home or you could be in the airport and buy one and the margins of the company selling lattes in the airport is a lot higher than the, you know, people roasting the coffee beans and it's because you've actually solved a much more acute human problem in the airport.[00:30:19] Bret: And, and it's just worth a lot more to that person in that moment. It's kind of the way I think about technology too. It sounds funny to liken it to coffee beans, but you're selling tools on top of a large language model yet in some ways your market is big, but you're probably going to like be price compressed just because you're sort of a piece of infrastructure and then you have open source and all these other things competing with you naturally.[00:30:43] Bret: If you go and solve a really big business problem for somebody, that's actually like a meaningful business problem that AI facilitates, they will value it according to the value of that business problem. And so I actually feel like people should just stop. You're like, no, that's, that's [00:31:00] unfair. If you're searching for an idea of people, I, I love people trying things, even if, I mean, most of the, a lot of the greatest ideas have been things no one believed in.[00:31:07] Bret: So I like, if you're passionate about something, go do it. Like who am I to say, yeah, a hundred percent. Or Gmail, like Paul as far, I mean I, some of it's Laura at this point, but like Gmail is Paul's own email for a long time. , and then I amusingly and Paul can't correct me, I'm pretty sure he sent her in a link and like the first comment was like, this is really neat.[00:31:26] Bret: It would be great. It was not your email, but my own . I don't know if it's a true story. I'm pretty sure it's, yeah, I've read that before. So scratch your own niche. Fine. Like it depends on what your goal is. If you wanna do like a venture backed company, if its a. Passion project, f*****g passion, do it like don't listen to anybody.[00:31:41] Bret: In fact, but if you're trying to start, you know an enduring company, solve an important business problem. And I, and I do think that in the world of agents, the software industries has shifted where you're not just helping people more. People be more productive, but you're actually accomplishing tasks autonomously.[00:31:58] Bret: And as a consequence, I think the [00:32:00] addressable market has just greatly expanded just because software can actually do things now and actually accomplish tasks and how much is coding autocomplete worth. A fair amount. How much is the eventual, I'm certain we'll have it, the software agent that actually writes the code and delivers it to you, that's worth a lot.[00:32:20] Bret: And so, you know, I would just maybe look up from the large language models and start thinking about the economy and, you know, think from first principles. I don't wanna get too far afield, but just think about which parts of the economy. We'll benefit most from this intelligence and which parts can absorb it most easily.[00:32:38] Bret: And what would an agent in this space look like? Who's the customer of it is the technology feasible. And I would just start with these business problems more. And I think, you know, the best companies tend to have great engineers who happen to have great insight into a market. And it's that last part that I think some people.[00:32:56] Bret: Whether or not they have, it's like people start so much in the technology, they [00:33:00] lose the forest for the trees a little bit.[00:33:02] Alessio: How do you think about the model of still selling some sort of software versus selling more package labor? I feel like when people are selling the package labor, it's almost more stateless, you know, like it's easier to swap out if you're just putting an input and getting an output.[00:33:16] Alessio: If you think about coding, if there's no ID, you're just putting a prompt and getting back an app. It doesn't really matter. Who generates the app, you know, you have less of a buy in versus the platform you're building, I'm sure on the backend customers have to like put on their documentation and they have, you know, different workflows that they can tie in what's kind of like the line to draw there versus like going full where you're managed customer support team as a service outsource versus.[00:33:40] Alessio: This is the Sierra platform that you can build on. What was that decision? I'll sort of[00:33:44] Bret: like decouple the question in some ways, which is when you have something that's an agent, who is the person using it and what do they want to do with it? So let's just take your coding agent for a second. I will talk about Sierra as well.[00:33:59] Bret: Who's the [00:34:00] customer of a, an agent that actually produces software? Is it a software engineering manager? Is it a software engineer? And it's there, you know, intern so to speak. I don't know. I mean, we'll figure this out over the next few years. Like what is that? And is it generating code that you then review?[00:34:16] Bret: Is it generating code with a set of unit tests that pass, what is the actual. For lack of a better word contract, like, how do you know that it did what you wanted it to do? And then I would say like the product and the pricing, the packaging model sort of emerged from that. And I don't think the world's figured out.[00:34:33] Bret: I think it'll be different for every agent. You know, in our customer base, we do what's called outcome based pricing. So essentially every time the AI agent. Solves the problem or saves a customer or whatever it might be. There's a pre negotiated rate for that. We do that. Cause it's, we think that that's sort of the correct way agents, you know, should be packaged.[00:34:53] Bret: I look back at the history of like cloud software and notably the introduction of the browser, which led to [00:35:00] software being delivered in a browser, like Salesforce to. Famously invented sort of software as a service, which is both a technical delivery model through the browser, but also a business model, which is you subscribe to it rather than pay for a perpetual license.[00:35:13] Bret: Those two things are somewhat orthogonal, but not really. If you think about the idea of software running in a browser, that's hosted. Data center that you don't own, you sort of needed to change the business model because you don't, you can't really buy a perpetual license or something otherwise like, how do you afford making changes to it?[00:35:31] Bret: So it only worked when you were buying like a new version every year or whatever. So to some degree, but then the business model shift actually changed business as we know it, because now like. Things like Adobe Photoshop. Now you subscribe to rather than purchase. So it ended up where you had a technical shift and a business model shift that were very logically intertwined that actually the business model shift was turned out to be as significant as the technical as the shift.[00:35:59] Bret: And I think with [00:36:00] agents, because they actually accomplish a job, I do think that it doesn't make sense to me that you'd pay for the privilege of like. Using the software like that coding agent, like if it writes really bad code, like fire it, you know, I don't know what the right metaphor is like you should pay for a job.[00:36:17] Bret: Well done in my opinion. I mean, that's how you pay your software engineers, right? And[00:36:20] swyx: and well, not really. We paid to put them on salary and give them options and they vest over time. That's fair.[00:36:26] Bret: But my point is that you don't pay them for how many characters they write, which is sort of the token based, you know, whatever, like, There's a, that famous Apple story where we're like asking for a report of how many lines of code you wrote.[00:36:40] Bret: And one of the engineers showed up with like a negative number cause he had just like done a big refactoring. There was like a big F you to management who didn't understand how software is written. You know, my sense is like the traditional usage based or seat based thing. It's just going to look really antiquated.[00:36:55] Bret: Cause it's like asking your software engineer, how many lines of code did you write today? Like who cares? Like, cause [00:37:00] absolutely no correlation. So my old view is I don't think it's be different in every category, but I do think that that is the, if an agent is doing a job, you should, I think it properly incentivizes the maker of that agent and the customer of, of your pain for the job well done.[00:37:16] Bret: It's not always perfect to measure. It's hard to measure engineering productivity, but you can, you should do something other than how many keys you typed, you know Talk about perverse incentives for AI, right? Like I can write really long functions to do the same thing, right? So broadly speaking, you know, I do think that we're going to see a change in business models of software towards outcomes.[00:37:36] Bret: And I think you'll see a change in delivery models too. And, and, you know, in our customer base you know, we empower our customers to really have their hands on the steering wheel of what the agent does they, they want and need that. But the role is different. You know, at a lot of our customers, the customer experience operations folks have renamed themselves the AI architects, which I think is really cool.[00:37:55] Bret: And, you know, it's like in the early days of the Internet, there's the role of the webmaster. [00:38:00] And I don't know whether your webmaster is not a fashionable, you know, Term, nor is it a job anymore? I just, I don't know. Will they, our tech stand the test of time? Maybe, maybe not. But I do think that again, I like, you know, because everyone listening right now is a software engineer.[00:38:14] Bret: Like what is the form factor of a coding agent? And actually I'll, I'll take a breath. Cause actually I have a bunch of pins on them. Like I wrote a blog post right before Christmas, just on the future of software development. And one of the things that's interesting is like, if you look at the way I use cursor today, as an example, it's inside of.[00:38:31] Bret: A repackaged visual studio code environment. I sometimes use the sort of agentic parts of it, but it's largely, you know, I've sort of gotten a good routine of making it auto complete code in the way I want through tuning it properly when it actually can write. I do wonder what like the future of development environments will look like.[00:38:55] Bret: And to your point on what is a software product, I think it's going to change a lot in [00:39:00] ways that will surprise us. But I always use, I use the metaphor in my blog post of, have you all driven around in a way, Mo around here? Yeah, everyone has. And there are these Jaguars, the really nice cars, but it's funny because it still has a steering wheel, even though there's no one sitting there and the steering wheels like turning and stuff clearly in the future.[00:39:16] Bret: If once we get to that, be more ubiquitous, like why have the steering wheel and also why have all the seats facing forward? Maybe just for car sickness. I don't know, but you could totally rearrange the car. I mean, so much of the car is oriented around the driver, so. It stands to reason to me that like, well, autonomous agents for software engineering run through visual studio code.[00:39:37] Bret: That seems a little bit silly because having a single source code file open one at a time is kind of a goofy form factor for when like the code isn't being written primarily by you, but it begs the question of what's your relationship with that agent. And I think the same is true in our industry of customer experience, which is like.[00:39:55] Bret: Who are the people managing this agent? What are the tools do they need? And they definitely need [00:40:00] tools, but it's probably pretty different than the tools we had before. It's certainly different than training a contact center team. And as software engineers, I think that I would like to see particularly like on the passion project side or research side.[00:40:14] Bret: More innovation in programming languages. I think that we're bringing the cost of writing code down to zero. So the fact that we're still writing Python with AI cracks me up just cause it's like literally was designed to be ergonomic to write, not safe to run or fast to run. I would love to see more innovation and how we verify program correctness.[00:40:37] Bret: I studied for formal verification in college a little bit and. It's not very fashionable because it's really like tedious and slow and doesn't work very well. If a lot of code is being written by a machine, you know, one of the primary values we can provide is verifying that it actually does what we intend that it does.[00:40:56] Bret: I think there should be lots of interesting things in the software development life cycle, like how [00:41:00] we think of testing and everything else, because. If you think about if we have to manually read every line of code that's coming out as machines, it will just rate limit how much the machines can do. The alternative is totally unsafe.[00:41:13] Bret: So I wouldn't want to put code in production that didn't go through proper code review and inspection. So my whole view is like, I actually think there's like an AI native I don't think the coding agents don't work well enough to do this yet, but once they do, what is sort of an AI native software development life cycle and how do you actually.[00:41:31] Bret: Enable the creators of software to produce the highest quality, most robust, fastest software and know that it's correct. And I think that's an incredible opportunity. I mean, how much C code can we rewrite and rust and make it safe so that there's fewer security vulnerabilities. Can we like have more efficient, safer code than ever before?[00:41:53] Bret: And can you have someone who's like that guy in the matrix, you know, like staring at the little green things, like where could you have an operator [00:42:00] of a code generating machine be like superhuman? I think that's a cool vision. And I think too many people are focused on like. Autocomplete, you know, right now, I'm not, I'm not even, I'm guilty as charged.[00:42:10] Bret: I guess in some ways, but I just like, I'd like to see some bolder ideas. And that's why when you were joking, you know, talking about what's the react of whatever, I think we're clearly in a local maximum, you know, metaphor, like sort of conceptual local maximum, obviously it's moving really fast. I think we're moving out of it.[00:42:26] Alessio: Yeah. At the end of 23, I've read this blog post from syntax to semantics. Like if you think about Python. It's taking C and making it more semantic and LLMs are like the ultimate semantic program, right? You can just talk to them and they can generate any type of syntax from your language. But again, the languages that they have to use were made for us, not for them.[00:42:46] Alessio: But the problem is like, as long as you will ever need a human to intervene, you cannot change the language under it. You know what I mean? So I'm curious at what point of automation we'll need to get, we're going to be okay making changes. To the underlying languages, [00:43:00] like the programming languages versus just saying, Hey, you just got to write Python because I understand Python and I'm more important at the end of the day than the model.[00:43:08] Alessio: But I think that will change, but I don't know if it's like two years or five years. I think it's more nuanced actually.[00:43:13] Bret: So I think there's a, some of the more interesting programming languages bring semantics into syntax. So let me, that's a little reductive, but like Rust as an example, Rust is memory safe.[00:43:25] Bret: Statically, and that was a really interesting conceptual, but it's why it's hard to write rust. It's why most people write python instead of rust. I think rust programs are safer and faster than python, probably slower to compile. But like broadly speaking, like given the option, if you didn't have to care about the labor that went into it.[00:43:45] Bret: You should prefer a program written in Rust over a program written in Python, just because it will run more efficiently. It's almost certainly safer, et cetera, et cetera, depending on how you define safe, but most people don't write Rust because it's kind of a pain in the ass. And [00:44:00] the audience of people who can is smaller, but it's sort of better in most, most ways.[00:44:05] Bret: And again, let's say you're making a web service and you didn't have to care about how hard it was to write. If you just got the output of the web service, the rest one would be cheaper to operate. It's certainly cheaper and probably more correct just because there's so much in the static analysis implied by the rest programming language that it probably will have fewer runtime errors and things like that as well.[00:44:25] Bret: So I just give that as an example, because so rust, at least my understanding that came out of the Mozilla team, because. There's lots of security vulnerabilities in the browser and it needs to be really fast. They said, okay, we want to put more of a burden at the authorship time to have fewer issues at runtime.[00:44:43] Bret: And we need the constraint that it has to be done statically because browsers need to be really fast. My sense is if you just think about like the, the needs of a programming language today, where the role of a software engineer is [00:45:00] to use an AI to generate functionality and audit that it does in fact work as intended, maybe functionally, maybe from like a correctness standpoint, some combination thereof, how would you create a programming system that facilitated that?[00:45:15] Bret: And, you know, I bring up Rust is because I think it's a good example of like, I think given a choice of writing in C or Rust, you should choose Rust today. I think most people would say that, even C aficionados, just because. C is largely less safe for very similar, you know, trade offs, you know, for the, the system and now with AI, it's like, okay, well, that just changes the game on writing these things.[00:45:36] Bret: And so like, I just wonder if a combination of programming languages that are more structurally oriented towards the values that we need from an AI generated program, verifiable correctness and all of that. If it's tedious to produce for a person, that maybe doesn't matter. But one thing, like if I asked you, is this rest program memory safe?[00:45:58] Bret: You wouldn't have to read it, you just have [00:46:00] to compile it. So that's interesting. I mean, that's like an, that's one example of a very modest form of formal verification. So I bring that up because I do think you have AI inspect AI, you can have AI reviewed. Do AI code reviews. It would disappoint me if the best we could get was AI reviewing Python and having scaled a few very large.[00:46:21] Bret: Websites that were written on Python. It's just like, you know, expensive and it's like every, trust me, every team who's written a big web service in Python has experimented with like Pi Pi and all these things just to make it slightly more efficient than it naturally is. You don't really have true multi threading anyway.[00:46:36] Bret: It's just like clearly that you do it just because it's convenient to write. And I just feel like we're, I don't want to say it's insane. I just mean. I do think we're at a local maximum. And I would hope that we create a programming system, a combination of programming languages, formal verification, testing, automated code reviews, where you can use AI to generate software in a high scale way and trust it.[00:46:59] Bret: And you're [00:47:00] not limited by your ability to read it necessarily. I don't know exactly what form that would take, but I feel like that would be a pretty cool world to live in.[00:47:08] Alessio: Yeah. We had Chris Lanner on the podcast. He's doing great work with modular. I mean, I love. LVM. Yeah. Basically merging rust in and Python.[00:47:15] Alessio: That's kind of the idea. Should be, but I'm curious is like, for them a big use case was like making it compatible with Python, same APIs so that Python developers could use it. Yeah. And so I, I wonder at what point, well, yeah.[00:47:26] Bret: At least my understanding is they're targeting the data science Yeah. Machine learning crowd, which is all written in Python, so still feels like a local maximum.[00:47:34] Bret: Yeah.[00:47:34] swyx: Yeah, exactly. I'll force you to make a prediction. You know, Python's roughly 30 years old. In 30 years from now, is Rust going to be bigger than Python?[00:47:42] Bret: I don't know this, but just, I don't even know this is a prediction. I just am sort of like saying stuff I hope is true. I would like to see an AI native programming language and programming system, and I use language because I'm not sure language is even the right thing, but I hope in 30 years, there's an AI native way we make [00:48:00] software that is wholly uncorrelated with the current set of programming languages.[00:48:04] Bret: or not uncorrelated, but I think most programming languages today were designed to be efficiently authored by people and some have different trade offs.[00:48:15] Evolution of Programming Languages[00:48:15] Bret: You know, you have Haskell and others that were designed for abstractions for parallelism and things like that. You have programming languages like Python, which are designed to be very easily written, sort of like Perl and Python lineage, which is why data scientists use it.[00:48:31] Bret: It's it can, it has a. Interactive mode, things like that. And I love, I'm a huge Python fan. So despite all my Python trash talk, a huge Python fan wrote at least two of my three companies were exclusively written in Python and then C came out of the birth of Unix and it wasn't the first, but certainly the most prominent first step after assembly language, right?[00:48:54] Bret: Where you had higher level abstractions rather than and going beyond go to, to like abstractions, [00:49:00] like the for loop and the while loop.[00:49:01] The Future of Software Engineering[00:49:01] Bret: So I just think that if the act of writing code is no longer a meaningful human exercise, maybe it will be, I don't know. I'm just saying it sort of feels like maybe it's one of those parts of history that just will sort of like go away, but there's still the role of this offer engineer, like the person actually building the system.[00:49:20] Bret: Right. And. What does a programming system for that form factor look like?[00:49:25] React and Front-End Development[00:49:25] Bret: And I, I just have a, I hope to be just like I mentioned, I remember I was at Facebook in the very early days when, when, what is now react was being created. And I remember when the, it was like released open source I had left by that time and I was just like, this is so f*****g cool.[00:49:42] Bret: Like, you know, to basically model your app independent of the data flowing through it, just made everything easier. And then now. You know, I can create, like there's a lot of the front end software gym play is like a little chaotic for me, to be honest with you. It is like, it's sort of like [00:50:00] abstraction soup right now for me, but like some of those core ideas felt really ergonomic.[00:50:04] Bret: I just wanna, I'm just looking forward to the day when someone comes up with a programming system that feels both really like an aha moment, but completely foreign to me at the same time. Because they created it with sort of like from first principles recognizing that like. Authoring code in an editor is maybe not like the primary like reason why a programming system exists anymore.[00:50:26] Bret: And I think that's like, that would be a very exciting day for me.[00:50:28] The Role of AI in Programming[00:50:28] swyx: Yeah, I would say like the various versions of this discussion have happened at the end of the day, you still need to precisely communicate what you want. As a manager of people, as someone who has done many, many legal contracts, you know how hard that is.[00:50:42] swyx: And then now we have to talk to machines doing that and AIs interpreting what we mean and reading our minds effectively. I don't know how to get across that barrier of translating human intent to instructions. And yes, it can be more declarative, but I don't know if it'll ever Crossover from being [00:51:00] a programming language to something more than that.[00:51:02] Bret: I agree with you. And I actually do think if you look at like a legal contract, you know, the imprecision of the English language, it's like a flaw in the system. How many[00:51:12] swyx: holes there are.[00:51:13] Bret: And I do think that when you're making a mission critical software system, I don't think it should be English language prompts.[00:51:19] Bret: I think that is silly because you want the precision of a a programming language. My point was less about that and more about if the actual act of authoring it, like if you.[00:51:32] Formal Verification in Software[00:51:32] Bret: I'll think of some embedded systems do use formal verification. I know it's very common in like security protocols now so that you can, because the importance of correctness is so great.[00:51:41] Bret: My intellectual exercise is like, why not do that for all software? I mean, probably that's silly just literally to do what we literally do for. These low level security protocols, but the only reason we don't is because it's hard and tedious and hard and tedious are no longer factors. So, like, if I could, I mean, [00:52:00] just think of, like, the silliest app on your phone right now, the idea that that app should be, like, formally verified for its correctness feels laughable right now because, like, God, why would you spend the time on it?[00:52:10] Bret: But if it's zero costs, like, yeah, I guess so. I mean, it never crashed. That's probably good. You know, why not? I just want to, like, set our bars really high. Like. We should make, software has been amazing. Like there's a Mark Andreessen blog post, software is eating the world. And you know, our whole life is, is mediated digitally.[00:52:26] Bret: And that's just increasing with AI. And now we'll have our personal agents talking to the agents on the CRO platform and it's agents all the way down, you know, our core infrastructure is running on these digital systems. We now have like, and we've had a shortage of software developers for my entire life.[00:52:45] Bret: And as a consequence, you know if you look, remember like health care, got healthcare. gov that fiasco security vulnerabilities leading to state actors getting access to critical infrastructure. I'm like. We now have like created this like amazing system that can [00:53:00] like, we can fix this, you know, and I, I just want to, I'm both excited about the productivity gains in the economy, but I just think as software engineers, we should be bolder.[00:53:08] Bret: Like we should have aspirations to fix these systems so that like in general, as you said, as precise as we want to be in the specification of the system. We can make it work correctly now, and I'm being a little bit hand wavy, and I think we need some systems. I think that's where we should set the bar, especially when so much of our life depends on this critical digital infrastructure.[00:53:28] Bret: So I'm I'm just like super optimistic about it. But actually, let's go to w

