Podcast appearances and mentions of joe reis

Some people speculate that AI will make software and data engineers obsolete. If the only thing engineers do is write code, sure. But we do a lot more than that, and I believe we'll actually need more engineers, not fewer.In this episode, I discuss how I think AI will change the craft of software and data engineering. Spoiler - I think it will make it way more fun and productive.------------------------------Thanks to dbt and GoodData for sponsoring this episode. Please support them, as they're awesome.dbt Launch ShowcaseJoin dbt Labs May 28 for the dbt Launch Showcase to hear from executives and product leaders about the latest features landing in dbt. See firsthand how features will empower data practitioners and organizations in the age of AI.---------GoodData WebinarAnalytics and data engineering used to live in separate worlds—different teams, different tools, different goals. But the lines are blurring fast. As modern data products demand speed, scale, and seamless integration, the best teams are embracing engineering principles and best practices.In this no-BS conversation, Ryan Dolley, Matt Housley, and Joe Reis, dive into how engineering principles are transforming the way analytics is built, delivered, and scaled.

I've spoken all over the world, at the biggest events, the most intimately small, and everywhere in between. In this Practical Data Community lunch and learn, I share some experiences with public speaking, give some advice on getting your first public speaking gig, how to prepare, preparing talks, dealing with stage fright, getting paid, and much more. Enjoy!From the Practical Data Lunch and Learn, May 9, 2025

In this episode of R Weekly Highlights: We have a six-month follow-up perspective from an early Positron user, how the current landscape of AI tools perform when learning the ropes with the Tidyverse, and how you can create your first Observable plot while using R for data munging.Episode LinksThis week's curator: Jon Carroll - @jonocarroll@fosstodon.org (Mastodon) & @jonocarroll.fosstodon.org.ap.brid.gy (Bluesky) & @carroll_jono (X/Twitter)Positron: current joys and painsLearning the tidyverse with the help of AI toolsObservable for R usersEntire issue available at rweekly.org/2025-W15Supplement ResourcesPositron +1e https://open-vsx.org/extension/grrrck/positron-plus-1-eVanishing Gradients episode 47 (The Great Pacific Garbage Patch of Code Slop with Joe Reis) https://vanishinggradients.fireside.fm/47Observable color palette viewer https://observablehq.com/plot/features/scales#color-scalesObservable Plots (R/Pharma 2024 Workshop Series) https://www.youtube.com/watch?v=M6fP68XnacMSupporting the showUse the contact page at https://serve.podhome.fm/custompage/r-weekly-highlights/contact to send us your feedbackR-Weekly Highlights on the Podcastindex.org - You can send a boost into the show directly in the Podcast Index. First, top-up with Alby, and then head over to the R-Weekly Highlights podcast entry on the index.A new way to think about value: https://value4value.infoGet in touch with us on social mediaEric Nantz: @rpodcast@podcastindex.social (Mastodon), @rpodcast.bsky.social (BlueSky) and @theRcast (X/Twitter)Mike Thomas: @mike_thomas@fosstodon.org (Mastodon), @mike-thomas.bsky.social (BlueSky), and @mike_ketchbrook (X/Twitter) Music credits powered by OCRemixSunny Side Up - Yoshi's Island DS - ZackParrish - https://ocremix.org/remix/OCR04558Costa Del Sol DANCE - Final Fantasy VII - Posu Yan - https://ocremix.org/remix/OCR00095

What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed? In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software development lifecycle — from experimentation and prototyping to deployment, maintainability, and everything in between. Joe is the co-author of Fundamentals of Data Engineering and a longtime voice on the systems side of modern software. He's also one of the sharpest critics of “vibe coding” — the emerging pattern of writing software by feel, with heavy reliance on LLMs and little regard for structure or quality. We dive into: • Why “vibe coding” is more than a meme — and what it says about how we build today • How AI tools expand the surface area of software creation — for better and worse • What happens to technical debt, testing, and security when generation outpaces understanding • The changing definition of “production” in a world of ephemeral, internal, or just-good-enough tools • How AI is flattening the learning curve — and threatening the talent pipeline • Joe's view on what real craftsmanship means in an age of disposable code This conversation isn't about doom, and it's not about hype. It's about mapping the real, messy terrain of what it means to build software today — and how to do it with care. LINKS * Joe's Practical Data Modeling Newsletter on Substack (https://practicaldatamodeling.substack.com/) * Joe's Practical Data Modeling Server on Discord (https://discord.gg/HhSZVvWDBb) * Vanishing Gradients YouTube Channel (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) * Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

