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This week on The Data Stack Show, Eric and welcomes back Ruben Burdin, Founder and CEO of Stacksync as they together dismantle the myths surrounding zero-copy ETL and traditional data integration methods. Ruben reveals the complex challenges of two-way syncing between enterprise systems like Salesforce, HubSpot, and NetSuite, highlighting how existing tools often create more problems than solutions. He also introduces Stacksync's innovative approach, which uses real-time SQL-based synchronization to simplify data integration, reduce maintenance overhead, and enable more efficient operational workflows. The conversation exposes the limitations of current data transfer techniques and offers a glimpse into a more declarative, flexible approach to managing enterprise data across multiple systems. You won't want to miss it.Highlights from this week's conversation include:The Pain of Two-Way Sync and Early Integration Challenges (2:01)Zero Copy ETL: Hype vs. Reality (3:50)Data Definitions and System Complexity (7:39)Limitations of Out-of-the-Box Integrations (9:35)The CSV File: The Original Two-Way Sync (11:18)Stacksync's Approach and Capabilities (12:21)Zero Copy ETL: Technical and Business Barriers (14:22)Data Sharing, Clean Rooms, and Marketing Myths (18:40)The Reliable Loop: ETL, Transform, Reverse ETL (27:08)Business Logic Fragmentation and Maintenance (33:43)Simplifying Architecture with Real-Time Two-Way Sync (35:14)Operational Use Case: HubSpot, Salesforce, and Snowflake (39:10)Filtering, Triggers, and Real-Time Workflows (45:38)Complex Use Case: Salesforce to NetSuite with Data Discrepancies (48:56)Declarative Logic and Debugging with SQL (54:54)Connecting with Ruben and Parting Thoughts (57:58)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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.
Madison Schott joins me to chat about about her journey from working as an analytics engineer to creating content about dbt, SQL, data modeling, and more.
In this episode of Building Better Developers with AI, Rob Broadhead and Michael Meloche revisit a popular question: What Happens When Software Fails? Originally titled When Coffee Hits the Fan: Developer Disaster Recovery, this AI-enhanced breakdown explores real-world developer mistakes, recovery strategies, and the tools that help turn chaos into control. Whether you're managing your first deployment or juggling enterprise infrastructure, you'll leave this episode better equipped for the moment when software fails. When Software Fails and Everything Goes Down The podcast kicks off with a dramatic (but realistic) scenario: CI passes, coffee is in hand, and then production crashes. While that might sound extreme, it's a situation many developers recognize. Rob and Michael cover some familiar culprits: Dropping a production database Misconfigured cloud infrastructure costing hundreds overnight Accidentally publishing secret keys Over-provisioned “default” environments meant for enterprise use Takeaway: Software will fail. Being prepared is the difference between a disaster and a quick fix. Why Software Fails: Avoiding Costly Dev Mistakes Michael shares an all-too-common situation: connecting to the wrong environment and running production-breaking SQL. The issue wasn't the code—it was the context. Here are some best practices to avoid accidental failure: Color-code terminal environments (green for dev, red for prod) Disable auto-commit in production databases Always preview changes with a SELECT before running DELETE or UPDATE Back up databases or individual tables before making changes These simple habits can save hours—or days—of cleanup. How to Recover When Software Fails Rob and Michael outline a reliable recovery framework that works in any team or tech stack: Monitoring and alerts: Tools like Datadog, Prometheus, and Sentry help detect issues early Rollback plans: Scripts, snapshots, and container rebuilds should be ready to go Runbooks: Documented recovery steps prevent chaos during outages Postmortems: Blameless reviews help teams learn and improve Clear communication: Everyone on the team should know who's doing what during a crisis Pro Tip: Practice disaster scenarios ahead of time. Simulations help ensure you're truly ready. Essential Tools for Recovery Tools can make or break your ability to respond quickly when software fails. Rob and Michael recommend: Docker & Docker Compose for replicable environments Terraform & Ansible for consistent infrastructure GitHub Actions, GitLab CI, Jenkins for automated testing and deployment Chaos Engineering tools like Gremlin and Chaos Monkey Snapshot and backup automation to enable fast data restoration Michael emphasizes: containers are the fastest way to spin up clean environments, test recovery steps, and isolate issues safely. Mindset Matters: Staying Calm When Software Fails Technical preparation is critical—but so is mindset. Rob notes that no one makes smart decisions in panic mode. Having a calm, repeatable process in place reduces pressure when systems go down. Cultural and team-based practices: Use blameless postmortems to normalize failure Avoid root access in production whenever possible Share mistakes in standups so others can learn Make local environments mirror production using containers Reminder: Recovery is a skill—one you should build just like any feature. Think you're ready for a failure scenario? Prove it. This week, simulate a software failure in your development environment: Turn off a service your app depends on Delete (then restore) a local database from backup Use Docker to rebuild your environment from scratch Trigger a mock alert in your monitoring tool Then answer these questions: How fast can you recover? What broke that you didn't expect? What would you do differently in production? Recovery isn't just theory—it's a skill you build through practice. Start now, while the stakes are low. Final Thought Software fails. That's a reality of modern development. But with the right tools, smart workflows, and a calm, prepared team, you can recover quickly—and even improve your system in the process. Learn from failure. Build with resilience. And next time something breaks, you'll know exactly what to do. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources System Backups – Prepare for the Worst Using Dropbox To Provide A File Store and Reliable Backup Testing Your Backups – Disaster Recovery Requires Verification Virtual Systems On A Budget – Realistic Cloud Pricing Building Better Developers With AI Podcast Videos – With Bonus Content
This is episode 298 recorded on July 9th, 2025, where John & Jason talk the Microsoft Fabric June 2025 Feature Summary including lots of Notebook updates in Data Engineering, lower cost for AI functions in Data Science, Copilot for RTI dashboards, and more. For show notes please visit www.bifocal.show
In today's digital world, securing your websites and web applications is more critical than ever. In this session, we break down the foundations of web security, with a sharp focus on defending against SQL injections, XSS, and other modern cyber threats. You'll learn how attackers exploit vulnerabilities in web applications and how to stop them using best practices like secure coding, parameterized queries, and Web Application Firewalls (WAFs). We also explore top web security tools, OWASP Top 10, and techniques used in penetration testing.Whether you're a developer, security analyst, or business owner, this episode equips you with the practical knowledge to identify, mitigate, and stay ahead of today's most common web attacks.