Most early-stage founders I talk to are focused on getting their first customers, hiring their first employees, or maybe, if they're lucky, closing their first round of funding. But what happens after that? For Rohit Choudhary, the answer was building a whole new category. Rohit is the CEO and co-founder of Acceldata, a data observability platform that helps companies manage the complexity of modern data infrastructure. Before starting the company, he spent years inside the problem — working on data engineering challenges at Hortonworks and other enterprise tech firms. Like a lot of technical founders, Rohit didn't start out dreaming of being a CEO — but the problem was too big to ignore. In this episode, we talk about: Why data engineering lacked the right tooling and how that led to Acceldata How his team validated the concept with real-world customer pain points The trade-offs of building in stealth mode vs. in public What he's learned about hiring, scaling, and making the leap from engineer to CEO If you're trying to figure out how to go from technical insight to scalable business, this one's for you. RUNTIME 37:37 EPISODE BREAKDOWN (2:16) “ There are four of us co-founders, and we were all part of the same engineering team at Hortonworks.” (4:33) “ We felt that here was a unique opportunity for us to be able to build something really, really large and big.” (6:16) How Acceldata approached proof-of-concept programs in its early days. (8:23) “ How did you decide which one of you would become the CEO?” (11:31) Rohit's seed-stage recruiting strategy: “ we had to excite them with the long-term vision.” (14:35) “ People like me, we learned how to sell despite coming from an engineering background.” (16:46) Why the co-founders “took a leap of faith” by formalizing their sales process early. (18:46) “ We were familiar with how business is conducted in the U.S.,” which made expansion easier. (21:08) Early challenges they faced after closing a Series A. (23:08) How “a big mistake” from a previous startup still influences Rohit's choices today. (25:30) Wondering if it's time to throw in the towel? Do a self-assessment. (28:31) Three core skills engineers need to acquire if they want to become effective CEOs. (31:39) “ I used to interview almost everyone until we were at about, you know, 170-180.” (33:82) How creating a 10-year strategy informed their day-to-day decision making. (36:27) The one question he'd have to ask the CEO in an interview before he could accept an offer. LINKS Acceldata Rohit Choudhary, co-founder/CEO Ashwin Rajeeva, co-founder/CTO Gaurav Nagar, co-founder/Senior Architect Raghu Mitra Kandikonda, co-founder/Director of Engineering Lightspeed Venture Partners Acceldata Announces $50 Million in Series C Funding to Expand Market Leadership and Product Innovation in Data Observability (press release) SUBSCRIBE LinkedIn Substack Instagram Thanks for listening! – Walter.

In this engaging conversation at the All Things Open conference, Tim Spann, Principal Developer Advocate at Zilliz, discusses the importance of community collaboration in advancing AI technologies. He emphasizes the need for diverse perspectives in solving complex problems and highlights his work with the Milvus open source vector database. Tim also explains the evolving landscape of retrieval augmented generation (RAG) and its applications and shares insights into the future of AI development. The conversation concludes on a lighter note with Tim describing his creative use of Milvus in a fun Halloween project to catalog and identify ghosts. 00:00 Introduction 00:41 Meet Tim Spann: Principal Developer Advocate 01:35 The Importance of Community in AI 02:56 Advanced RAG and Multimodal Models 06:17 The Future of Agentic RAG 09:04 Challenges and Excitement in AI Development 13:35 Building AI the Right Way 17:50 Fun with AI: Capturing Ghosts 19:24 Conclusion and Final Thoughts   Guest: Tim Spann is a Principal Developer Advocate for Zilliz and Milvus. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Principal Developer Advocate at Cloudera, Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.

Ari Zilka is CEO of mydecisive.ai, the general-purpose observability engine built on OpenTelemetry. Ari was previously the CTO of Hortonworks which built products on top of open source Apache Hadoop and merged with Cloudera in 2019. In this episode, we dig into the similar patterns Ari sees between Hortonworks / Hadoop and mydecisive.ai / OpenTelemetry, why large enterprises don't want their data to be held hostage and are shifting towards OpenTelemetry, how open source switches costs from vendors to engineering, why he's focused on building the community before monetizing, his view on monetization ("the developer bakes you in, the operator pays for it"), why the "ops" side of "devops" carries the money, and the learnings from building Hortonworks that he's bringing into mydecisive.ai.

We're featuring another popular session from ELC Annual 2023 – welcome to “Healthy Tension: GTM & Product/Eng Collaboration at Hundreds of Millions ARR Scale” with Tido Carriero, (Co-Founder @ Koala and Former VPE @ Segment) and Joe Morrissey (General Partner @ a16z & Former CRO @ Segment)! Tido & Joe share stories from the beginning of their partnership at Segment, including their first cross-functional annual planning meeting. They highlight lessons learned from those early days and how others can implement annual planning session frameworks to develop value drivers for their org in order to better serve customers & create products with value. Joe & Tido also cover how to build a healthy, trusting relationship between product & eng when it comes to building / executing a successful GTM strategy.ABOUT TIDO CARREIROTido is the Co-Founder & CEO of Koala. Prior to Koala, he led the Product & Engineering team at Segment from less than $5M in ARR to their $3.2B acquisition by Twilio.“I had been at Segment for four years. The big unlock for me and I think what I needed to lean into more in retrospect from a trust perspective was that Joe was really going to be a different kind of go to market partner. We had zoomed way out. We had looked at a multi-year strategy, not just a list of 25 features and ordering them quarter by quarter by quarter.”- Tido Carriero   ABOUT JOE MORRISSEYJoe Morrissey is a general partner on the Growth investing team at Andreessen Horowitz, focused on enterprise technology companies. Prior to joining a16z, Joe was chief revenue officer at Segment, where he scaled revenues to upwards of $200M ARR in advance of the company's $3.2B acquisition by Twilio. Before Segment, he was was the EMEA vice president and general manager for three open source software companies: Hortonworks, which combined with Cloudera in a $5.2B merger in 2019; MongoDB, which went public in 2017; and MySQL, which was acquired by Sun Microsystems for $1B in 2008. Joe holds a bachelor's degree in business studies from the University of Limerick, Ireland. He currently serves on the boards of Neon Inc., and Hopsworks AB and lives in Menlo Park with his wife and two kids."You've got to go through this tension and I think one of the things that can happen is you avoid the tension, you avoid the conflict, you say yes to things that maybe you're not comfortable with both on the product and on the go to market side then the plan goes wrong, right? So I really think like the tension is the critical thing and that the struggle is the critical thing and that's where the learning is.”- Joe Morrissey   We now have 10 local communities of engineering leaders hosting in-person meetups all over the world!Local communities are led by eng leaders just like you, who wanted to create a place to connect, share insights & tackle critical challenges in the job.New York City, Boston, Chicago, Seattle, Los Angeles, San Diego, San Francisco, London, Amsterdam, and Toronto in-person events are happening now!We're launching local events all the time - get involved at elc.community!SHOW NOTES:Joe's first impressions of Tido & the beginning of their relationship (2:28)The story of their low point & working together on annual planning (5:32)What was agreed on in the annual planning session (7:44)Focusing on value drivers & building a trusting GTM partnership (10:55)Why it's necessary to embrace tension in order to drive growth (15:01)Tido's lessons learned leading eng product & sales @ Koala (16:05)Audience Q&A: Frameworks for narrowing down value drivers (19:00)The importance of cross-functional participation in planning sessions (22:21)An inside look at the exercise of identifying value drivers (24:02)How deep should salespeople go on the product? (26:27)How does annual planning change day-to-day operations for the year? (27:56)Describing the Lighthouse program (30:10)Reorganizing the org to meet the three identified value drivers (32:32)Engineering leadership's involvement during the annual plan (35:24)Strategy behind building a platform (38:38)LINKS AND RESOURCESVideo version of this episodeMore sessions from ELC AnnualThis episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/

SHOWNOTESGet ready for the latest episode of Masters of MEDDICC! Host Andy Whyte chats with experienced revenue leader, Jeff Miller, current CRO of StarTree. Listen and have a chuckle as the two talk about their shared history of selling door-to-door - and what they learned from it.The way you communicate with your customer can make or break a deal or engagement. There are a lot of opinions out there about the best way to do it - product-led or open-source? Either way, Andy and Jeff look at how MEDDIC can underpin it all, and help you explain the value of your solution to your customers.Andy and Jeff discuss how their ADHD acts as a superpower in sales. Jeff, as always, ties it back to the customer, comparing how he explained how his ADHD makes his brain work differently to understanding that every customer will think differently to you. ABOUT JEFF:Jeff Miller is the Chief Revenue Officer at StarTree, leading the company's go-to-market strategy across its Sales, Revenue Operations, Solutions Engineering, Customer Success and Product-Led Growth teams. Jeff brings more than two decades of experience leading, managing and growing successful sales teams and exponentially increasing revenue for high-growth tech companies. Prior to joining StarTree, Jeff served as the CRO of Cockroach Labs, where he helped lead the company to its Series F stage and a $5billion valuation with over 250 customers. He previously served as Senior Vice President of Sales at Hortonworks, leading the organization to become the fastest-growing software company ever to get $100million in ARR and a successful IPO. 