In this season premiere of The Data Chief podcast, your host Cindi Howson sits down with three industry visionaries to explore the trends, predictions, and must-take actions for data leaders in 2025. Get ready for a deep dive into: The generative AI revolution with Matt Turck, Partner at FirstMark CapitalThe future of data science and genAI with Steve Nouri, Founder of GenAI Works and AI for DiversityData Engineering in the Age of AI with Joe Reis, author of "Fundamentals of Data Engineering" and the upcoming "Mixed Model Arts."Plus: Hear their fun predictions for everything from sports to space travel!Key Moments:The generative AI revolution: Matt Turck, Partner at FirstMark Capital shares his insights on the evolving AI landscape, the rise of unstructured data, and why now is the time for enterprises to embrace AI. (1:40) The Future of Data Science: Steve Nouri, Founder of GenAI Works (an 8-million-strong community!) and AI for Diversity, discusses the impact of GenAI on data science roles, the ethical considerations of AI, and exciting trends like embodied AI and agentic AI. (29:36) Data Engineering in the Age of AI: Joe Reis, author of "Fundamentals of Data Engineering" and the upcoming "Mixed Model Arts," provides his expert perspective on the importance of data modeling, the need for upskilling in data teams, and the potential for a universal semantic layer. (1:00:00) Key Quotes:“I would predict that there's going to be a number of big acquisitions in our general space in 2025. This whole tension between the public markets doing very well, especially in tech, but the private markets still recovering - I think lends itself well to a wave of consolidation.”  - Matt Turck“Anything that requires democratization, I'm a big fan of. And certainly, the ability to query natural language databases and all things, making that available to everyone is a very powerful idea. You guys at ThoughtSpot know this better than anyone.” - Matt Turck“We are seeing people doing less coding, more relying on their co-pilots. It's going to evolve to become more and more robust. So we will be relying more on AI to do the coding.” - Steve Nouri“Well, that's what, you know, the tagline is, AI will do everything for you. It'll even do your laundry, the jobs that we don't like. And so you're actually saying you see a future where that actually is not too far off.” - Steve Nouri“I think that there's definitely a FOMO and a bit of a prisoner's dilemma problem with adopting AI in the organization because they're getting a lot of pressure from the top down, especially to do AI. Understanding what that means to your organization should be table stakes.” - Joe Reis“Learning never stops, investment never stops. And the best investment you can make is always improving yourself, no matter what that looks like.” Joe ReisMentions:FirstMark MAD Landscape 2024The MAD Podcast with Matt TurckAI4DiversityGenAI.WorksFundamentals of Data EngineeringJoe Reis Substack Guest Bios:Matt Turck is a Partner at FirstMark, where he focuses primarily on early-stage enterprise investing in the US and Europe. Matt is particularly active in the data, machine learning and AI space. For the last 10+ years, he has been organizing Data Driven NYC, the largest data/AI community in the US, and publishing the MAD Landscape, an annual analysis of the data/AI industry. He also hosts the weekly MAD (ML, AI, Data) Podcast. He can be followed on X/Twitter at @mattturck.Steve Nouri is the CEO and Co-founder of GenAI Works, the largest AI community. He is a renowned AI leader and Australia's ICT Professional of the Year, has revolutionized AI perspectives while championing Responsible and inclusive AI, founding a global non-profit initiative.Joe Reis, a "recovering data scientist" with 20 years in the data industry, is the co-author of the best-selling O'Reilly book, "Fundamentals of Data Engineering." He's also the instructor for the wildly popular Data Engineering Professional Certificate on Coursera, in partnership with DeepLearning.ai and AWS.Joe's extensive experience encompasses data engineering, data architecture, machine learning, and more. He regularly keynotes major data conferences globally, advises and invests in innovative data product companies, writes at Practical Data Modeling and his personal blog, and hosts the popular data podcasts "The Monday Morning Data Chat" and "The Joe Reis Show." In his free time, Joe is dedicated to writing new books and articles, and thinking of ways to advance the data industry. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Highlights from this week's conversation include:Joe's Recent Projects and Work (0:55)Joe's New Book and Inspiration for Writing It (4:39)Challenges in Data Education (7:00)Internal Training Programs (10:02)Creative Problem Solving (17:46)Evaluating Candidates' Skills (21:18)Market Value and Career Growth (24:03)AI's Impact on Hiring (27:47)Content Production and Quality (31:56)The Evolution of AI and Data (34:00)Challenges of Automation (36:12)Convergence of Data Fields (40:26)Shortcomings of Relational Models (42:09)Inefficiencies of Poor Data Modeling (47:10)Discussion on Resource Constraints (51:50)The Role of Language Models (53:13)AI in Migration Projects (57:00)Joe's Teaser for a New Project (59:05)  Final Thoughts and Closing Remarks (1:00:07)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field. The panel features: - Ben Taylor (Jepson) (https://www.linkedin.com/in/jepsontaylor/) – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning. - Joe Reis (https://www.linkedin.com/in/josephreis/) – Co-founder of Ternary Data and author of Fundamentals of Data Engineering. - Juan Sequeda (https://www.linkedin.com/in/juansequeda/) – Principal Scientist and Head of AI Lab at Data.World, known for his expertise in knowledge graphs and the semantic web. The discussion unpacks essential topics such as: - The shift from prompt engineering to goal engineering—letting AI iterate toward well-defined objectives. - Whether generative AI is having an electricity moment or more of a blockchain trajectory. - The combinatorial power of AI to explore new solutions, drawing parallels to AlphaZero redefining strategy games. - The POC-to-production gap and why AI projects stall. - Failure modes, hallucinations, and governance risks—and how to mitigate them. - The disconnect between executive optimism and employee workload. Hugo also mentions his upcoming workshop on escaping Proof-of-Concept Purgatory, which has evolved into a Maven course "Building LLM Applications for Data Scientists and Software Engineers" launching in January (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor). Vanishing Gradient listeners can get 25% off the course (use the code VG25), with $1,000 in Modal compute credits included. A huge thanks to Dave Scharbach and the Toronto Machine Learning Society for organizing the conference and to the audience for their thoughtful questions. As we head into the new year, this conversation offers a reality check amidst the growing AI agent hype. LINKS Hugo on twitter (https://x.com/hugobowne) Hugo on LinkedIn (https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/) Vanishing Gradients on twitter (https://x.com/vanishingdata) "Building LLM Applications for Data Scientists and Software Engineers" course (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor).

Matt Housley and I have a LONG chat about working in consulting, leaving your job, AI, the job market, our thoughts on what's coming in 2025, and much more.

«Data Management is an interesting one: If it fails, what's the feedback loop?»For the Holiday Special of Season 4, we've invited the author of «Fundamentals of Data Engineering», podcast host of the «Joe Reis Show», «Mixed Model Arts» sensei, and «recovering Data Scientist» Joe Reis. Joe has been a transformative voice in the field of data engineering and beyond. He is also the author of the upcoming book with the working title "Mixed Model Arts", which redefines data modeling for the modern era.  This episode covers the evolution of data science, its early promise, and its current challenges. Joe reflects on how the role of the data scientist has been misunderstood and diluted, emphasizing the importance of data engineering as a foundational discipline. We explore why data modeling—a once-vital skill—has fallen by the wayside and why it must be revived to support today's complex data ecosystems. Joe offers insights into the nuances of real-time systems, the significance of data contracts, and the role of governance in creating accountability and fostering collaboration.  We also highlight two major book releases: Joe's "Mixed Model Arts", a guide to modernizing data modeling practices, and our host Winfried Etzel's book on federated Data Governance, which outlines practical approaches to governing data in fast-evolving decentralized organizations. Together, these works promise to provide actionable solutions to some of the most pressing challenges in data management today.  Join us for a forward-thinking conversation that challenges conventional wisdom and equips you with insights to start rethinking how data is managed, modeled, and governed in your organization.Some key takeaways:Make Data Management tangibleData management is not clear enough to be understood, to have feedback loops, to ensure responsibility to understand what good looks like.Because Data Management is not always clear enough, there is a pressure to make it more tangible.That pressure is also applied to Data Governance, through new roles like Data Governance Engineers, DataGovOps, etc.These roles mash enforcing policies with designing policies.Data ContractsShift Left in Data needs to be understood more clearly, towards a closer understanding and collaboration with source systems.Data Contracts are necessary, but it's no different from interface files in software. It's about understanding behavior and expectations.Data Contracts are not only about controlling, but also about making issues visible.Data GovernanceThink of Data Governance as political parties. Some might be liberal, some more conservative.We need to make Data Governance lean, integrated and collaborative, while at the same time ensuring oversight and accountability.People need a reason to care about governance rules and held accountable.If not Data Governance «(...) ends up being that committee of waste.»The current way Data Governance is done doesn't work. It needs a new look.Enforcing rules, that people don't se ant connection to or ownership within are deemed to fail.We need to view ownership from two perspectives - a legal and a business perspective. They are different.Data ModelingBusiness processes, domains and standards are some of the building blocks for data.Data Modeling should be an intentional act, not something you do on the side.The literature on Data Modeling is old, we are stuck in a table-centric view of the world.