SANS Internet Stormcenter Daily Network/Cyber Security and Information Security Stormcast
SSH Tunneling in Action: direct-tcp requests Attackers are compromising ssh servers to abuse them as relays. The attacker will configure port forwarding direct-tcp connections to forward traffic to a victim. In this particular case, the Yandex mail server was the primary victim of these attacks. https://isc.sans.edu/diary/SSH%20Tunneling%20in%20Action%3A%20direct-tcp%20requests%20%5BGuest%20Diary%5D/32094 Fortiguard FortiWeb Unauthenticated SQL injection in GUI (CVE-2025-25257) An improper neutralization of special elements used in an SQL command ('SQL Injection') vulnerability [CWE-89] in FortiWeb may allow an unauthenticated attacker to execute unauthorized SQL code or commands via crafted HTTP or HTTPs requests. https://www.fortiguard.com/psirt/FG-IR-25-151 Ruckus Virtual SmartZone (vSZ) and Ruckus Network Director (RND) contain multiple vulnerabilities Ruckus products suffer from a number of critical vulnerabilities. There is no patch available, and users are advised to restrict access to the vulnerable admin interface. https://kb.cert.org/vuls/id/613753
Nikolay and Michael are joined by Sugu Sougoumarane to discuss Multigres — a project he's joined Supabase to lead, building an adaptation of Vitess for Postgres! Here are some links to things they mentioned:Sugu Sougoumarane https://postgres.fm/people/sugu-sougoumaraneSupabase https://supabase.comAnnouncing Multigres https://supabase.com/blog/multigres-vitess-for-postgresVitess https://github.com/vitessio/vitessSPQR https://github.com/pg-sharding/spqrCitus https://github.com/citusdata/citusPgDog https://github.com/pgdogdev/pgdogMyths and Truths about Synchronous Replication in PostgreSQL (talk by Alexander Kukushkin) https://www.youtube.com/watch?v=PFn9qRGzTMcConsensus algorithms at scale (8 part series by Sugu) https://planetscale.com/blog/consensus-algorithms-at-scale-part-1A More Flexible Paxos (blog post by Sugu) https://www.sougou.io/a-more-flexible-paxoslibpg_query https://github.com/pganalyze/libpg_queryPL/Proxy https://github.com/plproxy/plproxyPlanetScale Postgres Benchmarking https://planetscale.com/blog/benchmarking-postgresMultiXact member exhaustion incidents (blog post by Cosmo Wolfe / Metronome) https://metronome.com/blog/root-cause-analysis-postgresql-multixact-member-exhaustion-incidents-may-2025~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith special thanks to:Jessie Draws for the elephant artwork
This week on The Data Stack Show, Eric and John welcome back Matt Kelliher-Gibson for another edition of the Cynical Data Guy. The group explores the current state of data engineering and team dynamics while critically examining the evolving landscape of analytics engineering, dissecting the hype around the modern data stack and its tools. The conversation also explores the challenges of data team management, including headcount reductions, rising technology costs, and the struggle to maintain efficiency. Key discussions revolve around the need for open standards, the impact of AI on data roles, the complex hiring practices in tech startups, and so much more. Highlights from this week's conversation include:The Evolution of Analytics Engineer Roles (1:53)Job Titles and Role Consolidation in Data (3:20)Standardization and Open Data Standards (7:51)SQL as a Universal Standard & Vendor Lock-In (11:58)Modern Data Stack: Hype vs. Reality (13:29)The State of Data Teams in 2025 (18:12)Morale and Job Market Realities for Data Professionals (25:17)Bonus Round: Extreme Work Culture Satire (28:41)Honesty in Hiring and Team Building (33:18)Challenges of Building and Leading Data Teams (37:31)Final Thoughts and Takeaways (41:15)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. 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.
Appsec still deals with ancient vulns like SQL injection and XSS. And now LLMs are generating code along side humans. Sandy Carielli and Janet Worthington join us once again to discuss what all this new code means for appsec practices. On a positive note, the prevalence of those ancient vulns seems to be diminishing, but the rising use of LLMs is expanding a new (but not very different) attack surface. We look at where orgs are investing in appsec, who appsec teams are collaborating with, and whether we need security awareness training for LLMs. Resources: https://www.forrester.com/blogs/application-security-2025-yes-ai-just-made-it-harder-to-do-this-right/ Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-338
This is episode 297 recorded on July 4th, 2025, where John & Jason talk the Microsoft Fabric June 2025 Feature Summary including updates to Visual Calcs, Sparklines are now GA (after 4 years), Azure Maps breaking changes, Org Apps updates & revelations, and Power Query editing of Import models in the web.
Appsec still deals with ancient vulns like SQL injection and XSS. And now LLMs are generating code along side humans. Sandy Carielli and Janet Worthington join us once again to discuss what all this new code means for appsec practices. On a positive note, the prevalence of those ancient vulns seems to be diminishing, but the rising use of LLMs is expanding a new (but not very different) attack surface. We look at where orgs are investing in appsec, who appsec teams are collaborating with, and whether we need security awareness training for LLMs. Resources: https://www.forrester.com/blogs/application-security-2025-yes-ai-just-made-it-harder-to-do-this-right/ Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-338
Appsec still deals with ancient vulns like SQL injection and XSS. And now LLMs are generating code along side humans. Sandy Carielli and Janet Worthington join us once again to discuss what all this new code means for appsec practices. On a positive note, the prevalence of those ancient vulns seems to be diminishing, but the rising use of LLMs is expanding a new (but not very different) attack surface. We look at where orgs are investing in appsec, who appsec teams are collaborating with, and whether we need security awareness training for LLMs. Resources: https://www.forrester.com/blogs/application-security-2025-yes-ai-just-made-it-harder-to-do-this-right/ Show Notes: https://securityweekly.com/asw-338
What's up everyone, today we have the pleasure of sitting down with Chris O'Neill, CEO at GrowthLoop. Summary: Chris explains how leading marketing teams are deploying swarms of AI agents to automate campaign workflows with speed and precision. By assigning agents to tasks like segmentation, testing, and feedback collection, marketers build fast-moving loops that adapt in real time. Chris also breaks down how reinforcement learning helps avoid a sea of sameness by letting campaigns evolve mid-flight based on live data. To support velocity without sacrificing control, top teams are running red team drills, assigning clear data ownership, and introducing internal AI regulation roles that manage risk while unlocking scale.The 2025 AI and Marketing Performance IndexThe 2025 AI and Marketing Performance Index that GrowthLoop put together is excellent, we're honored to have gotten our hands on it before it went live and getting to unpack that with Chris in this episode. The report answers timely questions a lot of teams are are wrestling with:Are top performers ahead of the AI curve or just focused on solid foundations? Are top performers focused on speed and quantity or does quality still win in a sea of sameness?We've chatted with plenty of folks that are betting on patience and polish. But GrowthLoop's data shows the opposite.