AI is a huge opportunity for businesses. How can organizations seize this opportunity? Well by understanding how AI works, its opportunities and drawbacks, responsible AI and data security. This is exactly what our guest Balaji Ganesan, Co-Founder and CEO of Privacera and our host Punit Bhatia, CEO of FIT4Privacy are talking about in this episode. Take a listen now.  KEY CONVERSATION POINTS  AI in one word  How can businesses combine data governance and AI?  How can companies start AI programs   Responsible AI framework and policies  Data governance and data security  Closing    ABOUT THE GUEST  Balaji Ganesan is CEO and co-founder of Privacera. Before Privacera, Balaji and Privacera co-founder Don Bosco Durai, also founded XA Secure. XA Secure's was acquired by Hortonworks, who contributed the product to the Apache Software Foundation and rebranded as Apache Ranger. Apache Ranger is now deployed in thousands of companies around the world, managing petabytes of data in Hadoop environments. Privacera's product is built on the foundation of Apache Ranger and provides a single pane of glass for securing sensitive data across on-prem and multiple cloud services such as AWS, Azure, Databricks, GCP, Snowflake, and Starburst and more.  ABOUT THE HOST  Punit Bhatia is one of the leading privacy experts who works independently and has worked with professionals in over 30 countries. Punit works with business and privacy leaders to create an organization culture with high AI & privacy awareness and compliance as a business priority by creating and implementing a AI & privacy strategy and policy.   Punit is the author of books “Be Ready for GDPR” which was rated as the best GDPR Book, “AI & Privacy – How to Find Balance”, “Intro To GDPR”, and “Be an Effective DPO”. Punit is a global speaker who has spoken at over 50 global events. Punit is the creator and host of the FIT4PRIVACY Podcast. This podcast has been featured amongst top GDPR and privacy podcasts.  As a person, Punit is an avid thinker and believes in thinking, believing, and acting in line with one's value to have joy in life. He has developed the philosophy named ‘ABC for joy of life' which passionately shares. Punit is based out of Belgium, the heart of Europe.     RESOURCES  Websites www.fit4privacy.com , www.punitbhatia.com,  www.privacera.com   Podcast https://www.fit4privacy.com/podcast Blog https://www.fit4privacy.com/blog YouTube http://youtube.com/fit4privacy  --- Send in a voice message: https://podcasters.spotify.com/pod/show/fit4privacy/message

Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It's the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it's real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Elad Eldor about operating Kafka in production and how to keep your clusters stable and performant Interview Introduction How did you get involved in the area of data management? Can you describe your experiences with Kafka? What are the operational challenges that you have had to overcome while working with Kafka? What motivated to write a book about how to manage Kafka in production? There are many options now for persistent data queues. What are the factors to consider when determining whether Kafka is the right choice? In the case where Kafka is the appropriate tool, there are many ways to run it now. What are the considerations that teams need to work through when determining whether/where/how to operate a cluster? When provisioning a Kafka cluster, what are the requirements that need to be considered when determining the sizing? What are the axes along which size/scale need to be determined? The core promise of Kafka is that it is a durable store for continuous data. What are the mechanisms that are available for preventing data loss? Under what circumstances can data be lost? What are the different failure conditions that cluster operators need to be aware of? What are the monitoring strategies that are most helpful for identifying (proactively or reactively) those errors? In the event of these different cluster errors, what are the strategies for mitigating and recovering from those failures? When a cluster's usage expands beyond the original designed capacity, what are the options/procedures for expanding that capacity? When a cluster is underutilized, how can it be scaled down to reduce cost? What are the most interesting, innovative, or unexpected ways that you have seen Kafka used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kafka? When is Kafka the wrong choice? What are the changes that you would like to see in Kafka to make it easier to operate? Contact Info LinkedIn (https://www.linkedin.com/in/elad-eldor/?originalSubdomain=il) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Kafka: Troubleshooting in Production (https://amzn.to/3NFzPgL) book (affiliate link) IronSource (https://www.is.com/) Druid (https://druid.apache.org/) Trino (https://trino.io/) Kafka (https://kafka.apache.org/) Spark (https://spark.apache.org/) SRE == Site Reliability Engineer (https://en.wikipedia.org/wiki/Site_reliability_engineering) Presto (https://prestodb.io/) System Performance (https://amzn.to/3tkQAag) by Brendan Gregg (affiliate link) HortonWorks (https://en.wikipedia.org/wiki/Hortonworks) RAID == Redundant Array of Inexpensive Disks (https://en.wikipedia.org/wiki/RAID) JBOD == Just a Bunch Of Disks (https://en.wikipedia.org/wiki/Non-RAID_drive_architectures#JBOD) AWS MSK (https://aws.amazon.com/msk/) Confluent (https://www.confluent.io/) Aiven (https://aiven.io/) JStat (https://docs.oracle.com/javase/8/docs/technotes/tools/windows/jstat.html) Kafka Tiered Storage (https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage) Brendan Gregg iostat utilization explanation (https://www.brendangregg.com/blog/2021-05-09/poor-disk-performance.html) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

In this episode we welcome serial investor, CEO and board member Tom Reilly who shares his insight into business growth, leadership and the role of a CEO. KEY TAKEAWAYS FROM THIS EPISODE What is the role of a CEO? The Three Essential Ingredients to Scaling a Business How to lead leaders The Story of a Ten Figure Exit THIS WEEK'S GUEST Tom Reilly has a thirty year career forming, leading, scaling and advising high-growth enterprise software and cybersecurity vendors. After an early career with IBM and running sales in the 90s for Lotus and BroadQuest, he became CEO of Trigeo, which was sold to IBM in 2004, and then became President and CEO of ArcSight, which he scaled globally, took through an IPO and exited to HP for over $1.5Bn and then CEO of Cloudera which he IPO'd with a $3Bn valuation and a $5.2Bn merger with HortonWorks. He has served on the boards of companies such as ELoqua, Jive Software, Trusona, Incorta, Datastax and Anomali, and served as the Chair of the Economic Development and Advisory Committee for the City of Sausalito. Tom is proud to support the work of Cybermindz.org, for more information about their incredible work in mental health within the cybersecurity community, please visit https://cybermindz.org/ YOUR HOST Simon Lader is the host of The Conference Room, Co-Founder of global executive search firm Salisi Human Capital, and podcast growth consultancy Viva Podcasts. Since 1997, Simon has helped cybersecurity vendors to build highly effective teams, and since 2022 he has helped people make money from podcasting. Get to know more about Simon at: Website: https://simonlader.com/ Make Money from Podcasting: https://www.vivapodcasts.com/podcastpowerups Twitter: https://twitter.com/simonlader LinkedIn: https://www.linkedin.com/in/headhuntersimonlader The Conference Room is available on Spotify, Apple Podcasts, Amazon Music iHeartRadio And everywhere else you listen to podcasts!

In this episode, we speak to Piet Loubser, the SVP of Marketing for Privacera about privacy and data governance issues for CMOs.Piet Loubser serves as Global Senior Vice President of Marketing at Privacera, Inc where he is responsible for leading all aspects of marketing. His professional career includes more than 30 years in the Hi-Tech industry driving product, sales and marketing strategies in numerous companies transforming product focused GTM into customer solutions centric GTM to accelerate growth. Prior to Privacera, Piet held executive leadership positions in marketing and sales at market leading companies including SymphonyAI, Paxata, Hortonworks, Informatica, SAP and Business Objects. As part of the executive leadership teams at these global technology companies, he has built deep expertise across the entire Product to GTM value chain to drive growth strategies at all levels of organizational size. Piet holds a B.S. degree in computer science and applied mathematics from the University of Stellenbosch in South Africa.Learn more about Piet LoubserLearn more about PrivaceraFollow Peter Mahoney on Twitter and LinkedInLearn more about PlannuhJoin The Next CMO CommunityRecommend a guest for The Next CMO podcastProduced by PodForte

https://fellow.app/supermanagers/dave-mcjannet-hashicorp-from-zero-to-scale-how-to-pick-a-market-find-the-people-and-build-the-systems/ Companies move through different growth phases and require unique strategies along the way. Dave first joined HashiCorp when there were just 30 employees. Today, there are over 2,500 people globally! In episode #130, Dave McJannet shares his top insights and lessons learned from taking a company from zero to scale. Dave McJannet is the CEO of HashiCorp, and has over 20 years of experience in product management, operations, finance, and marketing. Prior to HashiCorp, Dave worked at GitHub, Hortonworks, Microsoft and SpringSource. Dave explains how he builds high-performing teams, his time horizons for executives, and how he tests for systems thinking in the hiring process. Tune in to hear all about Dave's leadership journey and the lessons learned along the way! . . . Like this episode? Be sure to leave a ⭐️⭐️⭐️⭐️⭐️ review and share the podcast with your colleagues.

Redis, best known as a data cache or real-time data platform, is evolving into much more, Tim Hall, chief of product at the company told The New Stack in a recent TNS Makers podcast. Redis is an in-memory database or memory-first database, which means the data lands there and people are using us for both caching and persistence. However, these days, the company has a number of flexible data models, but one of the brand promises of Redis is developers can store the data as they're working with it. So as opposed to a SQL database where you might have to turn your data structures into columns and tables, you can actually store the data structures that you're working with directly into Redis, Hall said.  Primary Database? “About 40% of our customers today are using us as a primary database technology,” he said. “That may surprise some people if you're sort of a classic Redis user and you knew us from in-memory caching, you probably didn't realize we added a variety of mechanisms for persistence over the years.” Meanwhile, to store the data, Redis does store it on disk, sort of behind the scenes while keeping a copy in memory. So if there's any sort of failure, Redis can recover the data off of disk and replay it into memory and get you back up and running. That's a mechanism that has been around about half a decade now. Yet, Redis is playing what Hall called the ‘long game', particularly in terms of continuing to reach out to developers and showing them what the latest capabilities are. “If you look at the top 10 databases on the planet, they've all moved into the multimodal category. And Redis is no different from that perspective” Hall said. “So if you look at Oracle it was traditionally a relational database, Mongo is traditionally JSON documents store only, and obviously Redis is a key-value store. We've all moved down the field now. Now, why would we do that? We're all looking to simplify the developer's world, right?” Yet, each vendor is really trying to leverage their core differentiation and expand out from there. And the good news for Redis is speed is its core differentiation. “Why would you want a slow data platform? You don't, Hall said. “So the more that we can offer those extended capabilities for working with things like JSON, or we just launched a data structure called t-digest, that people can use along and we've had support for Bloom filter, which is a probabilistic data structure like all of these things, we kind of expand our footprint, we're saying if you need speed, and reducing latency, and having high interactivity is your goal Redis should be your starting point. If you want some esoteric edge case functionality where you need to manipulate JSON in some very strange way, you probably should go with Mongo. I probably won't support that for a long time. But if you're just working with the basic data structures, you need to be able to query, you need to be able to update your JSON document. Those straightforward use cases we support very, very well, and we support them at speed and scale.” Customer View As a Redis customer, Alain Russell, CEO at Blackpepper, a digital e-commerce agency in Auckland, New Zealand, said his firm has undergone the same transition. “We started off as a Redis as a cache, that helped us speed up traditional data that was slower than we wanted it,” he said. “And then we went down a cloud path a couple of years ago. Part of that migration included us becoming, you know, what's deemed as ‘cloud native.' And we started using all of these different data stores and data structures and dealing with all of them is actually complicated. You know, and from a developer perspective, it can be a bit painful.” So, Blackpepper started looking for how to make things simpler, but also keep their platform very fast and they looked at the Redis Stack. “And honestly, it filled all of our needs in one platform. And we're kind of in this path at the moment, we were using the basics of it. And we're very early on in our journey, right? We're still learning how things work and how to use it properly. But we also have a big list of things that we're using other data stores for traditional data, and working out, okay, this will be something that we will migrate to, you know, because we use persistent heavily now, in Redis.” Twenty-year-old Blackpepper works with predominantly traditional retailers and helps them in their omni-channel journey. Commercial vs. Open Source Hall said there are three modes of access to the Redis technology: the Redis open source project, the Redis Stack – which the company recommends that developers start with today -- and then there's Redis Enterprise Edition, which is available as software or in the cloud. “It's the most popular NoSQL database on the planet six years running,” Hall said. “And people love it because of its simplicity.” Meanwhile, it takes effort to maintain both the commercial product and the open source effort. Allen, who has worked at Hortonworks, InfluxData, said “Not every open source company is the same in terms of how you make decisions about what lands in your commercial offering and what lands in open source and where the contributions come from and who's involved.” For instance, “if there was something that somebody wanted to contribute that was going to go against our commercial interest, we probably not would not merge that,” Hall said. Redis was run by project founder Salvatore Sanfilippo, for many, many years, and he was the sole arbiter of what landed and what did not land in Redis itself. Then, over the last couple of years, Redis created a core steering committee. It's made up of one individual from AWS, one individual from Alibaba, and three Redis employees who look after the contributions that are coming in from the Redis open source community members who want to contribute those things. “And then we reconcile what we want from a commercial interest perspective, either upstream, or things that, frankly, may have been commoditized and that we want to push downstream into the open source offering, Hall said. “And so the thing that you're asking about is sort of my core existential challenge all the time, that is figuring out where we're going from a commercial perspective. What do we want to land there first? And how can we create a conveyor belt of commercial opportunity that keeps us in business as a software company, creating differentiation against potential competitors show up? And then over time, making sure that those things that do become commoditized, or maybe are not as differentiating anymore, I want to release those to the open source community. But this upstream/downstream kind of challenge is something that we're constantly working through.” Blackpepper was an open source Redis user initially, but they started a journey where they used Memcached to speed up data. Then they migrated to Redis when they moved to the AWS cloud, Russell said. Listen to the Podcast The Redis TNS Makers podcast goes on to look at the use of AI/ML in the platform, the acquisition of RESP.app, the importance of JSON and RediSearch, and where Redis is headed in the future.

Welcome to another engaging and informative episode of Data Gurus! Sima is delighted to have Raj Bains, the CEO and Founder at Prophecy, joining her for today's show! Raj started Prophecy five years ago and decided to build one big product to get data ready for analytics. He talks to Sima about his business and product and where he fits within the industry. Raj's background Raj started his professional career in the early 2000s. He started with graphics, worked as a developer, got into power tools, and worked at Microsoft. After that, he joined a team at NVIDIA to build CUDA, which now gets used for Bitcoin mining. Then he moved into the data space and shifted from engineering into marketing and product management. While selling big data platforms and managing Apache Hive for the Hadoop Company at Hortonworks, he saw data users struggling to be productive with outdated tools. He solved that problem by building power tools to make it easy to get data ready for analytics very quickly. He has focused on doing that since then. Prophecy Raj started Prophecy in 2017 to build a visual tooling layer to get data ready for analytics. Data scientists, data analysts, and data engineers can use the tool to avoid having to do unnecessary work. Standardization There is a big problem with standardization within large organizations when it comes to building data pipelines. The problem with unstandardized codes  In the past, people within the industry used to write scripts. Then they started using standardized visual tools. After that, they moved to the cloud, but nobody wanted to get locked into that tool, so they got rid of the visual development tools and started using codes. The codes within companies are unstandardized, however, so everyone's code looks different. That has led to many different problems. A solution Prophecy's clients do visual drag-and-drop development, and Raj and his team write high-quality code for them. That has opened people up to returning to visual development and allowed much more standardization within the industry. Building a solution Raj and his team started working with a few big credit card companies and banks. Then they spent two or three years building their product. They still have a year or two of building ahead of them, but they have reached a point where they can solve the entire problem for companies using their product. An enterprise standard They have created an enterprise standard for all lines of business, where data analysts, data engineers, and everyone else within a company use the same tool and speak the same language. Data pipelines Prophecy makes it quick and easy for companies to build data pipelines. Data pipelines are essential for analytics because they provide the necessary information for asking intelligent questions. The data has to be high-quality, timely, and in the right shape to answer questions quickly. The future Raj believes that they will solve the issues with data management within the next three to five years. Bio:   Raj is the founder & CEO of Prophecy. Previously, Raj led project management of Apache Hive at Hortonworks through their IPO. He also headed product management and marketing for a NewSQL database startup. Raj continues to actively develop compiler and database technologies in his quest to create data tools “that don't suck.” His engineering roles include developing a NewSQL database, building CUDA at NVIDIA as a founding engineer, and as a compiler engineer working on Microsoft Visual Studio.   Links: Email me your thoughts! Sima@Infinity-2.com LinkedIn Twitter Infinity-2.com   Connect with Raj Raj Bains on LinkedIn Raj Bains on Twitter

Watch this episode on YouTube: https://youtu.be/GZVugQeJ_XY This Week's Guest is Data Tools Guru, Raj Bains Raj is the founder & CEO of Prophecy. Previously, Raj led project management of Apache Hive at Hortonworks through their IPO. He also headed product management and marketing for a NewSQL database startup. Raj continues to actively develop compiler and database […]

Hello and welcome back to another episode of Her HypeSquad with Bosstrack In this episode I'm talking with Melissa Park, an award-winning global event producer and founder of Melissa Park Events and the Mel-Factor Method. Melissa and I talk about building leadership credibility, using your network for success, the importance of asking questions and the time she met Sir Richard Branson. I hope you find as much value in listening to this episode as I did recording it! About Melissa Park Melissa “Mel” Park is an award-winning Global Event Producer who has utilized her engaging personality, unending energy, and attention to logistical and design details to build a business that is recognized across the United States and Australia. After producing high profile, business and consumer events Melissa broke out on her own and launched her first event management company in Australia at the age of 26. She produced events like HSBC Bank's Chinese New Year Gala Dinner Tour and more than 20 outdoor festivals attracting 20,000 - 100,000 attendees. She also served as the stage manager for notable events like the Sydney Olympic Games 10 Year Anniversary Ceremony, Sydney International FIFA Fan Fest in 2010, and the Major League Baseball Opening Series in 2014. Melissa moved to the US in 2014 where she began working internally for two years at Hortonworks and two years later stepped back out on her own with Melissa Park Events specializing in elevating brands, amplifying corporate messages and transforming struggling events into extraordinary must-attend experiences. She's made a name for herself in the technology space for her seamlessly executed large-scale user conferences, strategic sponsorships, and innovative brand activations. Her client list has grown from one continent to another based on referrals, recognition from attendees onsite, and clients who return year after year for her to produce their events. She currently splits her time between New York and Sydney when she is not traveling to produce her nearly 30 annual events. Her events have been featured on many TV and radio programs in Australia. She is the creator of the The Mel-Factor Method masterclass, a contributing writer to numerous publications and a sought-after keynote speaker and panelist. I'm so excited to bring you my conversation with Melissa Park. You can reach Mel at Instagram: @melissaparkevents Website: www.melissapark.co Email: melissa@melissapark.co LinkedIn: melissapark

Today on That Tech Pod, Laura and Gabi talk with Raj Bains. Raj is the founder & CEO of Prophecy. Previously, Raj led project management of Apache Hive at Hortonworks through their IPO. He also headed product management and marketing for a NewSQL database startup. Raj continues to actively develop compiler and database technologies in his quest to create data tools “that don't suck.” His engineering roles include developing a NewSQL database, building CUDA at NVIDIA as a founding engineer, and as a compiler engineer working on Microsoft Visual Studio.