This is a special episode of The Data Engineering Show, and joining the Bros is not one guest, nor even two – instead they're revisiting the best bits from three different fascinating episodes. In each, they spotlight essential trends and lessons learned across the evolving data engineering landscape. From data observability to bridging academia with real-world practice, this episode covers perspectives on where data engineering is heading and why certain challenges persist.

Join us as we sit down with Joe Reis, live at Big Data LDN (London) 2024. Joe shares his partnership with DeepLearning.ai and AWS through his new course on Data Engineering. Joe's new course promises to elevate your data skills with hands-on exercises that marry foundational knowledge with cutting-edge practices. We dive into how this course complements his seminal book, "Fundamentals of Data Engineering," and why certification is valuable for those looking for foundational, hands-on knowledge to be a data practitioner. But that's not all; we also dissect the hurdles of adopting modern data architectures like data mesh in traditionally siloed companies. Using Conway's Law as a lens, Joe discuss why businesses struggle to transition from outdated infrastructures to decentralized systems and how cross-disciplinary skills—a concept inspired by mixed martial arts—are crucial in this endeavor as he cleverly calls it 'Mixed Model Arts'. Check out Joe's Work: Fundamentals of Data Engineering bookNew Coursera courses by Joe ReisWhat's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

We're back from our Summer break and ready to rant about all kinds of stuff. We talk about the tech downturn, AI hype bubbles, and much more.

Had a great chat with Mark Balkenende, VP of Product Marketing at Matillion and Joe Reis, Thought Leader in Data Engineering, at the Snowflake Summit. This year's Deep Dish Data Agenda is packed with incredible insights and interviews. Mark shared more about the exciting customer stories and interviews. We also discussed the current state of Data Engineering and its future. Stay tuned for more updates and insights from the Snowflake Summit! #data #ai #snowflakesummit #snowflakeflake2024 #matillion #theravitshow

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview hereSol Rashidi - Author of "Your AI Survival Guide", Keynote Speaker & "Forbes AI Maverick & Visionary of the 21st Century"Joe Reis - Co-Author of "Fundamentals of Data Engineering", CEO at Ternary Data, Keynote Speaker, Professor & PodcasterRESOURCESSolhttps://twitter.com/Sol_Rashidihttps://www.linkedin.com/in/sol-rashidi-a672291https://www.solrashidi.comhttps://solrashidi.substack.comJoehttps://www.linkedin.com/in/josephreishttps://github.com/JoeReishttps://joereis.substack.comhttp://josephreis.comLinkshttps://learning.oreilly.com/get-learning/?code=LEARNGIT23Trisha & Helen: https://youtu.be/pfinplXtrkADESCRIPTIONJoin Sol Rashidi, an AI deployment pioneer, offers practical wisdom on navigating AI's challenges in a conversation with Joe Reis. Her new book, "Your AI Survival Guide," provides actionable insights for individuals and businesses venturing into AI integration, emphasizing hands-on learning and a measured approach to address cost and sustainability concerns.RECOMMENDED BOOKSSol Rashidi • Your AI Survival GuideDavid Foster • Generative Deep LearningPhil Winder • Reinforcement LearningMustafa Suleyman • The Coming WaveAshley Peacock • Creating Software with Modern Diagramming TechniquesTwitterInstagramLinkedInFacebookLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Join Shane Gibson as he chats with Joe Reis on how the potential adoption of GenAI and LLM's in the way Data teams work. You can get in touch with Joe via LinkedIn or at https://joereis.substack.com/ If you want to read the transcript for the podcast head over to: https://agiledata.io/podcast/agiledata-podcast/ai-data-agents-with-joe-reis/#read Listen to more podcasts on applying AgileData patterns over at https://agiledata.io/podcasts/ Read more on the AgileData Way of Working over at https://wow.agiledata.io/way-of-working/   If you want to join us on the next podcast, get in touch over at https://agiledata.io/podcasts/#contact   Or if you just want to talk about making magic happen with agile and data you can connect with Shane @shagility or Nigel @nigelvining on LinkedIn.   Subscribe: Apple Podcast | Spotify | Google Podcast  | Amazon Audible | TuneIn | iHeartRadio | PlayerFM | Listen Notes | Podchaser |  Deezer | Podcast Addict |    Simply Magical Data

Today, we have Joe Reis on the show. Joe is the co author of the book, Fundamentals of Data Engineering, probably the best and most comprehensive book on data engineering you could think to read. We talk about the culture of Data Engineering, Relationship with Data Science, the downside of chasing bleeding edge technology in approaches to Data Modeling. Joe's got lots to say, lots of opinions and is super knowledgeable. So even if Data Engineering, Data Science isn't your thing. We think you're still going to really enjoy listening to the interview.

Joe Reis and Matt Housley are back for another listener Q&A. They chat about the demise of the Modern Data Stack, architecture, data modeling, AI, and much more.

It is always good to meet friends in person and chat about data. Check out my interview with Joe Reis

This week on The Changelog we're talking with Joe Reis about data engineering and the beginning of generative AI. We discuss, phone hacking via frequency, the role of a data engineer, this AI hype cycle we're in, build vs buy, the disconnect between data analysts and the business, ethical considerations around AI-generated content, and more. We also discuss the tension between AI and traditional engineering, as well as the inevitability of AI integration into pretty much everything.

This week on The Changelog we're talking with Joe Reis about data engineering and the beginning of generative AI. We discuss phone hacking via frequency, the role of a data engineer, this AI hype cycle we're in, build vs buy, the disconnect between data analysts and the business, ethical considerations around AI-generated content, and more. We also discuss the tension between AI and traditional engineering, as well as the inevitability of AI integration into pretty much everything.

Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! Join us for part two of our conversation with Joe Reis, host of 'Monday Morning Data Engineering' and co-author of 'Data Engineering Fundamentals'. In this episode, we continue our exploration of the evolution of data engineering and the shift towards real-time analytics. We discuss the fine line between streaming and real-time processing, the transition from ETL to data engineering, and the significance of immediate data processing in user interactions.