This show has been flagged as Explicit by the host. New hosts There were no new hosts this month. Last Month's Shows Id Day Date Title Host 4391 Mon 2025-06-02 HPR Community News for May 2025 HPR Volunteers 4392 Tue 2025-06-03 The Water is Wide, and the sheet music should be too Jezra 4393 Wed 2025-06-04 Journal like you mean it. Some Guy On The Internet 4394 Thu 2025-06-05 Digital Steganography Intro mightbemike 4395 Fri 2025-06-06 Second Life Lee 4396 Mon 2025-06-09 AI and Sangria operat0r 4397 Tue 2025-06-10 Transfer files from desktop to phone with qrcp Klaatu 4398 Wed 2025-06-11 Command line fun: downloading a podcast Kevie 4399 Thu 2025-06-12 gpg-gen-key oxo 4400 Fri 2025-06-13 Isaac Asimov: Other Asimov Novels of Interest Ahuka 4401 Mon 2025-06-16 hajime oxo 4402 Tue 2025-06-17 pinetab2 Brian in Ohio 4403 Wed 2025-06-18 How to get your very own copy of the HPR database norrist 4404 Thu 2025-06-19 Kevie nerd snipes Ken by grepping xml Ken Fallon 4405 Fri 2025-06-20 What did I do at work today? Lee 4406 Mon 2025-06-23 SVG Files: Cyber Threat Hidden in Images ko3moc 4407 Tue 2025-06-24 A 're-response' Bash script Dave Morriss 4408 Wed 2025-06-25 Lynx - Old School Browsing Kevie 4409 Thu 2025-06-26 H D R Ridiculous Monitor operat0r 4410 Fri 2025-06-27 Civilization V Ahuka 4411 Mon 2025-06-30 The Pachli project thelovebug Comments this month These are comments which have been made during the past month, either to shows released during the month or to past shows. There are 29 comments in total. Past shows There are 4 comments on 3 previous shows: hpr4375 (2025-05-09) "Long Chain Carbons,Eggs and Dorodango?" by operat0r. Comment 4: Torin Doyle on 2025-06-06: "Reply to @Bob" hpr4378 (2025-05-14) "SQL to get the next_free_slot" by norrist. Comment 1: Torin Doyle on 2025-06-12: "Cheers for this." hpr4388 (2025-05-28) "BSD Overview" by norrist. Comment 4: Henrik Hemrin on 2025-06-02: "Learned more about BSD." Comment 5: norrist on 2025-06-02: "Additional info for OpenBSD Router" This month's shows There are 25 comments on 10 of this month's shows: hpr4391 (2025-06-02) "HPR Community News for May 2025" by HPR Volunteers. Comment 1: Torin Doyle on 2025-06-06: "Very disappointed."Comment 2: Ken Fallon on 2025-06-06: "Thanks for your feedback."Comment 3: Torin Doyle on 2025-06-09: "Reply to Ken [Comment 2]"Comment 4: norrist on 2025-06-09: "Watch the Queue for a show about how to find all the comments"Comment 5: Torin Doyle on 2025-06-10: "Comment #3 typo."Comment 6: Torin Doyle on 2025-06-11: "Reply to Comment #4 by norrist"Comment 7: Torin Doyle on 2025-06-11: "Got the link." hpr4394 (2025-06-05) "Digital Steganography Intro" by mightbemike. Comment 1: Henrik Hemrin on 2025-06-05: "Fascinating topic"Comment 2: oxo on 2025-06-05: "Good show! " hpr4395 (2025-06-06) "Second Life" by Lee. Comment 1: Antoine on 2025-06-08: "Brings philosophical thoughts" hpr4397 (2025-06-10) "Transfer files from desktop to phone with qrcp" by Klaatu. Comment 1: Laindir on 2025-06-18: "The perfect kind of recommendation" hpr4398 (2025-06-11) "Command line fun: downloading a podcast" by Kevie. Comment 1: Henrik Hemrin on 2025-06-11: "Tempted to have fun"Comment 2: Ken Fallon on 2025-06-22: "Personal message to redhat (nprfan)" hpr4403 (2025-06-18) "How to get your very own copy of the HPR database" by norrist. Comment 1: Torin Doyle on 2025-06-18: "Appreciated!"Comment 2: Torin Doyle on 2025-06-18: "Database size."Comment 3: norrist on 2025-06-18: "Also an SQLite version"Comment 4: Torin Doyle on 2025-06-25: "Not able to use database to find my comments." hpr4404 (2025-06-19) "Kevie nerd snipes Ken by grepping xml" by Ken Fallon. Comment 1: Henrik Hemrin on 2025-06-22: "More to digest"Comment 2: Alec Bickerton on 2025-06-29: "Shorter version"Comment 3: Alec Bickerton on 2025-06-29: "Shorter version"Comment 4: Alec Bickerton on 2025-06-29: "XML parsing without xmlstarlet" hpr4405 (2025-06-20) "What did I do at work today?" by Lee. Comment 1: Dave Morriss on 2025-06-25: "Thanks for bringing us along..." hpr4406 (2025-06-23) "SVG Files: Cyber Threat Hidden in Images" by ko3moc. Comment 1: oxo on 2025-06-23: "Interesting! "Comment 2: ko3moc on 2025-06-24: "response " hpr4408 (2025-06-25) "Lynx - Old School Browsing" by Kevie. Comment 1: Henrik Hemrin on 2025-06-29: "Review ALT texts" Mailing List discussions Policy decisions surrounding HPR are taken by the community as a whole. This discussion takes place on the Mailing List which is open to all HPR listeners and contributors. The discussions are open and available on the HPR server under Mailman. The threaded discussions this month can be found here: https://lists.hackerpublicradio.com/pipermail/hpr/2025-June/thread.html Events Calendar With the kind permission of LWN.net we are linking to The LWN.net Community Calendar. Quoting the site: This is the LWN.net community event calendar, where we track events of interest to people using and developing Linux and free software. Clicking on individual events will take you to the appropriate web page. Provide feedback on this episode.