Frank Mong is COO of Nova Labs (formerly Helium Inc.), where he is responsible for sales, marketing and business development for the company. Before Helium, Mong spent 20 years in cyber security including CMO at Hortonworks, SVP of Marketing at Palo Alto Networks, and VP/GM of security at HP.

Raj Verma is the CEO at SingleStore, a database management system that helps modern applications be fast, frictionless, and flexible. When Raj started at SingleStore, he was Co-CEO with SingleStore's founder, Nikita Shamgunov. By building a healthy relationship and friendship with Nikita, Raj was able to lead with the company's founding principles, keeping that essence that makes SingleStore so special. Raj's transition to Co-CEO and later to CEO has as much to do with his mindset and leadership skills as it does with the CEO job description. In this episode, Catherine and Raj talk about Raj's transition from Co-CEO to CEO, why Raj views CEO as a mindset, how Raj leads with “a warrior's mentality and a servant's heart,” the importance of adaptation and change, how to build company culture, and more.Topics Include: - Relationship between founder and CEO - How his partnership with Nikita started - The accountability that comes with being a CEO- Executive coaching- Conscious capitalism - Combining business strategy with culture- The importance of finding you're why- What drives Raj toward success- Adapting to new circumstances - And other topics…Raj Verma is the Chief Executive Officer at SingleStore. He has more than 25 years of experience in enterprise software and scaling operations. Before SingleStore, Raj was the CEO, EVP of Global Scales, and COO of TIBCO Software, a company that he helped grow to over $1 billion in revenue. He was also COO at Aptus Software and Hortonworks. Raj holds a BA in computer science from BMS College of Engineering in Bangalore, India. Resources Mentioned: Man's Search For Meaning by Viktor E. Frankl: https://www.amazon.com/gp/product/0807060100/ Bad Blood by John Carreyrou: https://www.amazon.com/Bad-Blood-Secrets-Silicon-Startup/dp/152473165X Shantaram by Gregory David Roberts: https://www.amazon.com/Shantaram-Novel-Gregory-David-Roberts/dp/0312330537