We dive into the intricate world of data engineering with none other than Joe Reis, a celebrated figure in the tech education and data engineering arena. Joe, currently wearing multiple hats as an Instructor at DeepLearning.AI, CEO of Ternary Data, author with O'Reilly Media, and an adjunct professor at the University of Utah, brings a wealth of knowledge and experience to our table. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! In this week's episode, Tim chats with Joe Reis, a seasoned expert in data engineering and co-author of "Fundamentals of Data Engineering." They delve into the evolution of data engineering from ETL, the role of real-time data and analytics, and the future trajectory of the field. Joe also shares his diverse experiences, from being a 'recovering data scientist' to his current focus as a content creator and consultant.► The Fundamentals of Data Engineering by Joe Reis and Matt Housley: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/► The Joe Reis Show: https://open.spotify.com/show/3mcKitYGS4VMG2eHd2PfDN► Monday Morning Data Chat: https://podcasts.apple.com/us/podcast/monday-morning-data-chat/id1565154727

It's Joe and Matt today, taking listener questions and ranting about whatever's on their minds.

In this podcast episode, host Ole Olesen-Bagneux welcomes Joe Reis, a distinguished data science and engineering expert, CEO of Ternary Data, and co-author of "Fundamentals of Data Engineering". Joe opens by recounting his rich career spanning over two decades, from data science to machine learning, and his transition to influential author and educator.Looking back at the genesis of his best-selling book, Joe outlines his intent to bridge the educational gap in data engineering, the challenges he faced in its publication, and the importance of effective book promotion. Joe also introduces his forthcoming work, "Practical Data Modeling", which advocates for a 'Mixed Model Arts' approach akin to mixed martial arts, essential for navigating today's complex data landscape.The episode wraps up with a discussion on the current state and future of AI and data modeling, where Joe offers his candid perspectives on the hype and reality of AI, underscoring the need for a balanced view that recognizes AI's potential without falling prey to over-enthusiasm.

Matt Housley and Joe Reis chat about where data engineering is going, and take audience questions.

Episode OverviewData Engineers play a critical role in enabling the success of any data and analytics function. In this episode of the CDO Matters Podcast, Malcolm interviews Joe Reis, the co-author of the bestselling O'Reilly book, “Fundamentals of Data Engineering”. In the discussion, Joe gives a sneak peek into the minds of data engineers and what makes them tick. Data leaders, particularly those with less technical backgrounds, should find this discussion a helpful tool to improve their relationships with the people critical to the success of their data and analytics operation.Episode Links and Resources:Follow Malcolm Hawker on LinkedIn Follow Joe Reis on LinkedIn

The data job market is certainly evolving. Matt, Chris, and Joe have a candid chat and AMA about career advice going into 2024.

After co-writing the best-selling book ‘Fundamentals of Data Engineering', Joe Reis and Matt Housely joined the bros for some much-needed ranting, priceless data advice, and good laughs. So why are we still talking about providing business value and dashboards, even though we don't really have anything new to say? If there are so many great tools in the data stack, why are we still so troubled? How can we focus more on things like data governance and data quality that'll actually push the industry forward?

After co-writing the best-selling book ‘Fundamentals of Data Engineering', Joe Reis and Matt Housely joined the bros for some much-needed ranting, priceless data advice, and good laughs. So why are we still talking about providing business value and dashboards, even though we don't really have anything new to say? If there are so many great tools in the data stack, why are we still so troubled? How can we focus more on things like data governance and data quality that'll actually push the industry forward?

In this episode, Joe Reis, best-selling author of Fundamentals of Data Engineering explains why data modeling and semantics need to take the center stage for AI to truly succeed.

There's a lot of debate on big and small data. For systems and compute, some say "Big Data is Dead", while others challenge this notion. In AI and ML, big tech companies can pour tons of money and data into building massive LLMs, while open source provides compelling "small data" alternatives to the LLM walled gardens.So which is it? Will Big Data reign supreme or will small data become more popular? Matt and I riff on these topics and more.#data #dataengineering #chatgpt #ai #bigdata

I recap the Joe Reis + dbt roadshow in Denver (thanks to everyone who showed up) and discuss the divide between IT and "The Business." ----------------------- If you like this show, give it a 5-star rating on your favorite podcast platform. Purchase Fundamentals of Data Engineering at your favorite bookseller. Check out my substack: https://joereis.substack.com/

Join Shane Gibson as he chats with Joe Reis on his experience in building and running a successful data and analytics consulting company. You can get in touch with Joe on LinkedIn   If you want to read the transcript for the podcast head over to: https://agiledata.io/podcast/agiledata-podcast/data-consulting-patterns-with-joe-reis/#transcript Listen to more podcasts on applying AgileData patterns over at https://agiledata.io/podcasts/ Read more on the AgileData Way of Working over at https://wow.agiledata.io/way-of-working/     If you want to join us on the next podcast, get in touch over at https://agiledata.io/podcasts/#contact   Or if you just want to talk about making magic happen with agile and data you can connect with Shane @shagility or Nigel @nigelvining on LinkedIn.   Subscribe: Apple Podcast | Spotify | Google Podcast  | Amazon Audible | TuneIn | iHeartRadio | PlayerFM | Listen Notes | Podchaser |    Simply Magical Data

Joe Reis and Matt Housley rant about generative AI, Data Council, WASM, data modeling, regulations, and how programming languages and paradigms impact the business. #data #dataengineering #datamodeling #chatgpt

Mohamed Elsherif and Mohamed Hassan, will be leading the discussion and posing thought-provoking questions to Joe Reis. Get ready to be inspired and learn from the best in the business! Whether you're a seasoned data engineer or just starting out on your journey, this is an opportunity to gain invaluable insights and knowledge from someone who has truly made an impact in the field. You'll walk away with a newfound appreciation for the art and science of data engineering and a renewed sense of purpose and inspiration. So, what are you waiting for? RSVP now and secure your spot for this exciting event! We can't wait to see you there. For more information about Joe Reis, please visit https://josephreis.com/

This week, we are having a throwback elixir with some very special surprise guests.Tune in with hosts Juan and Tim to find out who.Key Takeaways: [00:26 - 01:51] Episode intro: Data Day and What keeps you up at night in data?[02:04 - 02:54] Joe Reis, the economy and showing value[02:59 - 03:30] Omar Khawaja: Anything that ends with board, data dashboards[03:54 - 04:05] Vip Parmar: Jet lag & underutilized data[04:16 - 04:51] Laura Ellis: Data governance and change management[04:59 - 06:18] Mohammed Syed: Change management and data solutions[06:22 - 06:49] Tim: Determining focus in a sea of technology opportunities[06:50 - 07:58] Juan: Disconnect between executives and how data teams work with the business