The big tech company conferences continued this summer with Vercel hosting Vercel Ship 2025. As you'd expect there was lots of talk about AI and Vercel's AI Cloud: tools, infrastructure, and platform enhancements to build AI agents and help AI agents use Vercel.On July 1, hosting platform Cloudflare declared Content Independence Day, and changed its settings to block AI crawlers by default unless they pay creators for their content. While we absolutely support this move, Cloudflare's future vision of a marketplace where content creators and AI companies come together and compensation is based on how much content “furthers knowledge” seems idealistic, but we'll have to wait and see.Serverless Postgres database company Neon has a new product called Neon Launchpad that can create an instant Neon database with zero configuration or account creation. Users get an automatically generated connection string, 72 hours to claim a new database, and even automatic database seeding with SQL scripts for schema and data initialization.Timestamps:2:13 - Vercel Ship event updates7:49 - Cloudfare declares content independence day16:12 - Neon Launchpad20:03 - Figma IPO22:24 - Deno v. Oracle trademark update25:10 - Antropic lets Claude run a vending machine32:21 - What's making us happyLinks:News:Paige - Cloudflare declares July 1 Content Independence DayJack - Neon Launchpad instant DBsTJ - Vercel Ship 2025Lightning:Figma has filed for an IPO to trade on the stock exchange as “FIG”Claude ran a vending machine, and the first attempt at “vibe management” wasn't greatDeno v. Oracle trademark updateWhat Makes Us Happy this Week:Paige - Squid Game season 3Jack - F1: The MovieTJ - Bobby Banilla DayThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast
In this episode of the Biz To Biz Podcast, we dive into the future of business intelligence with Sarah Kabakoff, CEO of Genetica, and a driving force behind intelligent automation in modern industries.With a deep background leading revenue and product strategy at AI-driven SaaS startups, Sarah and her team have spent the last decade transforming operations in restaurants, retail, and regulated markets through cutting-edge technology.At Genetica, Sarah is leading the charge on ServeAI, a data intelligence platform that merges enriched SQL layers, LLM-powered agents, and real-time business logic. The result? A revolutionary tool that eliminates the need for static dashboards, analysts, or complex BI systems.Follow Us On These Social Media Platforms
Join Shane Gibson and Nigel Vining as they describe and discuss the AgileData Engineering Pattern of Unioning two or more tables together automatically. The Automated Table Unioning pattern automatically combines two or more tables by intelligently looking up column names and data types to generate safe SQL under the covers. It supports disparate data sources, such as those from multiple publishers in a data clean room, and creates a view or incrementally loads the unified data into a physical table while tracking load watermarks to prevent duplicates. An AgileData Engineering Pattern is a repeatable, proven approach for solving a common data engineering challenge in a simple, consistent, and scalable way, designed to reduce rework, speed up delivery, and embed quality by default. If you want a copy of the pattern template head over to: https://agiledata.substack.com/i/167481120/pattern-name Discover more AgileData Engineering Patterns over at https://agiledata.substack.com/s/agiledata-engineering-patterns 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 on LinkedIn. Subscribe: Apple Podcast | Spotify | Google Podcast | Amazon Audible | TuneIn | iHeartRadio | PlayerFM | Listen Notes | Podchaser | Deezer | Podcast Addict | Buy the Green Book now! Simply Magical Data
In the final episode of this series on Oracle GoldenGate 23ai, Lois Houston and Nikita Abraham welcome back Nick Wagner, Senior Director of Product Management for GoldenGate, to discuss how parameters shape data replication. This episode covers parameter files, data selection, filtering, and transformation, providing essential insights for managing GoldenGate deployments. Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------------- Podcast Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! This is the last episode in our Oracle GoldenGate 23ai series. Previously, we looked at how you can manage Extract Trails and Files. If you missed that episode, do go back and give it a listen. 00:50 Lois: Today, Nick Wagner, Senior Director of Product Management for GoldenGate, is back on the podcast to tell us about parameters, data selection, filtering, and transformation. These are key components of GoldenGate because they allow us to control what data is replicated, how it's transformed, and where it's sent. Hi Nick! Thanks for joining us again. So, what are the different types of parameter files? Nick: We have a GLOBALS parameter file and your runtime parameter files. The global one is going to affect all processes within a deployment. It's going to be things like where's your checkpoint table located in name, things like the heartbeat table. You want to have a single one of these across your entire deployment, so it makes sense to keep it within a single file. We also have runtime parameter files. This are going to be associated with a specific extract or replicat process. These files are located in your OGG_ETC_HOME/conf/ogg. The GLOBALS file is just simply named GLOBALS and all capitals, and your parameter file names for the processes themselves are named with the process.prm. So if my extract process is EXT demo, my parameter file name will be extdemo.prm. When you make changes to parameter files, they don't take effect until the process is restarted. So in the case of a GLOBALS parameter file, you need to restart the administration service. And in a runtime parameter file, you need to restart that specific process before any changes will take effect. We also have what we call a managed process setting profile. And this allows you to set up auto restart profiles for each process. And the GoldenGate Gate classic architecture, this was contained within the GLOBALS parameter file and handled by the manager. And microservices is a little bit different, it's handled by the service manager itself. But now we actually set up profiles. 02:41 Nikita: Ok, so what can you tell us about the extract parameter file specifically? Nick: There's a couple things within the extract parameter file is common use. First, we want to tell what the group name is. So in this case, it would be our extract name. We need to put in information on where the extract process is going to be writing the data it captures to and that would be our trail files, and extract process can write to one or more trail files. We also want to list out the list of tables and schemas that we're going to be capturing, as well as any kind of DDL changes. If we're doing an initial load, we want to set up the SQL predicate to determine which tables are being captured and put a WHERE clause on those to speed up performance. We can also do filtering within the extract process as well. So we write just the information that we need to the trail file. 03:27 Nikita: And what are the common parameters within an extract process? Nick: There are a couple of common parameters within your extract process. We have table to list out the list of tables that GoldenGate is going to be capturing from. These can be wildcarded. So I can simply do table.star and GoldenGate will capture all the tables in that database. I can also do schema.star and it will capture all the tables within a schema. We have our EXTTRAIL command, which tells GoldenGate which trail to write to. If I want to filter out certain rows and columns, I can use the filter cols and cols except parameter. GoldenGate can also capture sequence changes. So we would use the sequence parameter. And then we can also set some high-level database options for GoldenGate that affect all the tables and that's configured using the tranlog options parameter. 