About ChrisChris Harris is Vice President, Global Field Engineering at Couchbase, a provider of a leading modern database for enterprise applications that 30% of the Fortune 100 depend on. With almost 20 years of technical field and professional services experience at early-stage, open source and growth technology companies, Chris held leadership roles at Cloudera, Hortonworks, MongoDB and others before joining Couchbase.Links Referenced: couchbase.com: https://couchbase.com LinkedIn: https://www.linkedin.com/in/chris-harris-5451953/ Twitter: https://twitter.com/cj_harris5 TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Couchbase Capella Database-as-a-Service is flexible, full-featured and fully managed with built in access via key-value, SQL, and full-text search. Flexible JSON documents aligned to your applications and workloads. Build faster with blazing fast in-memory performance and automated replication and scaling while reducing cost. Capella has the best price performance of any fully managed document database. Visit couchbase.com/screaminginthecloud to try Capella today for free and be up and running in three minutes with no credit card required. Couchbase Capella: make your data sing.Corey: This episode is sponsored in part by LaunchDarkly. Take a look at what it takes to get your code into production. I'm going to just guess that it's awful because it's always awful. No one loves their deployment process. What if launching new features didn't require you to do a full-on code and possibly infrastructure deploy? What if you could test on a small subset of users and then roll it back immediately if results aren't what you expect? LaunchDarkly does exactly this. To learn more, visit launchdarkly.com and tell them Corey sent you, and watch for the wince.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. One of the stranger parts of running this show is when I have a promoted guest episode like this one, where someone comes on, and great, “Oh, where do you work?” And the answer is a database company. Well, great, unless it's Route 53, it's clearly not the best database in the world, but let's talk about how you're making a strong showing for number two.It sounds like it's this whole ridiculous, negging nonsense or whatever the kids are calling it these days, but that's not how it's intended. Today's promoted guest is Chris Harris, who's the Vice President of Global Field Engineering at Couchbase. Chris, thank you for joining me and I really hope I got it right and that Couchbase is a database company or that makes no sense whatsoever.Chris: It's great to be on the show, and thank you for the invitation. I'm looking forward to it. Yeah, we're a database company. That's exactly what we do.Corey: I always find it interesting when companies start pivoting from a thing that they were and, “What do you do?” “We build databases.” [unintelligible 00:01:29] getting out of that space it's, “What do you do?” “We're a finance company.” And then there's a period of time in which they start reframing what they do. It's, “We're a data platform.” Or, “We're now a tech company.”Really? Because I don't get that sense in any meaningful perspective. Couchbase was founded as a database company. You went public last year—congratulations on that—and now you continue to say, “Yes, we're a database company,” rather than an everything trying to eat the world all at the same time, mostly ineffectively, company. So, what kind of database are you folks?Chris: So, if you look at the database world, you can see—I've been in the space for quite some time now, a good few years, and I've had the privilege, if you like, of being at other database companies, been in the analytics space, and I'm here at Couchbase. But if you look at the history over the last—let's just not go back all the way that far, but let's go back to, like, ten years ago, everybody was building their applications on traditional relational databases. And what you saw is that the Oracle and MySQL, as traditional databases of the world. And then… probably at the time, we realized that with, talking ten years ago where we had this demand for high throughput of data, next generation of applications will be built, and then people realized the traditional database architectures weren't going to cut it, if you like. And it spawned this industry.You know, a big NoSQL market was created. And you have document databases, and then you have graph databases, and then you have analytics databases, and you have search databases, and then you have every sort of database you could possibly think of type database that's out there in the world.Corey: You have so many kinds you need to keep track of it all inside of the database.Chris: That's what you have to do, right? [laugh]. But the interesting thing is it became different types of database. And even see this in many of the code providers today, right, that you have multiple different types of databases no matter what you're trying to do, right? So, we kind of went—Couchbase kind of took a step back and went, okay, we were originally a cache, right, this is where we came from, and then kind of built that into a document database, and then kind of went to the market and went hold on here, rather than it being let's call it a noSQL versus SQL discussion, why can't it just be a database, right?Why can't you have a SQLite interface on top of a modern architecture? Why can you do that, right? Why can't you have the flexibility and architectural [unintelligible 00:04:16] of a JSON-based database with the interface of—with SQL, and then analytics built on top of that, right? So, why can't you have the power of SQL on the next generation architecture? So, that's kind of where we fit in the world.Corey: When we talk about origin stories and where things come from, well, let's start with you. I guess the impolite version of the question is, “Why on earth would you be in a space like this for so long?” But you've been on a lot of interesting places doing somewhat similar things. You were at Cloudera, you were at Hortonworks until you apparently heard a who or whatnot, you were at MongoDB, you were at VMware, you were at Red Hat. And that's going reverse chronologically, but it's clear that you're very focused on a particular expression of a particular problem. Why are you the way that you are? Only pretend that's a polite question.Chris: “Why am I the way that I am?” Well, first of all, I love technology, right? That's the key. And I think many of us in the industry would definitely say that, right? I started off in core engineering, building—I know some people today wouldn't probably remember this, but when you had Chip and Pin where your credit card and you have to type it in and put in a pin number, that was created originally in the UK, and then went off and built e-commerce websites for retailers.Well, that then turned into—was a common theme that I kept seeing is that lot of the technology that we're using was open-source technology. And that kind of got me into the open-source movement, if you like, and I was lucky enough to then join Red Hat when they built middleware frameworks, so got into that space there. And then did a lot of innovation in the middleware space. Went to SpringSource and we did some great work there in the Java Development Framework space. But what became interesting is that—you still see it today—like, in this innovation happening in that middleware space and there's some great innovation happening, right?There's all this stuff with Lambda and serverless architecture that's out there, but they always came back to, we've got the database, this thing that is in the architecture if it goes down, you're stuffed, right? This is where the core value of your company is sitting. So, then that got me interested to see what innovation was happening in this space. And as I say, I got into this field in the early stages of NoSQL, where there was that spawn of new database technologies being created. And then from there, it was like, “Okay, let's get into what was happening in the analytic space.”Again, I'm still in the Hortonworks, and Cloudera space, that's all open-source. But it came down to this is different types of databases that were required different types of skills. And then I started talking to the team here, who was like, “How can we take as great innovation and leverage the skills I already have?” And I thought that was an interesting point.Corey: In the interest of full disclosure, I tend to take the exact inverse approach to the way that you did. When I was going through the worlds of systems administrator, than rebadged as DevOps, or SRE, or systems engineer, production engineer, whatever we're calling ourselves this week, I was always focused primarily on stateless things like web servers, or whatnot because it turns out that—this should be no surprise to longtime listeners of this show—but I'm really bad with computers. And most other things, too; I just brute force my way through it. And that's hilarious when you keep taking down web servers you can push a button and recreate. When you do that to a database or anything that's stateful, it leaves a mark.And if you do it the wrong way, just well enough, you might not have a company anymore, so your DR plan starts to look a lot more like updating your resume. So, I always tried to shy away from things that played to my specific weaknesses that would, you know, follow me around like a stink. You, on the other hand, apparently sound—how to frame it—you know, good at things, and in a way that I never was. So you're—ah, you see a problem, you're running towards it trying to help fix it; I'm trying to how do I keep myself away from making the problem worse is my first approach. It seems like you have definitely been focused on not just data themselves—I mean at some level, [if it was a 00:08:55] pure data problem, it feels like we'd be talking a lot more about storage, but rather how to wind up organizing that data, how to wind up presenting that data, and the relationship that data has to other things that are going on. I'm not speaking in the sense of a traditional relational database, necessarily, but the idea of how that data empowers businesses and enables them to do different things. Is that directionally a fair synopsis of how you see it?Chris: I think the [unintelligible 00:09:21] thing is what I would agree with. What makes it really interesting to me is what we enable people to do with that data, and being able to build, kind of, really fascinating innovation applications that are affecting their underlying businesses, right, from it could be health care, it could be airlines, financial services, some really high, interesting use cases that people are doing that are leveraging the database to be able to drive that level of innovation. Because it's very difficult; I can build some sophisticated application, but if I can't get the performance out of my database, I have a pretty poor experience to my users in today's world. Because, fortunately or unfortunately, people aren't very patient, right? If you have a website that doesn't return very quickly, a customer's gone like minutes ago. You literally got to instantly respond to someone. That's a challenging problem.Corey: It absolutely is. Something that I found as I've talked to a bunch of different companies operating in different ways is the requirements on data stores are generally very different depending upon primarily latency and performance. There's only so long people that are going to watch the spinning circle of doom on a website spin before they realize they're going to go somewhere that has its act together. Conversely, for a lot of business intelligence and analytics queries, there are an awful lot of stories where the thing that people care about is that we actually have to have the results of this query by noon on Thursday. And there are very different use cases for that, and some companies seem to be focused very much on, “We're going to solve both of those use cases extremes and everything in between with the same product offering,” and others tend to say, “Okay, this is the area of the market we're going to focus on.”You could also say that this is an expression of the larger industry question of do I want, more or less a one-size-fits-most database that's general-purpose, or do I want very specific purpose-built databases based upon the use case and the problem? Where do you find yourself on that spectrum?Chris: I find myself on that spectrum is that there's—if you want to describe it at a high level and we can break it down, there's operational-type databases, where I'd say Couchbase fits where you're talking about, I've just built an application; I'm talking to the live user, right, this is what I care about, and when I'm talking about speed and performance here, I'm talking about something that returned within milliseconds of response time. I'm playing an online game, or I'm doing online betting on a sports game. That has to be pretty much instant, right? If we're playing multiplayer games and you're doing something, then I want to be able to see what you're doing straight away, right? People don't expect it to lay there.If you're looking at streaming—people do this with Couchbase—streaming the Olympic Games or Super Bowl in the US, and you want to be able to be there, that whole profile management of that user has to be instant, have that stream to you has to be instant. People use telephone calls and use Couchbase to do, behind the scenes, profile management, right, so they know who you are who's making that call. That's an operational database problem. That's not a traditional analytical problem, right? So, there's a whole other space in the database world for analytics, right, which is bringing all the data together into one place, and I'll help you do data science, AI, machine learning, be able to crunch and compute large volumes of data. If I get back to you, rather than a week in an hour, that's great, but that's not operational. That's analytical.Corey: In data center environments, it's an argument to be made for going in a bunch of different directions; we're going to use a bunch of different data stores to store all these things. Because, generally speaking, the marginal cost of moving data from one of your data storage systems to another one of your data storage systems, one rack row over is fairly small, whereas in cloud, effectively, there are no real capacity constraints anymore until you can get the bill, but that's the entire problem where a lot of the transfer for these things is metered per gigabyte. So, there's a increased desire on a lot of architectural pressures, to wind up making sure that where the data lives, it stays. And whatever it is that you do with that data, it should be able to operate on that data in a way that fits your performance characteristic requirements in the place that it currently is. And on the one hand, I can definitely see that driving a lot of decisions people have made.The counterargument is that it feels a little weird when the cost constraints of how the cloud providers—mostly you, AWS—have decided to build these things out. And that, in turn, is shaping your entire approach to not just your architecture, but your systems design of how data winds up working its way through your lifecycle. It's frustrating, on some level, especially given that they themselves offer something like 15 distinct managed databases offerings but more announced all the time. It becomes very difficult not only to disambiguate between all of them but to afford moving data from one to the next.Chris: The affordability is an interesting discussion, right, because you can look at it from a billing perspective and go absolutely, there's a challenge associated to that. Then is a question of where is my data because it's spread across all these different services; that's another challenge. And then you have the challenge of, okay, the cost associated to having developers build applications against all these different types of services because they all require different APIs and different ways of programming. So that's, there's a cost associated everywhere.Corey: Oh, by far and away, the most expensive part of your AWS, or any cloud spend, is not the infrastructure itself; it's the payroll expense associated with the people working on it. People always cost more than infrastructure. If not, something very strange is going on.Chris: But then you look at it, and you go, okay, if that's the case, I kind of use the analogy, right, that it's like a car, where everyone is talking these days about the electric car [that's going 00:16:05] on that path, right? Now, I should be able—if I was getting an electric car—think of it now, I actually have one—that I can get in the car and I can drive it like any other car. I know what a steering wheel is, I know where my pedals work, it looks and feels like a normal car. But architecturally it's fundamentally different how it operates. So, why can you apply that same thing, that same analogy to a database, right?So, why can't I have the ability from an operational perspective, [unintelligible 00:16:42] talk about operational databases, not necessarily, I don't know, full-blown analytical databases, but operationally being able to say I can store the data in an enterprise database; I can use that to leverage my SQL skills like I have before, and also use it to have a document store under operational analytics, to eventing, to full-stack search, key things that people want to do operationally, but keeping the data together in one database, like an iPhone. I want a database to have these capabilities; I don't want to have all these different types of devices that are everywhere. I want, you know, my iPhone to be able to go to have the capabilities that I'm using. Or my car, to feel like I'm driving a car; doesn't matter if the underlying architecture of the engine changed. That's great, I want the benefit, but I want to be able to drive it in the same way that I've driven any other car out there. And that's kind of trying to solve multiple problems that because you're trying to solve the issue of skills.Corey: It's one of the hard challenges out there, and I think your car analogy can even be extended a bit further because in the early days of the automobile, you were more or less taking some significant risk by driving a car if you weren't also mechanically inclined and to fix it yourself. And in time, we've sort of seen that continue to evolve where they mostly work, and now they work really reliably. And then you take it even a step beyond that, and all right now I'm just going to pay a car service so someone else has to deal with the car and a driver, and I don't have to deal with any of that aspect. And it feels like there are certain parallels, similar to that, toward the end of last year, 2021, you folks, more or less moved away from you can have it in any color you want, as long as you run it yourself—more or less—into offering a fully-managed database-as-a-service cloud option called Capella, which, on the ads for this show, I periodically sing because if you didn't want me to do that, you would not have named it Capella. Now, what was it that inspired you folks to say, “Hm, we could actually offer this as a managed service ourselves?”It's definitely a direction a lot of companies have gone in, but usually, they have to wait to be forced into it by—let's be serious for a second here—Amazon launching the Amazon Basics version of whatever it is themselves and, “Okay, well, they validated our market for us. Let's explore it.”Chris: If you look at that, you go Couchbase has been around for a good few years now selling, as you point out, high-performance databases to large-scale enterprises, on real mission-critical, people call it tier-zero type applications, high-performance applications. And these are some of the most fascinating, most innovative type of applications that I've been involved with through my career. Now, how can we take that capability, provide it to the mass market if you'd like, to be able to give it to people that don't need to have a large number of people out there managing their own infrastructure, being able to understand how to finely tune that underlying infrastructure to get the level of performance that you need from high-performance databases. Now, there are use cases for doing that, so it's not one or the other. It's not that you have to go all-in.There are particular companies out there that, for the economics reasons, for the use case reasons that are running today on-premise, and there's a rational reason for why they do that, right? But for a lot of people out there, whether they're leveraging the cloud, there's an opportunity here to take the power of the database, allow us to then manage it for people, take away that complexity of it, but being able to give them the power so they can leverage their skills, take advantage of Couchbase far easier than ever have been able to in the past. It's opened up a bigger market for us, to summarize your question.Corey: This episode is sponsored by our friends at Oracle Cloud. Counting the pennies, but still dreaming of deploying apps instead of “Hello, World” demos? Allow me to introduce you to Oracle's Always Free tier. It provides over 20 free services and infrastructure, networking, databases, observability, management, and security. And—let me be clear here—it's actually free. There's no surprise billing until you intentionally and proactively upgrade your account. This means you can provision a virtual machine instance or spin up an autonomous database that manages itself, all while gaining the networking, load balancing, and storage resources that somehow never quite make it into most free tiers needed to support the application that you want to build. With Always Free, you can do things like run small-scale applications or do proof-of-concept testing without spending a dime. You know that I always like to put asterisks next to the word free? This is actually free, no asterisk. Start now. Visit snark.cloud/oci-free that's snark.cloud/oci-free.Corey: One way that I tend to evaluate where a given vendor sees themselves—and it's sort of an odd thing to do, but given that I do fix AWS bills for a living, it probably makes sense—I wind up pulling up the website, I ignore the baseline stuff of the, “This is what Gartner says,” and here's a giant series of scrolls. I just go for the hamburger menu and I look for, “All right, where's the pricing information?” Because pricing speaks a lot. And there are two things I generally try to find. One is, is there a free trial that I can basically click and get started working with?Because invariably, I'm trying to beat my head off of a problem at two in the morning, and if it's, “Oh, talk to a salesperson,” well as a hobbyist, or as an engineer who does not have signing authority for things, but it's talk to sales, I realize, “Oh, yeah. One, I probably can't afford it. Two, it's going to be a week or so before I can actually make progress on this, and I'm hoping to get something up by sunrise, and it's probably not for me.” Conversely, the enterprise tier should always have a, “Call for details,” because that is a signal to large enterprise procurement departments and buyers and the rest were it's, “Oh, we will never accept default terms. We always want them customized. We also don't believe in signing any contract without at least two commas in it.”Great. So, being able to speak to both ends of the market is one of those critical things that you folks absolutely nail that. What I like is the fact that if someone has a problem that they're experimenting with at two in the morning, they can get started with your database-as-a-service platform—Capella; or however you want to sing it—and they don't need to wind up talking to you folks directly, first. There's no long-term commitments, there's no [unintelligible 00:22:39] of the infrastructure themselves. There's no getting hounded for the rest of their days over making a purchase for something that didn't pan out.To me, that's always been the real innovation and breakthrough of cloud is that I can spend a few hours some evening kicking around an idea, and if it doesn't work, I can turn it off and spend 17 cents on the process, whereas if it does work, I can keep scaling up without at some point having to replace all of the Raspberry Pi's and popsicle sticks, I build things with real enterprise-grade stuff. There's a real accessibility and democratization that is entered into it. So, I'm always excited when I see companies that are embracing that model. Because, yeah, I'm a grumpy old sysadmin because it's not like there's a second kind of sysadmin, but—and I have a particular exposure and experience level with these things that I can't expect modern developers to work on. They have an idea, they want to launch something, and they just need a database to throw things against and put data into, and ideally get it back again when they query later. And that empowers them to move forward.They're not in this because they really want to run virtual machines themselves and get those set up and secured and patched and hardened, and then install the software on top of it, and, “Why is it not working? Oh, security groups, how you vex me again. I'll just open you to the entire world,” and so on. And we know where that path leads. So, it's nice to see that there is an accessible option there.Conversely, if you come at this with an approach if we are only available in our hosted cloud environment, well now those big enterprise companies that have, you know, compliance concerns are going to have some thoughts for you, none of them particularly pleasant in some cases. So, I like the fact that you're able to expand your offering to encompass different user personas without also, I don't know, turning what has historically been a database into now it's an LDAP server, and trying to eat the world, piece by piece, component by component.Chris: It's interesting that you say that because I think there's a number of things that you're touching on that were to me, if you look at us as a company in particularly this space, there's a lot of focus around the community and the open-source community. And I think there's an element of how do you make it accessible to people as a community as a whole? And then you kind of go down the path of, “Okay, let's allow people,”—as a developer, let's think of it this way, right, the ultimate thing they want to do, and you touched upon it there, is they want to build an application. They get passionate about building the application or maybe even in the weekend, and they got this funky idea that they're going to literally knock some code out.And I remember my fond memories of being an application engineer of being able to sit down for hours just been able to put my ideas into code and watch it execute. The last thing that I want to do is get to the point where I get the database and go, oh, here we go. This is going to take me a bunch of hours, now, and I'm going to set it all up and do other stuff. And I almost literally want to be able to click a few buttons—Corey: You know what I want to do tonight? Feel really dumb as I tackle a problem I don't fully understand. Gr—I'd love smacking into walls that point out my own ignorance. It's discouraging as hell. I'm right there with you.Chris: Yeah, you don't want to do that, right? So, you almost want to make like the database disappear for people, right? You want to be able to just say, like, “Here's your command. Off you go. Bring the data back. Bring it back in full. Allow it to scale.”Because you want that developer to have that experience of not breaking their flow. And what do you want them to be able to be so excited about the application and innovation that they've built, that they want to go and show that teammates? They want to say, “Look at the great thing I built over the weekend. Look at this, this is amazing.” Right?And then be able to get all the teammates pretty excited about what they built in a way in which they can try it out really easily, right? They can take this little thing that they built into the database, click some buttons, and off we go, right? And now your development team is super excited about some of the great innovation that you have. But you also have to have the reverse. You have to have the architecturally sound, so then when you get to the architect, if you like, who is looking at the bigger picture of what's the future going to look like? Is it the right technology? Is this something that we can bring into the organization? And you know, this is a cool bit of application you just built me, but you know, is this realistic that I can deploy this thing?And this is where you start going back into it still has to have high performance, the security has to be there, the scalability has to be there so that I can potentially—I can start small and grow this thing horizontally as I see the requirements coming. There are different set of requirements architecturally, so we're looking at—you know, as a company, our key focus is how do you drive that developer community so that you give the people the freedom to build the next generation of applications in the simplest way [unintelligible 00:27:35], say with free trials, click some buttons, have the database up in minutes, but also then being able to have that capability in the underlying database to take it to the architect. That's what our core focus is every day.Corey: I agree with everything that you're saying. You're making an awful lot of great points, but for me, the proof in the pudding is the second thing that I tend to look at on your website after the pricing page, and that is your list of customers. Because it's always interesting when someone talks about how they're revolutionizing everything, and this is the way to go, and everyone who's anyone is doing these things. And then you look at their customer page and either they don't have one, which is telling, or the customers on that page are terrifying in that, “Wow, that sounds like a whole bunch of fly-by-night startups whose primary industry is scamming people.”You have a bunch of household blue-chip names as well as a bunch of newer companies that are very clearly not what people think of as legacy—you know, that condescending engineering term that means it makes money. It's across the board, it is broad-spectrum, and it is companies that absolutely know exactly what it is that they're doing when it comes to these things. That to me is far more convincing than almost anything else that can be said because it's—look, you can come on and talk to me about anything you want about your product, and I can dismiss it and, “Yeah, whatever. Great.” But when I start talking to customers, as I did prior to recording this episode, and seeing how they talk about you folks, that to me is what reaffirms that, okay, this is actually something that has legs and is solving real customer problems.Because early stage, it's, “We have this idea for this company we're going to build that it's going to be great.” “Awesome. Go talk to more customers.” That is a default, safe piece of advice generically you can give to anyone. And it's easy to give and hard to take.I've been saying this for years, and I still screwed it up and we started trying to launch a SaaS product here called DuckTools. Yeah, it turns out that we didn't talk to enough customers first about what they're actually trying to achieve, and we assumed we knew the answers. It's an easy mistake to make. What I really appreciate is—about a Couchbase in particular—is not just the fact that you have all of these customer references, but the fact that each one talks about what the value to the business is not just in terms of, “Oh yes, now we can query data and there was no way for us to do that before.” Of course, people have found ways to do that since business started.Instead, it's much more about this is how it made it more efficient, more optimal, how it unlocked possibilities and capabilities for us. That alone tells me that there definitely is significant value that you're delivering to customers. In my own business, whenever I think I've seen it all, I have to do is talk to one more customer and learn something new. What have you seen in recent memory, from a customer, that surprised you about how they're using Couchbase?Chris: You look at that, and you can see—I could probably talk for hours on different types of customers, but it's the ones that you can literally see in your life and you can reflect to, right? So, if you taken one of the biggest airlines that are out there today, they're completely changing, kind of, the whole experience. And our whole experience of and how do I get feedback? Because Couchbase's customers, [unintelligible 00:31:01] customer, right, is what they're thinking about, right? They're an airline.So, these passengers; fine. But how many times have you got on a plane, and you see all these people, literally, there's obviously the passengers, and then there's the cabin crew, and then there's the people on the ground, and then there's the pilot, and for the sake of the discussion, the staff that are there are literally passing paper back and forth to each other. And surely there a better way to do this. And for someone who likes to solve complex technical problems, you go, “Wow, this is going to be a bit of a challenge.” Because if you want to collect feedback from an aeroplane in the air, [laugh] right, and you want to connect that to the ground data that people are having in terms of maintenance data, you want to do that across the world, in multiple different time zones, that's pretty tricky problem to try to go solve, right?So therefore, how do you get a database that is able to work remotely and on what people would call the edge; let's just call it in this case in a device that's literally a cabin crew member is carrying around with them that's not connected because there is no connection because I'm in the middle of the air. But I want to pair it with the other cabin crew members that are around, right, in flight, and then when I land, I want to sync that data backup to the maintenance people. So, you need a database that's able to operate on a device with no connection, and then being able to synchronize backup to a cloud database that is then collecting data from all the other flights around the world.Corey: Synchronization sounds super easy until you actually try and do it, and then, “Oh, wow.” It's like, you could cut to pieces by the edge cases.Chris: And then people go, “Well, there's no problem. There's internet everywhere these days.” Yeah, sure there is. [laugh]. You get disconnected all of the time.Corey: Not to name names. This is very evocative, an earlier episode of this show I had with Tyler Slove, who's a senior manager over at United Airlines, about specifically how they're approaching a lot of their own reimagining and the rest. It's a fascinating use case, and as someone who's a bit of a travel geek himself—you know, in the before times—that's always an area of intense interest because it's… I'm sorry, I'm still a little boy at heart; it's magic to me. You get on a plane, you go somewhere else, close the doors, it opens it up, and you're on the other side of the world. And now there's internet on it? Oh, my God, who would have imagined such a thing?Chris: Uh-huh. But that's changing the experience for people. It's just really fascinating.Corey: Completely. And it's empowering and unlocking that experience you're talking about of being able to sync between the crews, about handling all this stuff behind the scenes. Everyone loves to complain about airlines because no one knows really how to run the massive logistical part of an airline. But the WiFi was a little bit slow or the food was cold; well, that's something I know how to complain about Twitter.Chris: [laugh].Corey: It becomes this idea of almost a bikeshed problem expression, where it's, “Oh, yeah. I'm just going to complain about things I can wrap my head around.” Yeah.Chris: I was talking to somebody recently, and they were—swapping topics a little bit—and they were like, so—they were talking about innovation on some new web application that they built. And I literally have to explain them, and I said, “Well, if you think of it, the underlying whole technology stack that's behind this for high-scale e-commerce, it's sophisticated, right, because people will literally walk away from a page, an application, a mobile app, if they don't get an instant response time. And that request has to literally travel, physically, quite a fair amount of distance, talk to multiple different types of technology, answer to that question, then come back to you instantly.” The sheer amount of technology that's involved here of moving that data around is a complicated architectural problem to fix. A database only plays a small part of that. You can't be the slowest player in the party.Corey: No. And that is always the challenge is that when you're looking at different use cases, there's always a constraint, and how that constraint winds up manifesting in different ways, if it's not the thing that's slowing things down, it's also not where the attention goes. If you have a single thing like, the database for example, slowing things down, everyone cares about improving databases, people focusing on, “Well, we're going to improve the JavaScript load time on the website,” that's not the problem. Find the bottleneck and focus on it. And although I'm generally a fan of picking a database and using that as a general-purpose thing until it makes sense not too—much like I am cloud providers—[audio break 00:35:54]Corey: —journey personally, where's the best place to find you?Chris: Clearly, if you want to find more about Couchbase, you can obviously go to couchbase.com. You kindly pointed out you can go and look at the trial for Capella and try out the tech. You're more than welcome to do that as a free trial.If you want to contact me particularly, you find me on LinkedIn; I'm Chris Harris at Couchbase. You'll find me [unintelligible 00:36:26] with Chris Harris in general and probably find lots of them. In the UK, Chris Harris is a famous racing driver. That's not me; it's someone else. So, find me on LinkedIn, I'm sure it won't be that difficult to find what you find. Or you can find me on Twitter.Corey: And we will of course, but links to all of that into the [show notes 00:36:43]. I really want to thank you for being so generous with your time today. It's always appreciated to talk to people who actually know what they're doing.Chris: You're more than welcome. It's been great to be on the show. Thanks, Corey.Corey: Chris Harris, Vice President of Global Field Engineering at Couchbase. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice along with an angry comment. I'm going to wind up using all of those angry comments, at one point, as a database.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.Announcer: This has been a HumblePod production. Stay humble.