Joe and Matt discuss getting Fundamentals of Data Engineering signed with O'Reilly, the writing process, and marketing a best-selling tech book. This is worth a listen if you've ever wanted to write a technical book. #dataengineering #data #author #oreilly

Joe and Matt are two pioneers in the data engineering space. They recently published the book: The Fundamentals of Data Engineering which is now a best seller, and have worked together at Ternary data consulting in the space for the past 5 years. In this episode we learn about the future of data engineering, the scope of new AI models, and about how they make career transitions at this stage in their careers. Their Book: https://amzn.to/3IW9XeZJoe's Linkedin: https://www.linkedin.com/in/josephreis/Matt's Linkedin: https://www.linkedin.com/in/housleymatthew/

MLOps Coffee Sessions #141 with Stephen Bailey, Airflow Sucks for MLOps co-hosted by Joe Reis. // Abstract Stephen discusses his experience working with data platforms, particularly the challenges of training and sharing knowledge among different stakeholders. This talk highlights the importance of having clear priorities and a sense of practicality and mentions the use of modular job design and data classification to make it easier for end users to understand which data to use. Stephen also mentions the importance of being able to move quickly and not getting bogged down in the quest for perfection. We recommend Stephen's blog post "Airflow's Problem" for further reading. // Bio Stephen has worked as a data scientist, analyst, manager, and engineer, and loves all the domains equally. He currently works at Whatnot, a collectibles marketplace that focuses on live shopping, and has previously worked in privacy tech at Immuta. He has his Ph.D. from Vanderbilt University in educational cognitive neuroscience, but it has yet to help him understand why his three children are so crazy. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Airflow's Problem blog post: https://stkbailey.substack.com/p/airflows-problem Airflow's Problem and the reception it got on Hacker News: https://news.ycombinator.com/item?id=32317558 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Joe on LinkedIn: https://www.linkedin.com/in/josephreis/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stkbailey/ Timestamps: [00:00] Stephen's preferred coffee [00:19] Introduction to co-host Joe Reis [01:40] Takeaways [06:29] Subscribe to our newsletters! [06:55] Shout out to our sponsor, Wallaroo! [08:05] Whatnot [10:47] Stephen's side hustle [14:35] Stephen's work breakdown at Whatnot [18:03] Fundamental tensions in the data world [21:27] Initial questions to answer that you were on the right path [24:06] Recommender systems [28:15] Coordinating with ML teams [29:43] Daxter [31:38] Too advanced, more challenging [34:37] Orchestration layer [36:14] Decision criteria [39:23] Human design aspect of Daxter [40:53] Orchestration layer centralization and sharing knowledge with stakeholders [46:18] Airflow's Problem and the reception it got on Hacker News [51:00] Wrap up

Welcome to our worst episode ever! But also pretty fun. There's a lot going on in the data and tech space right now. Matt and Joe kick it old school and dive into a variety of topics ranging from all things downturn, cloud vs on-prem, what we look for in guests, and much more. We had to cut the show a bit short because Joe kept getting interrupted by delivery people. #dataengineering #data #tech

Joe Reis and Matthew Housley of Ternary Data join us to talk about building loosely coupled systems, data contracts, and avoiding data hell. They also elaborate on some of our favorite passages from their incredible book:  Fundamentals of Data Engineering. We also muse over future-facing trends such as data streaming becoming the future ETL (ETL 2.0). Joe and Matt break down their expert perspectives on this episode of What's New in Data with host John Kutay. What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

In this "reverse interview", Keith McCormick turns the tables and interviews Joe Reis and Matt Housley about their book, Fundamentals of Data Engineering, along with questions data scientists might have about data engineering. Keith's LinkedIn: https://www.linkedin.com/in/keithmccormick/ #datascience #dataengineering #data

What is the state of Data engineering today and where is it going (or should it be going)? Who better to talk about Data Engineering than the authors of the recent O'Reilly book “Fundamentals of Data Engineering”, Joe Reis and Matt Housley from Ternary Data. Join Tim, Juan, Joe and Matt to discuss the state of data engineering.

Joe Reis and Matt Housley chat about local data scenes in a post-COVID world, whether there's a meetup winter, and much more. Special cameo appearance by Chris Tabb. #dataengineering #data #meetup #technology --------------------------------- TERNARY DATA We are Matt and Joe, and we're "recovering data scientists". Together, we run a data architecture company called Ternary Data. Ternary Data is not your typical data consultancy. Get no-nonsense, no BS data engineering strategy, coaching, and advice. Trusted by great companies, both huge and small. Check out Fundamentals of Data Engineering (O'Reilly): https://amzn.to/3bftdoQ Subscribe to our newsletter, or check out our services at Ternary Data Site - https://ternarydata.com Please follow our LinkedIn page - https://www.linkedin.com/company/ternary-data/ Subscribe to our YouTube and smash the like button! - https://www.youtube.com/channel/UC3H60XHMp6BrUzR5eUZDyZg Thanks for your support!

Joe Reis, CEO of Ternary Data and Matthew Housley, CTO of Ternary Data are joining us on this insightful episode to talk about the Fundamentals of Data Engineering.

Tune in as Joe Reis and Matt Housley, co-founders of Ternary Data and co-authors of the book “Fundamentals of Data Engineering” join Jon Krohn to discuss major undercurrents across the data engineering lifecycle, and their top tools and techniques. In this episode you will learn: • What is data engineering? [3:55] • Why Joe and Matt identify as “recovering data scientists” [6:12] • What kinds of people tend to become data scientists vs. data engineers [10:38]? • Key components of Joe and Matt's book [26:31] • Major undercurrents across the data engineering lifecycle [28:26] • The most under-utilized tool in a data engineer's toolbox [34:39] • How there are tradeoffs in any data pipeline latency considerations, but faster is typically the default assumption [38:55] • Joe and Matt's favorite data engineering tools and techniques [43:39] Additional materials: www.superdatascience.com/595

Data engineering is a difficult job, requiring a large number of skills that often don't overlap. Any effort to understand how to start a career in the role has required stitching together information from a multitude of resources that might not all agree with each other. In order to provide a single reference for anyone tasked with data engineering responsibilities Joe Reis and Matt Housley took it upon themselves to write the book "Fundamentals of Data Engineering". In this episode they share their experiences researching and distilling the lessons that will be useful to data engineers now and into the future, without being tied to any specific technologies that may fade from fashion.