04:14 Lois: Nick, can you talk a bit about the different types of tranlogoptions settings? How can they be used to control what the extract process does? Nick: So one of the first ones is ExcludeTag. So GoldenGate has the ability to exclude tagged transactions. Within the database itself, you can actually specify a transaction to be tagged using a DBMS set tag option. GoldenGate replicat also sets its transactions with a tag so that the GoldenGate process knows which transactions were done by the replicat and it can exclude them automatically. You can do exclude tag with a plus. That simply means to exclude any transaction that's been tagged with any value. You can also exclude specific tags. Another good option for TranLogOptions is enable procedural replication. This allows GoldenGate to actually capture and replicate database procedure calls, and this would be things like DBMS AQ, NQ operations, or DQ operations. So if you're using Oracle advanced queuing and you need GoldenGate to replicate those changes, it can. Another valuable tranlogoption setting is enable auto capture. Within the Oracle Database, you can actually set ALTER TABLE command that says ALTER TABLE, enable logical replication. Or when you create a table, you can actually do CREATE TABLE statement and at the end use the enable logical replication option for that CREATE TABLE statement. And this tells GoldenGate to automatically capture that table. One of the nice features about this is that I don't need to specify that table and my parameter file, and it'll automatically enable supplemental logging on that table for me using scheduling columns. So it makes it very easy to set up replication between Oracle databases. 06:01 Nikita: Can you tell us about replicat parameters, Nick? Nick: Within a replicat, we'll have the group name, some common other parameters that we'll use is a mapping parameter that allows us to map the source to target table relationships. We can do transformation within the replicat, as well as error handling and controlling group operations to improve performance. Some common replicat parameters include the replicat parameter itself, which tells us what the name of that replicat is. We have our map statement, which allows us to map a source object to a target object. We have things like rep error that control how to handle errors. Insert all records allows us to change and convert, update, and delete operations into inserts. We can do things like compare calls, which helps with active-active replication in determining which columns are used in the GoldenGate WHERE clause. We also have the ability to use macros and column mapping to do additional transformation and make the parameter file look elegant. 07:07 AI is being used in nearly every industry…healthcare, manufacturing, retail, customer service, transportation, agriculture, you name it! And it's only going to get more prevalent and transformational in the future. It's no wonder that AI skills are the most sought-after by employers. If you're ready to dive in to AI, check out the OCI AI Foundations training and certification that's available for free! It's the perfect starting point to build your AI knowledge. So, get going! Head on over to mylearn.oracle.com to find out more. 07:47 Nikita: Welcome back! Let's move on to some of the most interesting topics within GoldenGate… data mapping, selection, and transformation. As I understand, users can do pretty cool things with GoldenGate. So Nick, let's start with how GoldenGate can manipulate, change, and map data between two different databases. Nick: The map statement within a Replicat parameter allows you to provide specifications on how you're going to map source and target objects. You can also use a map and an extract, but it's pretty rare. And that would be used if you needed to write the object name. Inside the trail files is a different name than the actual object name that you're capturing from. GoldenGate can also do different data selection, mapping, and manipulation, and this is all controlled within the Extract and Replicat parameter files. In the classic architecture of GoldenGate, you could do a rudimentary level of transformation and filtering within the extract pump. Now, the distribution service is only allowing you to do filtering. Any transformation that you had within the pump would need to be moved to the Extract or the Replicat process. The other thing that you can do within GoldenGate is select and filter data based on different levels and conditions. So within your parameter clause, you have your Table and Map statement. That's the core of everything. You have your filtering. You have COLS and COLSEXCEPT, which allow you to determine which columns you're going to include or exclude from replication. The Table and Map statement works at the table level. The FILTER works at the row level. And COLS and COLSEXCEPTs works at the column level. We also have the ability to filter by operation type too. So GoldenGate has some very easy parameters called GitInserts, GitUpdates, GitDeletes, and conversely ignore updates, ignore deletes, ignore inserts. And that will affect the operation type. 09:40 Lois: Nick, are there any features that GoldenGate provides to make data replication easier? Nick: The first thing is that GoldenGate is going to automatically match your source and target column names with a parameter called USEDEFAULTS. You can specify it inside of your COLMAP clause, but again, it's a default, so you don't need to worry about it. We also handle all data type and character set conversion. Because we store the metadata in the trail, we know what that source data type is like. When we go to apply the record to the target table, the Replicat process is going to look up the definition of that record and keep a repository of that in memory. So that when it knows that, hey, this value coming in from the trail file is going to be of a date data type, and then this value in the target database is going to be a character data type, it knows how to convert that date to a character, and it'll do it for you. Most of the conversion is going to be done automatically for data types. Things where we don't do automatic data type conversion is if you're using abstract data types or user-defined data types, collections arrays, and then some types of CLOB operations. For example, if you're going from a BLOB to a JSON, that's not really going to work very well. Character set conversion is also done automatically. It's not necessarily done directly by GoldenGate, but it's done by the database engine. So there is a character set value inside that source database. And when GoldenGate goes to apply those changes into the target system, it's ensuring that that character set is visible and named so that that database can do the necessary translation. You can also do advanced filtering transformation. There's tokens that you can attach from the source environment, database, or records into a record itself on the trail file. And then there's also a bunch of metadata that GoldenGate can use to attach to the record itself. And then of course, you can use data transformation within your COLMAP statement. 11:28 Nikita: Before we wrap up, what types of data transformations can we perform, Nick? Nick: So there's quite a few different data transformations. We can do constructive or destructive transformation, aesthetic, and structural. 11:39 Lois: That's it for the Oracle GoldenGate 23ai: Fundamentals series. I think we covered a lot of ground this season. Thank you, Nick, for taking us through it all. Nikita: Yeah, thank you so much, Nick. And if you want to learn more, head over to mylearn.oracle.com and search for the Oracle GoldenGate 23ai: Fundamentals course. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 12:04 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
There have been lots of social media posts declaring things to be dead - SQL, R, data engineering, BI, etc.I give my thoughts on these proclamations, why it's a wrong way to think about our space, and more.