Andrew is Founder and CEO at Praxi Data which has a patented matching engine coupled with curated data libraries to enable true enterprise data discovery. Praxi Data enables you to search across all of your data with any coding standard and run their ML algorithms to discover the best quality source information, securely. They provide continuous deep data-driven insights for businesses and organizations by automating key processes with industry-specific expert libraries of terms. Prior to founding Praxi Data, Andrew spent time at Waterline Data, Hortonworks, Intercontinental Exchange, and the NYSE. Listen in to the episode to hear how Praxi Data got started and where they're at in their journey to making the most out of data: Show Notes: Learn more about Praxi Data: https://praxidata.com/ Check out Praxi Data's blog: https://praxidata.com/blog/ Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrewahn1 On tap for today's episode: Ikon Coffee and Bulletproof Coffee Contact Us: https://www.hashmapinc.com/reach-out

Sharad Varshney is Co-Founder and CEO at OvalEdge, a cost-effective data catalog and data governance toolset designed to be used by everyone, every day, to get trusted data when they need it. Prior to OvalEdge, Sharad spent time at Hortonworks, Cohesive, and other tech companies. Listen in to the episode to hear why Sharad believes data governance is a necessity for all businesses and how setting standards helps to ensure that data is trustworthy. Show Notes: Learn more about OvalEdge here: https://www.ovaledge.com/ Connect with Sharad at: https://www.linkedin.com/in/sharadvarshney/ On tap for this episode: Turkish coffee from Café Intermezzo and espresso from Nespresso. Contact Us: https://www.hashmapinc.com/reach-out

Episode 17: Navigating Your Life and Career with Liz Bronson & Kathleen Troyer By the time you've reached midlife, you've taken a few twists and turns in your life and career. Too often, we try to bifurcate our lives and jobs as if one has no impact upon the other, but that's impossible. You can't completely separate the different areas of your life. They are interconnected. Your job and career affect your personal life, and the individual decisions you make in life will impact your job and career. Today, our guests on the Midlife Career Rebel Podcast, Liz Bronson and Kathleen Nelson Troyer, hosts of the Real Job Talk Podcast, share their own life and career journeys and the wisdom they gleaned along the way. In this episode of the Midlife Career Rebel Podcast, you'll discover… Liz and Kathleen's career journeys. The power of having clarity about what you want to do. Why learning to say no helps you become clearer about what to say yes to. How to embrace getting uncomfortable to get to what you want. Why listening to your own voice is so important. The magic that comes from women working together in community. Featured On The Show: Learn more about the Career Rebel Academy: https://bit.ly/3mZ7Mwm Real Job Talk Podcast: https://realjobtalk.com/ The Ambition Decisions: What Women Know About Work, Family, and the Path to Building a Life (book mentioned): https://www.amazon.com/Ambition-Deci sions-Women-Family-Building/dp/0525558810 Liz Bronson is the VP of People at SupportLogic, a Series B start-up using the power of natural language processing to improve Customer Support by predicting and pre venting escalations, reducing churn, and protecting and growing revenue. In this role, Liz is building SupportLogic's people programs, including hiring, performance, trai ning, and enabling culture to attract and retain employees. Before SupportLogic, Liz was the owner of Liz Bronson Consulting- an HR and recruiting company that helped mostly tech start-up teams (SignalFx, Pulumi, Evernote, Hortonworks, and MyVest, to name a few) build their employee infrastructure and hire the right people to enable their success. Liz is passionate about designing authentic processes that match a company's culture. You can also find Liz on LinkedIn and Twitter. Kathleen Nelson Troyer is the CEO of Jigsaw Solutions, Inc., where she works with companies as a leadership, team, career coach, and organizational development con sultant. Kathleen works with her clients to grow strong leaders aligned with company values and contribute to healthy cultures where people want to come to work each day. Kat believes that everyone is the CEO of their own life and encourages her clien ts to step into greater leadership of their lives and their careers. Kathleen founded Jigsaw Solutions in 2003. Before that, she held roles in Staffing and Human Resources with Charles Schwab, Barclays Global Investors, Spirent Communications, Cloudera, and several technology start-ups. You can also find Kathleen on LinkedIn and Twitter. If you have questions about applying this work, email us at hello@carolparkerwalsh.com or reach out to me on social media (see the links below). Check out my FREE three part video series 10 Minute Career Jumpstart [https:// bit.ly/3zWJoz5] and learn what it takes to get the life and career you want. This video series is a game-changer. Need more support creating the career you want? You can do that in the Career Rebel Academy [https://bit.ly/3mZ7Mwm]. Rate, Review & Follow on Apple Podcasts "I'm loving the Midlife Career Rebel Podcast!" If that sounds like you, help us support more people like you to create a career and life they love. After all, the Midlife Career Rebel Podcast would not be possible without you. Click here for Apple Podcasts and scroll down to rate with five stars, select "write a review," then be sure to let me know what you appreciated about this or any other episode. You can also find us on Spotify. And if you're not following the podcast, don't forget to subscribe. Apple: https://podcasts.apple.com/us/podcast/the-midlife-career-rebel-podcast/ id1592972920 Spotify: https://open.spotify.com/show/2HxFeNxpf43J4FJRKxEmim And finally, manage your mind, value your brilliance, and courageously take action to step into the driver's seat of your life and career! Want more support to become the CEO of your life? Reach out to me at hello@carolparkerwalsh.com. Thanks for listening, Carol Website: https://www.carolparkerwalsh.com/podcast LinkedIn: https://linkedin.com/in/parkerwalsh Instagram: https://instagram.com/drcarolparkerwalsh Twitter: https://twitter.com/drcpwalsh Facebook: https://facebook.com/DrCarolParkerWalsh

On this episode of Hashmap on Tap, host Kelly Kohlleffel is joined by Suresh Srinivas and Harsha Chintalapani. Suresh and Harsha are the Co-Founders of OpenMetadata, a rapidly growing open source project that defines specifications to standardize metadata with a schema-first approach and a centralized metadata store that integrates with popular systems and services in the data stack to collect, store, and index metadata via API and through a web UI. These two have a wealth of information and experience from their time at Uber, Hortonworks, and Yahoo. Listen in to hear their perspective on the data landscape today and what they're up to at OpenMetadata plus their insights across a range of topics including how architecture is only clean in powerpoints (or google slides), the fact that data "missed the boat" on UX, and their thoughts about how data is a team game. Show Notes: Check out OpenMetadata: https://open-metadata.org/ Join the OpenMetadata Slack Channel: https://slack.open-metadata.org/ Check out their project on Github: https://github.com/open-metadata/OpenMetadata Experience OpenMetadata in their Sandbox: https://sandbox.open-metadata.org/signin sandbox.open-metadata.org Connect with Suresh on LinkedIn: https://www.linkedin.com/in/sureshsri/ Connect with Harsha on LinkedIn: https://www.linkedin.com/in/sriharsha/ On tap for today's episode: Ginger tea, Coffee, and Ginger Peach Tea Contact Us: https://www.hashmapinc.com/reach-out

Steve Sordello has spent nearly three decades working for some of the most iconic technology companies in Silicon Valley. Steve brings a diverse background in strategy, operational and financial management, mergers and acquisitions, and corporate leadership. Steve has served as the CFO of LinkedIn Corporation (LNKD), the online business networking service.During his tenure at LinkedIn, he oversaw LinkedIn's successful IPO, scaled the company from $10M to over $8B in revenue, raised critical capital and completed multiple private and public acquisitions, including LinkedIn's $26.2B merger with Microsoft where Steve has since continued to play a key leadership role in its success.Prior to LinkedIn, Steve served as CFO of TiVo, Inc. (TIVO), a manufacturer of digital video recorders, where he helped drive the company to profitability. Prior to TiVo, he served as CFO at Ask Jeeves, Inc. (ASKJ), an internet search engine company where he drove the dot-com turnaround, building a high-growth, highly profitable business and was instrumental in its sale to IAC in 2005. Steve also held senior roles at Adobe Systems, Inc. (ADBE), the global leader in digital media and digital marketing solutions. At Adobe, he drove the overall planning process and worked on a number of critical projects, including driving the financial analysis behind the acquisition of Photoshop, the launch of the Creative Bundle Suite, and the product launch of Acrobat. Prior to Adobe, Steve started his career on a rotation program at Syntex Corporation, a pharmaceutical company that was acquired by Roche Pharmaceuticals in 1994.Steve also serves as an independent director and audit committee chair at publicly traded Atlassian (TEAM), a leading provider of collaboration, development, and issue-tracking software, as an independent director at publicly traded Compass (COMP), a real estate technology company, and as a non-profit board member, trustee, and audit committee chair at Santa Clara University. Steve also served as an independent director and audit committee chair at publicly traded Cloudera (CLDR) up to its merger with Hortonworks in 2019.

Marketing leaders are faced with a litany of challenges, an ocean of tools, and seemingly infinite amounts of data, which can all get a bit overwhelming. Ingrid Burton, CMO of Quantcast, is passionate about the industry and on Marketing Trends she discusses with me some of the obstacles the modern marketer faces. “The challenges of today's CMO are very different than the challenges of even five years ago, 10 years ago. It is such a fast-moving space and CMOs have to be well versed in strategy and data in understanding the market. It's such a big job now. I wonder how my fellow CMOs are doing, because like I said, I started my day at four-thirty this morning because I lay awake at night with all these asks and I [wonder] how am I gonna get it all done? Do I have the right team on the field? Can we really execute this? Can we measure our results and make sure we're getting the attribution that we need. We need to be thinking about how we make sure CMOs don't burn out. How do we make sure CMOs are able to lead through this? And how do we make sure that the expectations are realistic?” There will never be an end to all of the additional things a marketer does, another channel to add to the mix, but be careful not to push yourself or your team beyond your limits. In this episode, Ingrid unpacks what they mean at Quantcast when they talk about providing a free and open internet. She delves into her passion and in-depth knowledge of machine learning, and how marketers can best utilize their endless amount of tools. She also explains why ESG is going to be a main driver for them next year and how they're ensuring true Diversity, Equality, and Inclusion. There's so much to enjoy, up ahead with Ingrid here on Marketing Trends. Main TakeawaysThe Challenges of the Modern CMO: The rapid pace of the software-driven industry is a lot to keep up with. Getting more data and analytics capabilities has driven a lot of growth in the last 5-10 years. The constant rush of information combined with the constant demand to put information out can lead some of even the most passionate marketers to burnout. Guarding against that is going to be what separates the leaders of the future. The expectations of many CMOs and marketing leaders are very high. The Value of a Free and Open Internet: The value of having clear and factual information widely acknowledged and accepted in culture is essential for unity. The internet disrupted the journalism industry, and this change has brought about the conversion to subscription fee-based models over the traditional ad-based mode. This means that some people don't have access to the factual information they could be learning their news from. Machine Learning - The Power of Noticing Patterns: Pattern recognition is one of the most useful tools in leadership and in scaling business. Machines that can be taught to recognize certain patterns can do so and scan the entire database instantaneously. If you can notice patterns in marketing that can help you predict what your customers may be interested in or looking for at certain times of the year, times of day, devices, or locations. The power of machine learning in marketing is just in the early stages.Key Quotes“Hopefully I don't say ‘I' too much. I always want to say ‘we' - We did this. We did that. I'm just the guide; here's the north star we want to take. Or as I put it, here's the mountain we need to take. I put that out there very early on. I think my team here was very surprised. And when I showed them just a few baby steps of how you're gonna climb small hills to get to the top of the peak, they saw that they could do it. They accomplished it. Some of it's confidence-building and having them believe in themselves.”“Who can afford to subscribe to all these news publications. There's gotta be a different way. I'm afraid for a society that if we charge for every piece of content, what's going to happen to people that can't afford it [is that] they're gonna be left behind. They get left behind because they're not getting the right news. The internet is a great equalizer and we need to make sure that it's not a fee-based internet.” “One of the things that's unique about Quantcast is we have this unique, real-time data set and it's one of the largest in the world behind Google and Facebook. Since we started the company, we have established a relationship with all the publishers out there. This is Hurst which is huge, Conde Nast...we have a hundred million websites. Their data is feeding into this anonymized data set. That is one of the largest actually running in the Amazon cloud, one of the largest that they have. We're using machine learning to find patterns and make predictions about the behavior of what's happening in this data set.”“The challenges of today's CMO, are very different than the challenges of even five years ago, 10 years ago. It is such a fast-moving space and CMOs have to be well versed in strategy and data in understanding the market. It's such a big job now. I wonder how my fellow CMOs are doing, because like I said, I started my day at four-thirty this morning because I lay awake at night with all these asks coming at me and I [wonder] how am I gonna get it all done? Do I have the right team on the field? Can we really execute to this? Can we measure our results and um, really make sure we're getting the attribution that we need. We need to really be thinking about how do we make sure CMOs don't burn out? How do we make sure CMOs are able to lead through this? And how do we make sure that the expectations are realistic?”BioIngrid Burton is a unique leader in the world of tech as she bridges the gap between technology and marketing in leading teams to unparalleled successes driving strategies for market trends including AI and machine learning, Java and HANA technologies, SaaS, Cloud Computing, Open Source, Internet of Things (IOT), community engagement and Big Data that have had a positive impact on the evolving technology landscape.Ingrid's career includes her role as a member of the board of directors at Extreme Networks. She also held the role of Chief Marketing Officer at H2O.ai, the open source leader in AI and machine learning, where she led marketing teams while positioning the company through its growth stages. Prior to H2O.ai, Ingrid advised companies including DriveScale, MapR (acquired by HPE) and Paxata (acquired by DataRobot). She was CMO of Hortonworks, a Big Data company, where she drove a brand and marketing transformation, positioning the company for growth and subsequent acquisition.Ms. Burton led the Product and Innovation marketing team at SAP, where she was the marketing leader of SAP HANA, analytics, and mobile offerings, and where she co-created the company Cloud strategy. As CMO of pre-IPO Silver Spring Networks, she positioned the company for their IPO as the leader in energy networks. While CMO at Plantronics she reshaped a 50-year-old brand into a modern and exciting communications model for both consumers and business.Previously at Sun Microsystems, Ingrid held various leadership roles including head of marketing for the company, driving both the company and Java brand, global citizenship, championing open source initiatives, and leading product and strategic marketing teams. Early in her career, Ingrid was a developer.Ms. Burton actively engages with and mentors people in both technology and business functions, and provides guidance for them in their careers. She has received numerous awards including the 2005 Silicon Valley TWIN award.---Marketing Trends podcast is brought to you by Salesforce. Discover marketing built on the world's number one CRM: Salesforce. Put your customer at the center of every interaction. Automate engagement with each customer. And build your marketing strategy around the entire customer journey. Salesforce. We bring marketing and engagement together. Learn more at salesforce.com/marketing.