Data engineering is a large and growing subject, with new technologies, specializations, and "best practices" emerging at an accelerating pace. This podcast does its best to explore this fractal ecosystem, and has been at it for the past 5+ years. In this episode Joe Reis, founder of Ternary Data and co-author of "Fundamentals of Data Engineering", turns the tables and interviews the host, Tobias Macey, about his journey into podcasting, how he runs the show behind the scenes, and the other things that occupy his time.

Surprise Friday chat! It's Matt Housley and Joe Reis chatting about their new best-selling book, Fundamentals of Data Engineering (O'Reilly 2022). #dataengineering #fundamentalsofdataengineering #data O'Reilly: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/ Amazon: https://www.amazon.com/Fundamentals-Data-Engineering-Robust-Systems-dp-1098108302/dp/1098108302

https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) Transcript for this episode (https://docs.google.com/document/d/1CrNt8qo72qGtU1dOdz4PMDVMfqeIZOAP9v5dFzpSKcI/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?datameshradio (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?datameshradio (here) In this episode, Scott interviewed Joe Reis, CEO/Co-Founder of data consultancy Ternary Data, Co-Host of the Monday Morning Data Chat, and author of the upcoming book Fundamentals of Data Engineering. Some key points or takeaways specifically from Joe's point of view (not necessarily those of the podcast): Find quick, high-value wins. Too often people focus on the big wins and those become overly complicated and end up in failure. Most software engineers don't understand data well enough to be data product developers in data mesh, at least yet. Data mesh is a polarizing topic. And that makes sense as it is pushing boundaries. Many hope it can come to fruition but it is a bit of a utopian view. The future of data engineering is to move past managing pipelines to much higher-value work. Speed to achieving wins with data - with a clear return on investment and trust - is the first thing you should focus on. Get this right and you can have the "luxury" of building great data products. Joe started by discussing the kind of nebulous area within software engineering and data that data engineering has always played - sit between the source systems and the data output, converting the data in the source systems into something consumable for data users. Previously, that was mostly about making sure reports got pushed through and you hoped people derived insights. Now it's more about pipelines. But the way we store information in source systems, it is not in the format or shape we need for analytical purposes. So there needs to be a go-between. A big trend in data engineering currently for Joe is the abstraction of tooling. Some of that can be good - makes people more productive - or bad - means it's harder to understand what is actually happening under the covers. But for Joe, it's probably worth it to use the abstractions as they are able to do the heavy lifting and data engineers can focus on the higher value work. We might be coming to the end of the "pipeline monkey" era of data engineering so we can shift more focus to the data output, DataOps, orchestration, security, etc. For Joe, the biggest value-add the data engineering team can have is getting wins quickly. When asked about speed to returns versus repeatability, Joe said that the speed is more important, especially when you are trying to prove out the value of your data team. Trust is crucial, so you have to be careful to not move too fast, but trying to do big-bang projects is often a recipe for failure in his view. When asked what could be the signs an organization is ready to implement data mesh, Joe mentioned that if an organization is already seeing "wins" with data across a number of teams/domains, that's a very good sign. But you can't only have a few teams getting those wins as that means the overall organization data maturity is still probably low. Joe made a good point about how polarizing data mesh can be. When he speaks with some organizations, there are a few leaders who simply reject the idea outright. But many also simply don't see data mesh as ever being possible specifically in...

Joe Reis is a business-minded data engineer who's worked in the industry for 20 years. He is the CEO and Co-Founder of Ternary Data, a data engineering and architecture consulting firm based in Salt Lake City, Utah. He volunteers with several technology groups and teaches at the University of Utah. In his spare time, he likes to rock climb, produce electronic music, and take his kids on crazy adventures.In this episode, we cover a range of topics including:- What does a data engineer do?- Relationship between data engineering and MLOps- What are the components of the data engineering lifecycle?- What should a data engineer know at a fundamental level to be successful at this?- His book Fundamentals of Data Engineering - Why writing a book is one of the few activities that exposes your weaknesses and very deeply refines your thinking- The three pillars of a solid data foundation: data architecture, data engineering, and DataOps. - How do you structure your first call with a potential client? - Why is there a mismatch between expectations and reality when it comes to using data science within a business?- Why he puts a lot of the responsibility on the student to get good at researching problems and solutions- How to think about architecture as a data professional

MLOps Coffee Sessions #94 with Mark Freeman, Traversing the Data Maturity Spectrum: A Startup Perspective. // Abstract A lot of companies talk about having ML and being data-driven, but few are there currently and doing it well. If anything, many companies are on the cusp of implementing ML rather than being ML mature. As a startup, what decisions are we making today to drive data maturity and set us up for success when we further implement ML in the near future. What business cases are we making for leadership buy-in to invest in data infrastructure as compared to product development while we identify product-market-fit. // Bio Mark is a community health advocate turned data scientist interested in the intersection of social impact, business, and technology. His life's mission is to improve the well-being of as many people as possible through data—especially among those marginalized. Mark received his M.S. from the Stanford School of Medicine where he was trained in clinical research, experimental design, and statistics with an emphasis on observational studies. In addition, Mark is also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. He is currently a senior data scientist at Humu where he builds data tools that drive behavior change to make work better. His core responsibilities center around 1) building data products that reach Humu's end users, 2) providing product analytics for the product team, and 3) building data infrastructure and driving data maturity. // MLOps Jobs board https://mlops.pallet.xyz/jobs // Related Links Website: humu.com The Informed Company: How to Build Modern Agile Data Stacks that Drive Winning Insights book: https://www.amazon.com/Informed-Company-Cloud-Based-Explore-Understand/dp/1119748003 Fundamentals of Data Engineering book by Joe Reis and Matt Housley: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Mark on LinkedIn: https://www.linkedin.com/in/mafreeman2/ Timestamps: [00:00] Introduction to Mark Freeman [01:43] Grab your own Apron merch @ https://mlops.community/! [03:41] LinkedIn stardom! [04:40] Followers or connections? [05:31] Leveraging essential information platform [08:56] Investment in time spent on creating and working on a social platform [12:16] Put yourself out there for people to find you [16:33] Data maturity is a spectrum that takes time to traverse [23:43] Maturity of path [28:43] Fundamentals for data products [33:05] Foundational data capabilities [37:32] Value of metrics [41:48] writing reused code timeframe vs working with stakeholders timeframe [44:11] Wrap up [45:14] Look for Meetups near you!