Welcome to episode 308 of The Cloud Pod – where the forecast is always cloudy! Justin and Matt are on hand and ready to bring you an action packed episode. Unfortunately, this one is also lullaby free. Apologies. This week we're talking about Databricks and Lakebridge, Cedar Analysis, Amazon Q, Google's little hiccup, and updates to SQL – plus so much more! Thanks for joining us. Titles we almost went with this week: KV Phone Home: When Your Key-Value Store Goes AWOL When Your Coreless Service Finds Its Core Problem Oracle’s Vanity Fair: Pretty URLs for Pretty Penny From Warehouse to Lakehouse: Your Free Ticket to Cloud Town 1⃣Databricks Uno: Because One is the Loneliest Number Free as in Beer, Smart as in Data Science Cedar Analysis: Because Your Authorization Policies Wood Never Lie Cedar Analysis: Teaching Old Policies New Proofs Amazon Q Finally Learns to Talk to Other Apps Tomorrow: Visual Studio’s Predictive Edit Revolution The Ghost of Edits Future: AI Haunts Your Code Before You Write It IAM What IAM: Google’s Identity Crisis Breaks the Internet Permission Denied: The Day Google Forgot Who Everyone Was 403 Forbidden: When Google’s Bouncer Called in Sick AWS Brings the Heat to Fusion Research Larry’s Cloud Nine: Oracle Stock Soars on Forecast Raise OCI You Later: Oracle Bets Big on Cloud Growth Oracle’s Crystal Ball Shows 40% Cloud Growth Ahead Meta Scales Up Its AI Ambitions with $14 Billion Investment From FAIR to Scale: Meta’s $14 Billion AI Makeover Congratulations Databricks one, you are now the new low code solution. AWS burns power to figure out how power works AI Is Going Great – Or How ML Makes Money 02:12 Zuckerberg makes Meta’s biggest bet on AI, $14 billion Scale AI deal Meta is finalizing a $14 billion investment for a 49% stake in Scale AI, with CEO Alexandr Wang joining to lead a new AI research lab at Meta. This follows similar moves by Google and Microsoft acquiring AI talent through investments rather than direct acquisitions to avoid regulatory scrutiny. Scale AI specializes in data labeling and annotation services critical for training AI models, serving major clients including OpenAI, Google, Microsoft, and Meta. The company’s expertise covers approximately 70% of all AI models being built, providing Meta with valuable intelligence on competitor approaches to model development. The deal reflects Meta’s struggles with its Llama AI models, particularly the underwhelming reception of Llama 4 and delays in releasing the more powerful “Behemoth” model due to concerns about competitiveness with OpenAI and
Amazon Aurora DSQL promises to bring a truly serverless experience to SQL databases. But does it actually deliver? In this episode of AWS Bites, we put Aurora DSQL to the test. We explore what makes it exciting, how it compares to traditional Aurora Serverless, and where it falls short. You'll hear what changed since our last Aurora deep dive, and why DSQL might be the PostgreSQL-compatible serverless database you've been waiting for.Big shoutout to fourTheorem for powering yet another episode of AWS Bites. At fourTheorem, we believe the cloud should be simple, scalable, and cost-effective, and we help teams do just that. Whether you're diving into containers, stepping into event-driven architecture, or scaling a global SaaS platform on AWS, or trying to keep cloud spend under control our team has your back. Visit https://fourTheorem.com to see how we can help you build faster, better, and with more confidence using AWS cloud!In this episode, we mentioned the following resources:AWS Blog – Amazon Aurora DSQL is Now Generally Available: https://aws.amazon.com/blogs/aws/amazon-aurora-dsql-is-now-generally-available/AWS Blog – Introducing Amazon Aurora DSQL: https://aws.amazon.com/blogs/database/introducing-amazon-aurora-dsql/Aurora DSQL Deep Dive by Amazon: https://www.youtube.com/watch?v=GCdAngjKZY4DSQL Example Application by fourTheorem: https://github.com/fourTheorem/dsql-example/PlanetScale Docs – Why Does PlanetScale Not Recommend Constraints: https://planetscale.com/docs/vitess/operating-without-foreign-key-constraints#why-does-planetscale-not-recommend-constraints-Marc Bowes – How to Spend a Dollar with DSQL: https://marc-bowes.com/dsql-how-to-spend-a-dollar.htmlAlessandro Volpicella – Amazon DSQL Pricing Guide: https://awsfundamentals.com/blog/amazon-dsql-pricing-guideBinaryHeap Blog – First Look at DSQL (The Naughty List): https://binaryheap.com/first-look-dsql/AWS Bites – Aurora Deep Dive (Episode 122): https://awsbites.com/122Do you have any AWS questions you would like us to address?Leave a comment here or connect with us on X/Twitter, BlueSky or LinkedIn:- https://twitter.com/eoins | https://bsky.app/profile/eoin.sh | https://www.linkedin.com/in/eoins/- https://twitter.com/loige | https://bsky.app/profile/loige.co | https://www.linkedin.com/in/lucianomammino/
Nikolay and Michael are joined by Gwen Shapira to discuss multi-tenant architectures — the high level options, the pros and cons of each, and how they're trying to help with Nile. Here are some links to things they mentioned:Gwen Shapira https://postgres.fm/people/gwen-shapiraNile https://www.thenile.devSaaS Tenant Isolation Strategies (AWS whitepaper) https://docs.aws.amazon.com/whitepapers/latest/saas-tenant-isolation-strategies/saas-tenant-isolation-strategies.html Row Level Security https://www.postgresql.org/docs/current/ddl-rowsecurity.htmlCitus https://github.com/citusdata/citusPostgres.AI Bot https://postgres.ai/blog/20240127-postgres-ai-bot RLS Performance and Best Practices https://supabase.com/docs/guides/troubleshooting/rls-performance-and-best-practices-Z5JjwvCase Gwen mentioned about the planner thinking an optimisation was unsafe Re-engineering Postgres for Millions of Tenants (Gwen's recent talk at PGConf.dev) https://www.youtube.com/watch?v=EfAStGb4s88 Multi-tenant database the good, the bad, the ugly (talk by Pierre Ducroquet at PgDay Paris) https://www.youtube.com/watch?v=4uxuPfSvTGU ~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith special thanks to:Jessie Draws for the elephant artwork
In today's episode, James Maude chats with Robin Wood—better known as “DigiNinja”—the creator of DVWA and co-founder of SteelCon. Robin shares wild stories from his hacking career, including an infamous SQL injection that accidentally overwrote every customer's credit card info on a gambling site, how he took down entire client networks with just two packets, and the origins of the UK's most eccentric security conference, SteelCon—featuring 450 stuffed whippets and full-on Nerf gun warfare.