On this episode of Hashmap on Tap, host Kelly Kohlleffel is joined by Rohit Choudhary. Rohit is Founder and CEO at Acceldata providing a multi-dimensional data observability cloud that provides compute performance monitoring, manages data reliability, and enables data pipeline observability. Before founding Acceldata, Rohit spent time with Hortonworks as Director of Engineering and founded Appsterix which was acquired by 24/7 iLabs. Show Notes: Check out Acceldata: https://www.acceldata.io/ See Acceldata's Resource Library: https://www.acceldata.io/resources Connect with Rohit on LinkedIn: https://www.linkedin.com/in/rconline/ On tap for today's episode: Masala Chai & Gingerbread Tea from Republic of Chai Contact Us: https://www.hashmapinc.com/reach-out

John Kreisa has been a marketing leader at a string of data product companies, including Business Objects, Hortonworks, Docker and now Couchbase. In this episode, John breaks down his thinking on why Product Marketing is so central to a Marketing team at data software companies, and how that translates into effective sales motions. He also talks about the future of Martech in the explosive data world, and how the rule of 1's and 3's plays out in revenue growth.This is the episode to unravel how to build your Marketing team around data products, and how to get the Sales motion humming along. Essential listening for anyone working in Marketing and data - there's lots to get right - and lots to get wrong!Highlights:- Product Marketing is central to building a Marketing function - and underserved in UK/EU- The rule of 1's and 3's in scaling your revenue- MarTech has evolved fast aided by the Pandemic- In Product led companies are you Sell-to or Buy-from?- Data creation rates are accelerating- The rise of Marketing Ops 

Starting out as a software engineer, Ingrid Burton, CMO, Quantcast, quickly moved into Marketing during her long tenure at Sun Microsystems. Her deep experiences of Sun’s early pioneering move to Open Source key software components has fuelled much of her stellar career as CMO of many leading companies such as Hortonworks and H2O.ai. There aren’t many marketers who’ve had the same hands-on experiences as Ingrid, creating whole new categories and communities around key Open Source technologies.Highlights:-What are the facets of Marketing and how do they relate to OpenSource?- There isn’t a one size fits all Open Source GTM model- How to sell ‘The Squeeze’- Why not to over complicate Category Creation- Effective teaming between Sales and Marketing- When to hire your first Marketing VPAs an accomplished CMO she talks through the relationship between Sales and Marketing, and her thoughts on ‘the squeeze play’. A fascinating listen from one of Silicon Valley’s very best.In conversation with Andy Leaver, and Paul Papadimitriou.

Veljko Krunic is an independent consultant and trainer specializing in data science, big data, and helping his clients get actionable business results from AI. He holds a PhD in computer science from the University of Colorado at Boulder and an additional degree in engineering management from the same institution. His MS degree in engineering management focused on applied statistics, strategic planning, and the use of advanced statistical methods to improve organizational efficiency. He is also a Six Sigma Master Black Belt. Veljko consulted with or taught courses for five of the Fortune 10 companies (as listed in Sept 2019), many of the Fortune 500 companies, and a number of smaller companies, in the areas of enterprise computing, data science, AI, and big data. Before consulting independently, he worked in the PSO organizations of Hortonworks, the SpringSource division of VMware, and the JBoss division of Red Hat. In those positions, he was the main technical consultant on highly visible projects for the top clients of those PSO organizations. ————————————————————————————— Connect with me here: ✉️ My weekly email newsletter: jousef.substack.com

Learn how the Cloudera, Hortonworks, and MapR data platforms are evolving to meet the demands for real-time analytics and machine learning --- Send in a voice message: https://anchor.fm/tonyphoang/message

More than a year into a global pandemic, if there’s one thing meeting planners have learned, it’s that connecting with at-home attendees presents distinct challenges. In the virtual environment, every detail needs to be reconsidered, from the flow of the agenda to education formats, marketing and more. Even as in-person events return, it’s important to consider welcoming virtual attendees for hybrid gatherings. To discuss these points, we spoke with Melissa Park, a global event producer who has specialized in creating events for the tech industry, first at Hortonworks data software company in California, and now as an independent planner based in Australia. She recently launched a masterclass on 7 Steps to Event Success. On this episode of Eventful: The Podcast for Meeting Professionals, Northstar Meetings Group Deputy Editor Alex Palmer speaks to Park about how to keep attendee eyes on screens for virtual events.  This episode is sponsored by Colombia, the most welcoming country in the world for all events. See omnystudio.com/listener for privacy information.

Databricks 年初完成 G 轮融资，产品毛利比 Snowflake 还高？ 继去年 Snowflake 上市之后，Databricks 于今年 2 月宣布完成 10 亿美元融资，估值高达 280 亿美元，近年内亦有上市计划。独角兽出现，大厂布局，仅头部两家公司之和就有着千亿美元市场的「云数据处理和分析」行业似乎是 Saas 行业最热的领域。 在这期节目中，我们讨论了诸如 Databricks 创始初期开源与闭源的选择和优劣对比；同样提供云数存储和分析服务，Databricks 和 Snowflake 有何异同，处于上下游关系的二者在业务上怎样「相互渗透」，其不同收费模式的优与劣；而营收远高于 Snowfalke 的 Teradata，为何估值远低于前者，云服务和 on-prem 的商业模式差别究竟有多大；以及从整体上看，云数据存储和分析赛道的竞争格局呈现了出何种面貌。 这是 What's Next 科技早知道 SaaS 专栏的第一期节目。客座主播是我们的老朋友 Howie Xu，他是硅谷人工智能创投家。嘉宾是曾在 Databricks 担任高级产品经理的 Yifan Cao，他也谈到了自己在 Databricks 的工作体验。 欢迎收听。 P.S. 声动活泼联合「哈佛商业评论」共同推出的播客节目「新增长学院 (https://mp.weixin.qq.com/s/slbULn-ms2rH1RAXtnHiww)」，现已在 苹果播客 (https://podcasts.apple.com/cn/podcast/%E6%96%B0%E5%A2%9E%E9%95%BF%E5%AD%A6%E9%99%A2/id1556166168)、喜马拉雅 (https://www.ximalaya.com/shangye/47524187/)、小宇宙APP (https://www.xiaoyuzhoufm.com/podcast/604b56af7f61288928647ef1?s=eyJ1IjogIjVlOTNkMGJiNTNhZDY2ZDhiNWYxNjU2NSJ9) 等音频平台上线，欢迎订阅。 听众福利 欢迎在评论区分享你对本期节目的各种想法或观点，我们将在小宇宙APP和 @声动活泼 相关微博的评论区选出 10 位听友，分别送出著名脱口秀演员黄西（Joe Wong）3 月 28 日在北京「幽默小区脱口秀」的专场门票 1 张。更多信息请见 Mar 28th 黄西英语脱口秀专场（New） (https://mp.weixin.qq.com/s/zloyX9bSzbE3BfaWUiUSxg) ，活动时间截止到 2021 年 3 月 25 日。 【主播】 Howie Xu，硅谷人工智能创投家、Zscaler 副总裁 @H0wie_Xu (https://twitter.com/H0wie_Xu) 【嘉宾】 Yifan Cao (https://www.linkedin.com/in/yifancao/)，前 Databricks 高级产品经理、目前供职于 Apple 【主要话题】 [04:24] 从 on-prem 转到云端，SaaS 商业模式的兴起 [09:28] 「短期内大家会高估开源的价值、低估商业的难度」 [17:35] Snowflake 和 Databricks 的区别与相似之处 [22:46] Snowflake vs Databricks: 上下游的双方相互竞争 [25:17] 从不同收费模式看谁的毛利更高 [34:25] 云数据存储和处理赛道上的竞争格局 [42:47] 机器学习的发展趋势 【相关节目】 - #45 股神加持云端独角兽 Snowflake，SaaS 的黄金 10 年来了？ (https://guiguzaozhidao.fireside.fm/snowflake) 【延伸阅读】 - Apache Spark：一种用于大数据工作负载的分布式开源处理系统。它使用内存中缓存和优化的查询执行方式，可针对任何规模的数据进行快速分析查询。它提供使用 Java、Scala、Python 和 R 语言的开发 API，支持跨多个工作负载重用代码—批处理、交互式查询、实时分析、机器学习和图形处理等。 - Spark Summit：Apache Spark 旗下的社区活动，拥有来自 250 多个组织的超过 1000 位贡献者，是大数据中最大的开源社区。2013 年首次举办。 - Databricks：Databricks 由 Apache Spark 的创始人建立，成立于 2013 年，重研发尖端系统，以从大数据中获取价值。Databricks 的目标是从 Spark 开始，构建一系列更强大、更简单的大数据分析处理工具盒平台。 - On-Premises：通常简写为 on-prem，指运行在企业本地自建环境中的软件或解决方案。 - API：全称为 Application Programming Interface，指应用程序接口。 - Databricks 在 2021 年 2 月的融资新闻：Databricks raises $1B at $28B valuation as it reaches $425M ARR (https://techcrunch.com/2021/02/01/databricks-raises-1b-at-28b-valuation-as-it-reaches-425m-arr/) - Snowflake：完全基于云构建、充分利用云特性的企业级 SaaS 数据仓库产品，具有灵活性（即买即用）、高安全性、极致扩展性和弹性等特点，支持多租户、事务、标准 SQL 语法和半结构化、非结构化数据。于 2015 年开始上线使用。 - Cloudera：美国软件公司，向企业客户提供基于 Apache Hadoop 的软件、支持、服务以及培训。 - Hortonworks：一家位于美国加州帕拉奥图的商业计算机软件公司，专注于 Apache Hadoop 的开发和支持。Apache Hadoop 是一种框架，能分布式处理跨计算机集群的海量数据。 - 闭源：作为开源的反义词而出现的一个术语，指被用于任何没有资格作为开源许可术语的程序。 - Product/Market Fit：产品市场匹配度，指产品和市场达到最佳的契合点。 - Tableau Software：数据分析与可视化工具。 - Data Warehouse：数据仓库。 是为企业所有级别的决策制定过程，提供所有类型数据支持的战略集合。它是单个数据存储，出于分析性报告和决策支持目的而创建。 - Data Lake：数据湖是多结构数据的系统或存储库，它们以原始格式和模式存储，通常作为对象 blob 或文件存储。 - Lakehouse：一种结合了数据湖和数据仓库优势的新范式，解决了数据湖的局限性。Lakehouse 使用新的系统设计：直接在用于数据湖的低成本存储上实现与数据仓库中类似的数据结构和数据管理功能。 - Delta Lake：一个开源的存储层，为数据湖带来了可靠性。 提供 ACID 事务、可伸缩的元数据处理以及统一的流和批数据处理。 它运行在现有的数据湖之上，与 Apache Spark API 完全兼容。 - ETL：Extract, transform, load. 用来描述将数据从来源端经过抽取（extract）、转换（transform）、加载（load）至目的端的过程。 - BI：Business Intelligence，商务智能，一整套的解决方案，对象往往是企业的经营问题。 - TensorFlow：一个免费的开源机器学习软件库。 - PyTorch：一个基于 Torch 库的开源机器学习库，用于计算机视觉和自然语言处理等应用。 - DataRobot：一款高度自动化的机器学习平台。由 Jeremy Achin，Thoman DeGodoy 等人创建，该平台声称已经消除了对数据科学家的需求。 - Teradata：关系数据库管理系统之一，主要适用于构建大规模数据仓库应用程序。 - Zscaler： 成立于 2008 年的网络安全公司，通过云平台提供安全服务。 - Amazon Redshift：亚马逊旗下的一种完全托管的 PB 级云中数据仓库服务。 - BigQuery：Google 推出的一项 Web 服务，该服务让开发者可以使用 Google 的架构来运行 SQL 语句对超大数据库进行操作。 - GCP：全称为 Google Cloud Platform，谷歌提供的云端平台服务，包含了运算（如 Compute Engine、Google Kubernetes Engine）、资料分析（如 BigQuery、Cloud Dataflow）、以及 API 管理（如 Apigee API 平台、API 数据分析）以及机器学习（如 Cloud Machine Learning Engine）等众多产品。 - Yifan 提及的 Databricks 的「竞争对手」：Dataproc、Amazon EMR、Azure HDInsight、Amazon SageMaker、Kubernetes、Domino Data Lab、RStudio - 提及的其它术语：HDFS、MapReduce、Hive、Python、SQL、Batch computing、PySpark、Exploratory Data Analysis 【后期】 Luke，陈太太 【监制】 Amanda 【音乐】 - Super 1-Cospe - Wholesome-Kevin MacLeod - Hundo P-Wesky - Spider Theory-Sage Oursler 【关于我们】 网站：shengfm.cn 社交媒体：声动活泼 邮件：admin@sheng.fm 国内打赏支持：https://www.shengfm.cn/donation 国外打赏支持：http://www.shengfm.cn/donation Special Guest: Yifan Cao.

Andy Leaver, Operating Partner at Notion Capital, on how do we build on the people, processes and tech to get to the next revenue boundary — the 1s and 3s. Interviewed by Paul Papadimitriou and Stephen Millard.Highlights:- Why, as enterprise tech startups grow, they follow the rule of 1s and 3s- An effective rev ops function is essential- The “people, process and tech” that enable a startup achieve a particular revenue boundary are unlikely to deliver on the next- Why pricing is an art and founders need to become masters- Why your customers are your best salespeople  For any enterprise cloud startup the journey to scale is one that is built around inflection points, which are critical to success. You need to find product market fit, moving beyond founder-led sales to go-to-market fit and then onwards; each layer builds on the previous, with new capabilities and often new people, processes and tech. Few people in Europe have as much pedigree in this than Andy Leaver, Operating Partner at Notion Capital. Andy operated at the most senior levels - from Series A / B to IPO - at Hortonworks, Workday, SuccessFactors, Bazaarvoice and Ariba. He's had an amazing career and is now bringing that experience with him to the Notion Family.Read more: https://notion.vc/resources/scaling-enterprise-software-startups-a-story-of-1s-and-3s/

Dave McJannet is the CEO @ Hashicorp, one of the fastest-growing enterprise companies of our time providing consistent workflows to provision, secure, connect and run any infrastructure for any application. To date, the company has raised $349M in funding from some of the best in the business including Bessemer, Redpoint, True Ventures, IVP, Mayfield, TCV and GGV to name a few. As for David, prior to Hashicorp, he held some incredible roles including VP Marketing at Github and Hortonworks, Senior Director of Product Marketing @ VMWare and then also spent over 5 years at Microsoft.  If you would like to find out more about the show and the guests presented, you can follow us on Twitter here: Jason Lemkin Harry Stebbings SaaStr

In this episode, we get to talk to an insider of the big data movement. Arun Murthy is one of the founders of Hortonworks and has spent well over a decade in the big data industry. We ask Arun to walk us through what has challenged in the industry, his journey, and what is next Read More ...