Support the show: https://www.buymeacoffee.com/datascienceharp Find Joe online: https://www.linkedin.com/in/josephreis Joe is a business-minded data nerd who's worked in the data industry for 20 years. In his two decades as a practitioner he's worked on the full gamut of data tasks from statistical modeling, forecasting, machine learning, data engineering, data architecture, and almost everything else in between. He's taken all that experience and started his own venture and is currently the CEO of Ternary data. Watch the video of this episode: https://youtu.be/6jGmXBaTJkI Memorable Quotes from the episode: [00:42:09] "...My other piece of advice, which is, do you lose money for the firm? I'll be understanding. If you lose a shred of reputation, I will be ruthless. Let's talk with me. Right. Reputation is everything. As he also says, it takes a lifetime to build a reputation. It takes 15 minutes to destroy it. So when we started our business, I thought it was interesting. We didn't really care about the money. We cared about reputation and cared about doing great work, meeting great people and just, I think developing good relationships. I always optimizing for reputation. I think we thought if we could build that pile of reputational capital, the money would follow. The reverse is rarely true, though. In the short term, you can build as much money as you can, but you can destroy your reputation. And then who's going to want to do business with you?" Highlights of the show: [00:01:11] Guest Introduction [00:03:34] Joe, where did you grow up and what was it like there? [00:05:22] What were you like as a high school kid? What did you think your future would look like? [00:06:46] When you'd make the move over to Salt Lake City? Was that when you started working? Did you go to school there? [00:09:08] What was it like kind of when you first started out and what drew you to this kind of field (data science)? [00:14:02] Where is the science in data science? Is there any science in data science? Is it scientism? [00:26:10] How did you guys link up and decide to start ternary data and can we even get the story behind the companies name as well? [00:27:23] What are some problems that you just see as a consultant pop up over and over? [00:34:06] Do engineers add value and how should we think about a return on investment for the work that they do? [00:41:23] Talk to us about your blog post about the concept of reputational capital. [00:43:04] Do you have any tips for people who are just early in their data science career. In their first job as a data scientist, how can they accrue some of this 'reputational capital'? [00:45:56] How reading science fiction has made you a better technologist? What science fiction has done for you, has it made you a better technologist? [00:47:44] What would you say is the one sci-fi work that's had the biggest influence on you as a technologist? [00:51:04-00:51:17] You've got such a dope setup here. What's all this about? The keyboards? You got turntables, you got multiple keyboards. Are you making your music. Do you got any undercover Spotify? [00:52:59] It's 100 years in the future. What do you want to be remembered for? Random Round [00:54:02] When do you think the first video to hit to 1 trillion views on YouTube will happen? When will that happen and what will that video be about? [00:55:33] What song do you have on repeat? [00:55:53] What are you currently reading? [00:59:19] What's kind of your process when you're reading? [01:03:12] What talent would you show off in a talent show? [01:03:39] What do you mean by organizational behaviour? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Join GDM and the co-founders of Ternary Data, Joe Reis and Matt Housley, as we explore the 3 key things that data engineers need to know in 2022 (and beyond). We will explore the top technologies that are a must-know, the technical skills that can make or break the job, and concepts/methodologies that are hyper relevant to the data engineers day-to-day. Grab your coffee and join us for this slide-free discussion that can help new engineers up-level their game and seasoned engineers find renewed inspiration.   ABOUT MATT + JOE: Matt and Joe fondly consider themselves "recovering data scientists". They are the authors of Fundamentals of Data Engineering, which is being published by O'Reilly Media later this year.   ABOUT TERNARY DATA: Ternary Data is a data engineering & data architecture consulting firm led by veteran data engineers, data scientists, and college professors. Rest assured that you're dealing with people who have deep, real-world experience (and the battle scars to prove it). Ternary Data is not your typical data consultancy. Get no-nonsense, no BS data engineering strategy, coaching, and advice. Trusted by great companies, both huge and small.

Even an amazing algorithm can't fix communication problems. In this episode, Ben and Michael sit down with Joe Reis, a data scientist and ML developer who's passionate about helping people level up their communication and build solid business infrastructure. “I feel like the infrastructure piece is getting better. Once you get past the technical layer, it's about basic things like communication, no matter how much money is thrown into it.” - Joe Reis In This Episode 1) SUPER exciting trends for data science for the first time in 10+ years 2) Why you NEED to have a marketing-focused approach in the ML space nowadays 3) The BIGGEST things you should consider when hiring and scaling to keep you business infrastructure intact Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links Joe Reis ( JoeReis ) (https://github.com/JoeReis)

Joe Reis is a “recovering data scientist”, CEO of Ternary Data, and a business-minded data nerd who's worked in the data industry for 20 years, with responsibilities ranging from statistical modeling, forecasting, machine learning, data engineering, data architecture, and almost everything else in between. He's the host of the popular data show and podcast, the Monday Morning Data Chat, interviews data celebrities on The Data Nerd Herd, and runs several popular meetups, including The Utah Data Engineering Meetup and SLC Python. Joe also teaches at the University of Utah, and is the co-author of the upcoming O'Reilly book, The Fundamentals of Data Engineering. When he's not busy running a company, teaching, or creating content, Joe often finds himself rock climbing or trail running in the mountains around Salt Lake City, Utah. In this episode Joe talks about how the data domain has changed over the last 20 years. You also learn about why he considers himself a “recovering data scientist”.

Let's discuss the trends and factors about the software engineering skills a data engineer should have. Listener questions and comments are welcome! Note - Monday Morning Data Chat is moving to a purely live format. Each Monday at 9am MST, Matt and Joe will broadcast live on LinkedIn Live and YouTube Live. Please subscribe to YouTube or follow Joe Reis on LinkedIn if you'd like to be notified when the episode airs. The discussion will be available as a podcast shortly after it airs. Thanks for your support! --------------------------------- TERNARY DATA We are Matt and Joe, and we're "recovering data scientists". Together, we run a data architecture company called Ternary Data. Ternary Data is not your typical data consultancy. Get no-nonsense, no BS data engineering strategy, coaching, and advice. Trusted by great companies, both huge and small. Ternary Data Site - https://ternarydata.com LinkedIn - https://www.linkedin.com/company/ternary-data/ YouTube - https://www.youtube.com/channel/UC3H60XHMp6BrUzR5eUZDyZg