In this episode of AI + a16z, dbt Labs founder and CEO Tristan Handy sits down with a16z's Jennifer Li and Matt Bornstein to explore the next chapter of data engineering — from the rise (and plateau) of the modern data stack to the growing role of AI in analytics and data engineering. As they sum up the impact of AI on data workflows: The interesting question here is human-in-the-loop versus human-not-in-the-loop. AI isn't about replacing analysts — it's about enabling self-service across the company. But without a human to verify the result, that's a very scary thing.Among other specific topics, they also discuss how automation and tooling like SQL compilers are reshaping how engineers work with data; dbt's new Fusion Engine and what it means for developer workflows; and what to make of the spate of recent data-industry acquisitions and ambitious product launches.Follow everyone on X:Tristan HandyJennifer LiMatt Bornstein Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.
At 23, Isaac is already jaded about software reliability - and frankly, he's got good reason to be. When your grandmother can't access her medical records because a username change broke the entire system, when bugs routinely make people's lives harder, you start to wonder: why do we just accept that software is broken most of the time?Isaac's answer isn't just better testing - it's a whole toolkit of techniques working together. He's advocating for scattering "little bombs" throughout your code via runtime assertions, adding in the right amount of static typing, building feedback loops that page you when invariants break, and running nightly SQL queries to catch the bugs that slip through everything else. All building what he sees as a pyramid of software reliability.Weaving into that, we also dive into the Roc programming language, its unique platform architecture that tailors development to specific domains. Software reliability isn't just about the end user experience - Roc feeds in the idea we can make reliability easier by tailoring the language domain to the problem at hand.–Isaac's Homepage: https://isaacvando.com/Episode on Property Testing: https://youtu.be/wHJZ0icwSkcProperty Testing Walkthrough: https://youtu.be/4bpc8NpNHRcSupport Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@developervoices/joinIsaac on LinkedIn: https://www.linkedin.com/in/isaacvando/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/
This is episode 296 recorded on June 6th, 2025, where John & Jason talk the Microsoft Fabric May 2025 Feature Summary including a REST API updates for Fabric, updates to User Data Functions, Copilot in Power BI support for Fabric data agents, CosmosDB in Fabric, DataFlows Gen 2 CI/CD support is now GA, updates to Data Pipelines & Mirroring, and much more. For show notes please visit www.bifocal.show
In this episode, Lois Houston and Nikita Abraham, along with Nick Wagner, Senior Director of Product Management, dive into the Replicat process in Oracle GoldenGate 23ai. They discuss how Replicat applies changes to the target database, highlighting the different types: Classic, Coordinated, and Parallel Replicat. Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to another episode of the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! If you've been listening to us these last few weeks, you'll know we've been discussing the fundamentals of GoldenGate 23ai. Today is going to be all about the Replicat process. Again, this is something we've discussed briefly in earlier episodes, but just to recap, the Replicat process applies changes from the source database to the target database. It's responsible for reading trail files and applying the changes to the target system. 01:04 Lois: That's right, Niki. And we'll be chatting with Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Thanks for joining us again today. Let's get straight into it. Can you give us an overview of the Replicat process? Nick: One thing that's very important is the Replicat is extremely chatty with that target database. So it's going to be going in and trying to make lots of little transactions on that system. The Replicat process only issues single row DML. So if you can imagine a source database that's generating hundreds of thousands of changes per second, we're going to have to have a Replicat process that can do 100,000 changes per second on that target site. That means that it's going to have to send a lot of little one record commands. And so we've got a lot of ways to optimize that. But in all situations you're really going to want very, very low ping time between that Replicat process and that target database. This often means that if you're going to be running GoldenGate in a cloud, you're going to want the Cloud GoldenGate environment to be running in that target data center, wherever that target database is. 02:06 Lois: What are the key characteristics of the process, Nick? Nick: Replicat process is going to read the changes from the trail file and then apply them to the target system, just like any database user would. It's not doing anything special where it's going under the covers and trying to apply directly to the database blocks. It's just applying regular standard insert, update, delete, and DDL statements to that target database. A single trail file does support high volume of data replication activity depending on the type of Replicat. Replicats do preserve the boundary of their transactions. So in the situations, by default, a transaction that's on the source, let's say five inserts followed by a commit will remain five inserts followed by a commit on the target site. There are some operations and changes that do affect this, but they're not turned on by default. There are things like group transactions that allows you to group multiple transactions into a single commit. This one could actually improve performance in some cases. We also have batch SQL that can change the boundaries of a transaction as well. And then in a Parallel Replicat, you actually have the ability to split a large transaction into multiple chunks and apply those chunks in Parallel. So again, by default, it's going to preserve the boundaries, but there are ways to change that. And then the Replicats use a checkpoint table to help with recovery and to know where they're applying data and what they've done. The other thing in here is, like an Extract process can write to multiple trails and write subsets of data to each one, a Replicat can only process a single set of trail files at once. So it's going to be attached to a specific trail file like trail file AB, and will only be able to read changes from trail file AB. If I have multiple trails that need to be applied into a target system, then I have to set up multiple Replicats to handle that. 03:54 Nikita: So, what are the different Replicat types, Nick? Nick: We have three types in the product today. We have Classic Replicat, which should really only be used for testing purposes or in environments that don't support any of the other specialized Replicats. We have Coordinated Replicat, which is a high speed apply mechanism to apply data into a target system. It does have some parallelism in it, but it's user defined parallelism. And then we have our flagship and that's Parallel Replicat. And this is the most performant lowest latency Replicat that we have. 04:25 Lois: Ok. Let's dive a little deeper into each of them, starting with the Classic Replicat. How does it work? Nick: It's pretty straightforward. You're going to have a process that reads the trail files, and then in a single threaded fashion it's going to take the trail file logical change record, convert it to an insert, update, or delete, and then apply it into that target database. Each transaction that it does is preceded by a change to the checkpoint table. So when the transaction that the Replicat is currently doing is committed, that checkpoint table update also gets committed. That way when the Replicat restarts, it knows exactly what transaction it left off and how it last applied the record. And all the Replicats work the same way with regards to checkpoint tables. They each have their own little method of ensuring that the transaction they're applying is also reflected within the checkpoint table so that when it restarts, it knows exactly where it happened. That way, if a Replicat dies in the middle of a transaction, it can be restarted without any duplicate data or without missing data. 05:29 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You'll find training on everything from multicloud, database, networking, and security to artificial intelligence and machine learning, all free for our subscribers. So, what are you waiting for? Pick a topic, head over to mylearn.oracle.com, and get started. 