Veljko Krunic is an independent consultant and trainer specializing in data science, big data, and helping his clients get actionable business results from AI. He holds a Ph.D. in computer science from the University of Colorado at Boulder and an additional MS in engineering management from the same institution. His MS degree in engineering management focused on applied statistics, strategic planning, and the use of advanced statistical methods to improve organizational efficiency. He is also a Six Sigma Master Black Belt. Veljko consulted with or taught courses for five of the Fortune 10 companies (as listed in Sept 2019), many of the Fortune 500 companies, and several smaller companies, in the areas of enterprise computing, data science, AI, and big data. Before consulting independently, he worked in the PSO organizations of Hortonworks, the SpringSource division of VMware, and the JBoss division of Red Hat. In those positions, he was the principal technical consultant on highly visible projects for the top clients of those PSO organizations. Veljko's LinkedIn page is https://www.linkedin.com/in/veljkokrunic/. ========= Some deals from Manning Publications (Veljko's Publisher) A permanent 40% discount code (suitable for all our products in all formats)- podfuture20 Five free eBook codes (each good for one sample of Succeeding with AI): suaihfr-925D, suaihfr-F88C, suaihfr-76D2, suaihfr-3783, suaihfr-91B7 Veljko's product page Enjoy! --- Send in a voice message: https://anchor.fm/thinkfuture/message Support this podcast: https://anchor.fm/thinkfuture/support

In this episode, Erasmus Elsner is talking to Bryan Offutt from Index Ventures, which is an active investor in the open source vertical, having invested in the likes of Hortonworks, Confluent, Elastic, Kong, Cockroach Labs and most recently Starburst. In this session we talk about the next frontier of open source, such as the open sourcing of pre-trained ML/NLP models (such as BERT) and distributed design. We also cover Index Venture's most recent COSS investment in Starburst, an open core company built around Presto, a distributed, open source query engine that has originally been developed at Facebook. Check out the Youtube version on: https://channel.sandhillroad.io

In today's Minutes: Honda will partner with GM to develop autonomous vehicles as part of a $2.5B deal, Hortonworks and Cloudera will come together in a $5.2B merger, and Highfields Capital is the latest hedge fund to shut its doors. Plus, men slack off when their bonuses are replaced with salary. Shocker. --- Support this podcast: https://anchor.fm/watercoolesthq/support

How do we educate the new, younger workforce coming into Oil and Gas? Mark and Guest-host, Paige Wilson, had a chance to talk with Mark Mathis, President at Clear Energy Alliance during our monthly happy hour about how he got started in making documentaries on Oil and Gas and short videos on energy. "We make short-run videos, on a wide variety of topics, that are not just oil and gas, it's renewables, it's climate change, it's anything connected to energy, we will do a video on it. Generally 4-4.5 minutes" says Mark Mathis of Clear Energy Alliance. "We can educate people in small bites along the way, get them engaged in it and interested, and then have them come back for another bite on the next video." Click Play to Hear the Oil and Gas HSE Podcast Episode 83 – Clear Energy Alliance Upcoming Events OGGN's Monthly Happy Hour: June 26th at TechSpace, 2101 CityWest Blvd., from 6:00 - 9:00 pm. There will be a giveaway of $250 cash at the event for the person who refers the most friends to sign up! Enter here. Click here to register! A special THANK YOU to this month's Happy Hour sponsors: Hortonworks and ReactWell Hortonworks is a leading innovator in the industry, creating, distributing and supporting enterprise-ready open data platforms and modern data applications. Their mission is to manage the world's data. They have a single-minded focus on driving innovation in open source communities such as Apache Hadoop, NiFi, and Spark. ReactWell provides advanced technology services and products for organizations in the energy, chemical, oil and gas and petrochemical industry, doing business inside and out of the United States of America. ReactWell achieves material quality results for their clients by blending creative solutions, constrained by the laws of hard science and scarce resources. Enter to Win! To get your hands on one of these awesome offshore bags, all you have to do is enter! Follow the link below and select Oil and Gas HSE and enter your information. We pick one lucky winner each week. Click Here to Enter More Information To find out more about Clear Energy Alliance, you can find them at https://clearenergyalliance.com/ Like Clear Energy Alliance on Facebook. Check out some of Clear Energy Alliance videos on YouTube. Follow Clear Energy Alliance on Twitter @clearenergy Connect with Mark Mathis on Linkedin.

In this episode, Paige sits with Jack Hinton at The Capital Grille CityCentre to discuss his journey in the Oil and Gas Industry to his current role as a Chief HSE Officer at Baker Hughes a GE Company. Reach out to Jack and learn more about Baker Hughes a GE Company. Leave a Review Enjoy listening? Support the show by leaving a review in iTunes. Sign Up and Win Click here to sign up here to win a FR Shirt and FR Base Layer from Bulwark! 2018 Event Sponsors OGGN is always accepting event sponsors.  If you would like to get your company in front of our large global audience, reach out to us and we would be happy to share the details. Events on Deck OGGN's Monthly Happy Hour: June 26th at TechSpace, 2101 CityWest Blvd., from 6:00 – 9:00 pm. There will be a giveaway of $250 cash at the event for the person who refers the most friends to sign up! Enter here. Click here to register! A special THANK YOU to this month's Happy Hour sponsors: Hortonworks and ReactWell Hortonworks is a leading innovator in the industry, creating, distributing and supporting enterprise-ready open data platforms and modern data applications. Their mission is to manage the world's data. They have a single-minded focus on driving innovation in open source communities such as Apache Hadoop, NiFi, and Spark. ReactWell provides advanced technology services and products for organizations in the energy, chemical, oil and gas and petrochemical industry, doing business inside and out of the United States of America. ReactWell achieves material quality results for their clients by blending creative solutions, constrained by the laws of hard science and scarce resources. IDT Expo 2018 - Come and say hi to the OGGN crew on Thursday, June 28th at the Norris Conference Center. More Oil and Gas Global Network Podcasts Oil and Gas This Week Podcast | Oil and Gas HS&E Podcast Engage with Oil and Gas Global Network LinkedIn Group | Facebook | modalpoint | Lean Oilfield | WellHub David Studio Emin is OGGN's Professional Audio Editor for all of our shows.  If you're interested in services, send an e-mail with OGGN in the subject to receive $5 off. Connect with Paige Wilson LinkedIn | Twitter | E-Mail | Oil and Gas Global Network Jack Hinton on Oil and Gas Industry Leaders Podcast - OGIL038

What is the root cause of accidents, is it one person's fault? Or the culture as a whole? Mark and Guest-host, Paige Wilson, had a chance to talk with Steven Riddle, Global Operations Integrity at ExxonMobil during OTC 2018 about how implementing change in culture will drive for change within the industry. "So what we as an industry need to do is understand within our cultures, we need to understand why, the why people make choices. Understand the why behind it, you know what empowers them to make the right choice, the safe choice" says Steven Riddle, Global Operations Integrity at ExxonMobil. Click Play to Hear the Oil and Gas HSE Podcast Episode 82 – Exxonmobil at OTC 2018 Upcoming Events OGGN's Monthly Happy Hour: June 26th at TechSpace, 2101 CityWest Blvd., from 6:00 - 9:00 pm. There will be a giveaway of $250 cash at the event for the person who refers the most friends to sign up! Enter here. Click here to register! A special THANK YOU to this month's Happy Hour sponsors: Hortonworks and ReactWell Hortonworks is a leading innovator in the industry, creating, distributing and supporting enterprise-ready open data platforms and modern data applications. Their mission is to manage the world's data. They have a single-minded focus on driving innovation in open source communities such as Apache Hadoop, NiFi, and Spark. ReactWell provides advanced technology services and products for organizations in the energy, chemical, oil and gas and petrochemical industry, doing business inside and out of the United States of America. ReactWell achieves material quality results for their clients by blending creative solutions, constrained by the laws of hard science and scarce resources. Enter to Win! To get your hands on one of these awesome offshore bags, all you have to do is enter! Follow the link below and select Oil and Gas HSE and enter your information. We pick one lucky winner each week. Click Here to Enter More Information To find out more about Exxonmobil, you can find them at http://corporate.exxonmobil.com/ Connect with Exxonmobil on LinkedIn. Like Exxonmobil on Facebook. Check out some of Exxonmobil's videos on YouTube. Follow Exxonmobil on Twitter @exxonmobil Connect with Steven Riddle on Linkedin. Leave a Review Help your oil and gas peers find the Oil and Gas HSE Podcast by ...

Ross Brand with your Livestream Universe Update and Flash Briefing for Wednesday, May 30th, 2018. Today's theme is tech and business as Summit Live holds the first of four one-day events. This one is in San Francisco with upcoming summits in LA, New York and Las Vegas. You can view the livestream of today's sessions at Summit.Live. Speakers include Eddie Vaca of AmpLive, Ryan Bell of NASA, Rudy J Ellis of Switchboard Live, Nick Mattingly and Marc Gawith of Switcher Studio, Dave Basulto of iOgrapher, and Rebecca Stone of LiveRamp. Plus speakers and panelists from Microsoft, Salesforce, BrandLive, Hortonworks, Eventbrite and Epiphan Video. Congratulations to Dave Jackson of the School of Podcasting on obtaining industry immortality. Dave will be inducted into the Academy of Podcasters Hall of Fame at an award's ceremony in Philadelphia on July 24th. Dave livestreams his weekly Ask The Podcast Coach show on YouTube Live. On the viewing schedule: At 2pm ET, it's LegalHour.Live with Mitch Jackson and Joey Vitale. They'll be discussing workplace arbitration agreements in light of the recent Supreme Court decision. That's on the LegalHour.Live Facebook page. At 3pm ET, it's Camera Confidence Live with Molly Mahoney. She'll share 3 keys to kicking your business into high gear. That's on the BeLive TV Facebook page. And if you didn't catch our Best of BeLive discussion on how to grow your business with livestreaming, the replay is available on the BeLive TV Facebook page. Show & event links at our website. For LivestreamUniverse.com, I'm Ross Brand. Have a great day! The post https://livestreamuniverse.com/summit-live-tackles-tech-and-business-dave-jackson-to-enter-podcasting-hall-of-fame/ (Summit.Live Tackles Tech and Business; Dave Jackson to Enter Podcasting Hall of Fame (Update Ep77)) appeared first on https://livestreamuniverse.com/ (Livestream Universe).

We take for granted that we can track and measure every piece of data. Big data guru Bob Page takes us back to a time before enterprise class web analytics were a thing. Bob shares his experience founding analytics firm Accrue, owning analytics at Yahoo & eBay, and building Hortonworks. Bob talks about working with large data sets, how the Search team at Yahoo developed Hadoop, and how he used data to support decision making at Yahoo and eBay, including an ROI model to calculate data program investments. Learn more about your ad choices. Visit megaphone.fm/adchoices See acast.com/privacy for privacy and opt-out information.

The buzz: Crystal ball for 2017. If your #1 business wish this holiday is to know what 2017 holds for your company, your industry and the world, we've got the next best thing. We're bringing you more than 70 thought leaders' insightful predictions for the technologies, strategies, and trends that can help you grow and compete in 2017 and beyond. Pour a cup of Joe, Earl, or Dom, and join us for SAP Game-Changers Radio 2017 Predictions – Part 2 live. And tune in Jan. 4, 11, and 18 for the rest of this special feature. Our featured guests: Timo Elliott, SAP; Nance L. Schick, Esq.; Mal Poulin, ANCILE; Bryan Hicks, SAP; Jerry Silva, IDC; Sanjay Kumar, Hortonworks; Nicole Sahin, Globalization Partners; Jim Fields, SAP; Jeff Hattendorf, Macrospect; Sandi Webster, Consultants 2 Go; Sudha Jamthe, IoT Disruptions; Richard McCammon, Delego Software; Heather Ashton, IDC; Fabiana Lacerca-Allen, Ethiprax; Pieter van Schalkwyk, XMPro; John Sullivan, SAP. Happy holidays from SAP Game-Changers Radio!

The buzz: Crystal ball for 2017. If your #1 business wish this holiday is to know what 2017 holds for your company, your industry and the world, we've got the next best thing. We're bringing you more than 70 thought leaders' insightful predictions for the technologies, strategies, and trends that can help you grow and compete in 2017 and beyond. Pour a cup of Joe, Earl, or Dom, and join us for SAP Game-Changers Radio 2017 Predictions – Part 2 live. And tune in Jan. 4, 11, and 18 for the rest of this special feature. Our featured guests: Timo Elliott, SAP; Nance L. Schick, Esq.; Mal Poulin, ANCILE; Bryan Hicks, SAP; Jerry Silva, IDC; Sanjay Kumar, Hortonworks; Nicole Sahin, Globalization Partners; Jim Fields, SAP; Jeff Hattendorf, Macrospect; Sandi Webster, Consultants 2 Go; Sudha Jamthe, IoT Disruptions; Richard McCammon, Delego Software; Heather Ashton, IDC; Fabiana Lacerca-Allen, Ethiprax; Pieter van Schalkwyk, XMPro; John Sullivan, SAP. Happy holidays from SAP Game-Changers Radio!

The buzz: Crystal ball for 2017. If your #1 business wish this holiday is to know what 2017 holds for your company, your industry and the world, we've got the next best thing. We're bringing you more than 70 thought leaders' insightful predictions for the technologies, strategies, and trends that can help you grow and compete in 2017 and beyond. Pour a cup of Joe, Earl, or Dom, and join us for SAP Game-Changers Radio 2017 Predictions – Part 2 live. And tune in Jan. 4, 11, and 18 for the rest of this special feature. Our featured guests: Timo Elliott, SAP; Nance L. Schick, Esq.; Mal Poulin, ANCILE; Bryan Hicks, SAP; Jerry Silva, IDC; Sanjay Kumar, Hortonworks; Nicole Sahin, Globalization Partners; Jim Fields, SAP; Jeff Hattendorf, Macrospect; Sandi Webster, Consultants 2 Go; Sudha Jamthe, IoT Disruptions; Richard McCammon, Delego Software; Heather Ashton, IDC; Fabiana Lacerca-Allen, Ethiprax; Pieter van Schalkwyk, XMPro; John Sullivan, SAP. Happy holidays from SAP Game-Changers Radio!

The buzz: Crystal ball for 2017. If your #1 business wish this holiday is to know what 2017 holds for your company, your industry and the world, we've got the next best thing. We're bringing you more than 70 thought leaders' insightful predictions for the technologies, strategies, and trends that can help you grow and compete in 2017 and beyond. Pour a cup of Joe, Earl, or Dom, and join us for SAP Game-Changers Radio 2017 Predictions – Part 2 live. And tune in Jan. 4, 11, and 18 for the rest of this special feature. Our featured guests: Timo Elliott, SAP; Nance L. Schick, Esq.; Mal Poulin, ANCILE; Bryan Hicks, SAP; Jerry Silva, IDC; Sanjay Kumar, Hortonworks; Nicole Sahin, Globalization Partners; Jim Fields, SAP; Jeff Hattendorf, Macrospect; Sandi Webster, Consultants 2 Go; Sudha Jamthe, IoT Disruptions; Richard McCammon, Delego Software; Heather Ashton, IDC; Fabiana Lacerca-Allen, Ethiprax; Pieter van Schalkwyk, XMPro; John Sullivan, SAP. Happy holidays from SAP Game-Changers Radio!

In this episode we'll go into more depth on NiFi complete with our second interview with Joe Witt, Senior Director of Engineering at Hortonworks who dives into how NiFi works under the covers and some considerations to think about when using it for real. 00:00 Recent events New logo for the podcast Hadoop use in telecom Spark masterclass details Apache Nifi "Hype Train" concerns 09:14 Main Topic Second interview with Joe Witt: a deeper dive on Apache NiFi 35:30 Questions from our Listeners: I have already implemented some of my ingest in flume/kafka/storm, do I need to replace that with NiFi? Is it true there is no chance of data loss with NiFi? Can I aggregate or combine data as part of the flow process? Do I need a hadoop cluster to use NiFi? 47:18 End Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.

In this episode we'll cover some an introduction to NiFi complete with an interview with Joe Witt, Senior Director of Engineering at Hortonworks who explains exactly where NiFi came from and how it fits into your Big Data plans. 00:00 Recent events The usual "Start of the Year" meetings and events Using Apache NiFi as a self documenting deployment system We are now available on iTunes 04:50 Main Topic Interview with Joe Witt, one of the creators of Apache NiFi and currently Director of Engineering for HDF at Hortonworks. 22:40 Questions from our Listeners: Is NiFi really as easy to use as it looks? Is NiFi a part of Hadoop now? >How do I get started with NiFi? Is NiFi an ETL tool? 30:45 End Please use the Contact Form on this blog or our twitter feed to send us your questions, or to suggest future episode topics you would like us to cover.