In my 26th episode I speak to recovering data scientist Joe Reis! We talk about: 1) His background and what he's up to now 2) What is a recovering data scientist? 3) Is Data Science losing it's edge? 4) What is the role of the Data Engineer and why they are needed in the data ecosystem? 5) Why data scientists are like "cross fit"? 6) What skills do the data scientists of today need to strengthen? 7) Why has the trend been to separate the the Data Engineer and the Machine Learning Engineer? 8) How do organisations that have legacy applications build better and highly efficient pipelines? 9) Why we are still seeing the same issues around Data Quality and it's affect on the Data Engineer. 10) Why the Data Engineer needs to be a diplomat. 10) What are the core skills the Data Engineer needs to be trained in? 11) The difference between the Data Engineer and the Data Architect? 12) What kind of team does an organisation need to start with in the analytics space? About Joe: Joe has labelled himself to be a "Recovering data scientist" and has firsthand experience with the challenges of succeeding with data. Data architect and data engineer. Helping companies build a solid data foundation. He is the CEO and founder of Ternary Data. About Samir Samir is a data strategy and analytics leader, CEO and Founder of datazuum. He has a history of helping data executives and leaders craft and execute their data strategies. His passion for data strategy led him to launch the Data Strategy & Analytics Coaching Programme, and is host of the Data Strategy Show. After a career in both private and public sectors Samir launched the datazuum brand in 2012, with a view to working with executives to deliver data strategy at a time when data was not seen as a business asset. Today datazuum delivers projects across both private and public sectors including: Charities, Financial Services (Banking & Insurance), Government, Housing & Construction, Law Enforcement, Logistics, Media & Publishing, Outsourcing, Postal, Retail, Telecoms, Transport and Utilities. Samir has 20 years of international experience across Europe, North America, and Africa. Is a regular speaker at international conferences, coach / mentor, a charity fundraiser, and youth champion for Working Knowledge - supporting young people to achieve their personal and career goals in life. Samir lives in London with his wife and daughter. Contact: samir@datazuum.com

We are joined by these amazing Data Scientists: Danny Ma, Joe Reis, Koo Ping Shung, and David Langer as we'll talk about all things data science. David Langer: Founder of Dave on Data and TDWI data analytics instructor Koo Ping Shung: AI & Analytics Instructor, LinkedIn Top Voices 2020, Co-Founder of AI Professionals Association, and Data Science Rex Joe Reis: Adjunct Professor at University of Utah, and CEO of Ternary Data Danny Ma: Founder & CEO of Sydney Data Science, and Data with Danny

On this week's episode of The Data Stack Show, Eric and Kostas are joined by Matthew Housley, CTO, and Joe Reis, CEO and co-founder of Ternary Data. These self-described “recovering data scientists” focus on teaching skills to build a solid foundation for organizations to work with their data.Highlights from this week's episode include:Joe and Matt's background and expertise (2:44)Common threads and trends in the data sphere (9:39)Differences and commonalities between startups and enterprises and the way they deal with data (18:28)Discussing how the role of data engineering has evolved over the years and what it might morph into in the near future (27:52)The ideal data infrastructure and what future shifts excite them (39:52)How ML is shaping the data space (44:30)The state of real time (49:56)The Data Stack Show is a weekly podcast powered by RudderStack. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

The Data Science Happy Hours Holiday edition! You don't want to miss this one. We had some of the most influential names from the LinkedIn Data Science community in attendence! Check it out and don't forget to register for future office hours: http://bit.ly/adsoh If you want to interact with me multiple times a week, join Data Science Dream Job for 70% off: http://dsdj.co/artists70 Watch the episode on YouTube here: https://www.youtube.com/playlist?list=PLx-pFwty92wJoWzoO7WlfaM7iYB8qjm We were voted one of the top ten data science podcasts by FeedSpot - check it out here: https://blog.feedspot.com/datasciencepodcasts/ Chat Transcript: http://theartistsofdatascience.fireside.fm/articles/oh14-chat-transcript SHOW NOTES [00:02:05] Work-life balance of a data scientist [00:06:27] “So, to answer your question, data science is just a regular ass job” [00:08:45] Hello to everyone who joined! [00:09:58] So many ways to learn, which one should I choose? [00:12:26] Moving from project management to data science [00:14:19] Dave share's his perspective after 20 years in the data industry [00:16:18] Tips for being more active on LinkedIn [00:17:16] Susan – The Classification Guru – shares her tips for posting on LinkedIn [00:18:45] Giovanna shares her tips on being positive on LinkedIn [00:20:27] Greg Coquillo - LinkedIn Top Voice for Data Science 2020 – shares his tips on creating content and networking on LinkedIn [00:24:06] What are your tips for debugging code? [00:24:38] Liuna – Senior Data Scientist at IBM – shares her tips for debugging code [00:25:28] Srivatsan Srinivasan shares his tips on how to debug code [00:28:35] Carlos Mercado talks about Debugging in R [00:30:21] Monica Kay Royal shares her secrets for debugging code [00:31:15] George Firican shares some tips as well! [00:32:08] Joe Reis closes out the discussion on debugging [00:37:32] A big debate on the never end Python vs R question [00:42:47] What are your expectations from someone in a junior data scientist position? [00:43:14] Sarah shares what she's looking for in a junior level candidate [00:44:42] What Srivatsan looks for in a junior data scientist [00:45:53] Monica, what do you look for in a junior data scientist? [00:46:39] What Vin Vashishta looks for in a junior data scientist? [00:47:51] Liuna what do you look for in a junior data scientist? [00:49:09] Mikiko what do you look for in a junior data scientist? [00:51:55] What does Kamrin look for in an junior data scientist? [00:53:39] How to stay motivated and re-evaluate your hustle when you're in the job search? [00:56:24] Sarah shares some tips on turning applications into interviews [00:58:19] Mikiko shares some words of encouragement as well [01:01:17] Ben shares some advice on what to so with the crazy job descriptions [01:05:25] Jean-Sebastian shares some advice as well [01:08:25] Greg Coquillo adds to the discussion on what to do when you don't hear back from applying for a job [01:10:13] Liuna closes out the discussion [01:11:51] Eric comes in and shares a LinkedIn hack. Also asks a question on scoring clusters [01:14:06] Dave has an answer to this tough question [01:17:06] Greg has a question on Federated Learning [01:27:13] Switching to python from a SAS background [01:34:04] We congratulate a member of the community on landing a job! [01:38:39] What's the difference between a data scientist and a data analyst? [01:58:41] Why do you do these happy hours? Special Guests: Carlos Mercado, David Tello, Greg Coquillo, Mikiko Bazeley, Srivatsan Srinivasan, and Vin Vashishta.