05:53 Nikita: Welcome back! Moving on, what about Coordinated Replicat? Nick: The Coordinated Replicat is going to read from a set of trail files. It's going to have multiple threads that do this. So you have your base thread, your coordinated thread that's going to be thread 1. It's going to process the data and apply it into that target database. You then have thread 2, 4, 5, 6, and so on. When you set up your Replicat parameter file for a Coordinated Replicat, the map commands that maps from one table on the source to a table on the target has an additional option. So you'll have an option called a range or thread range. With the range and thread range option, you can actually tell which table to go into which thread. 06:38 Lois: Can you give us an example of this? Nick: So I could say map Scott.M into thread 1 and I want Scott.Dept into thread 2. Well, this is fantastic until you realize that Scott.M and Scott.Dept have a foreign key between them or a child dependencies, parent-child relationships. What that means is that now I'm going to have to disable that foreign key on the target site, because there's no way for GoldenGate to coordinate the changes in one thread to another thread. And so you really have to be careful on how you pair your tables together. If you don't have any referential integrity on that target database, then you can use parallel coordinated Replicat to really high degrees of parallelism, and you get some very good performance out of it. Let's say that you have a table that's really got too much data for even a single thread to process, that's where the thread range comes in. And thread range command will use something like the table's primary key to split transactions on that table across multiple threads. So I can say, hey, take my table Scott.M and I want to spread transactions across threads 10, 11, 12, 13, and 14 and then spread them evenly based on the primary key. And Coordinated Replicat will do that. So you can get some very high performance numbers out of it and you can really fine tune the tables, especially if you know the amount of data coming into each one. While this does work great, we observed that a lot of customers really don't know their applications to that level of detail, and so we needed a different method to push data into that target database, where we could define the parallelism based on the database expectations. So instead of the customer having to try and figure out what are the parent-child relationships, why can't GoldenGate do it for me? And that led to Parallel Replicat. 08:26 Nikita: And what are the benefits and features of the Parallel Replicat process? Nick: So Parallel Replicat has been around for quite a few years now. It supports most targets, it was Oracle initially, but now it's been expanded out to a lot of the non-Oracle targets and even some of the nonrelational database targets. It has absolutely the best performance of any Replicat process out there. You can use it to split large transactions as well. So if all of a sudden you have a batch job that does a million inserts followed by a single commit, I can split that across 10 threads, each thread doing 100,000 inserts. And it's aware of your transaction dependencies, that's the cool thing. So in Coordinated Replicat, you had to worry about how to split your tables up, in Parallel Replicat, we do it for you. 09:11 Lois: And how does Parallel Replicat work? Nick: So there's three main processes to the Parallel Replicat. You have your first is the mapper process. This is going to be responsible for taking the data out of the trail files and putting them into kind of our collator and scheduler box. As transactions go from the trail file, they get put into this box in memory where they're processed. There's a collator process that will look at these processes and go, OK, as they're coming in, let me read some of the data in them to determine how they can be applied in Parallel or not. And so the collator process understands the foreign key dependencies on that target database. And it's able to say, hey, I know that my two tables are these two tables, have a parent-child relationship, I need to make sure that changes on those tables go in the correct order. And so if all of a sudden you see an insert using the parent record and then another insert into the child record and they're mapped together, GoldenGate will ensure that those two transactions go serially and not parallel where they could get applied out of order. There's then a scheduler process that's going to look at this and say, OK, now that I'm taking transactions from the collator process, who's already identified whether or not transactions can be applied in parallel or serial, and I'm going to feed them off to applier processes that are ready and waiting for me to apply those changes into the database. And then the applier process is waiting for the scheduler process to send its transactions and say, OK, what's my next one? Where's the next transaction I should be working on and applying? And then the applier process is the one actually applying the changes into that target database, again, just using standard DML operations. So there's a lot of benefits to this one. You don't need to worry about your foreign key dependencies, you can leave all your foreign keys enabled. The collator process will actually use information within the trail file to determine which transactions can be applied in parallel, and which one needs to be applied serially. 11:13 Lois: Thank you, Nick, for this insightful conversation. There's loads more to discover about the Replicat process, and you can do that by heading over to mylearn.oracle.com and searching for the Oracle GoldenGate 23ai: Fundamentals course. Nikita: In our next episode, Nick will take us through managing Extract Trails and Files. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 11:37 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
In this episode, Dave and Jamison answer these questions: A listener named Mike says, To what degree do you think it's appropriate to talk with your peer managers about people that have moved from their team to yours? How much weight do you give their criticisms of an IC that they used to manage that is working out just fine under your leadership? How do you know if it was mostly due to a conflict in their relationship, or if there's a nugget of truth you need to look out for? Hi, thanks for a great show. I've listened to 400 episodes in a year - thanks for making my commute fun! I've been at my current job as a software developer for a year. It's a great company overall, but we rely on a 30-year-old in-house ticket system that also doubles as a time reporting tool. It lacks many basic features, and project managers often resort to SQL and Excel just to get an overview. As you can imagine, things get forgotten and lost easily. Everyone dislikes it, but the old-timers are used to it. They want any replacement to be cheap and also handle time reporting, which really limits our options. I suggested to keep using the old system for time reporting only for now, but the reaction made me feel like I'd suggested going back to pen and paper. While the company is old and set in its ways in some areas, it has made big changes in others, so I'm not ready to give up hope just yet. How can I at least nudge the company toward adopting a more modern ticket system to improve visibility and planning? I've shown examples that save time and offer better overviews, but it hasn't made much impact. Where should I focus my efforts—or do I just have to learn to live with it? Some more context: This is in Europe and the culture at the company is generally open to feedback and discussions from anyone. I have 10+ years experience and a relatively good influence. My manager is driving change successfully to make the company more modern but I suspect he might have given up on this one.
Bob Ward is a Principal Architect for the Microsoft Azure Data team, which owns the development for Microsoft SQL Edge to Cloud. Bob has worked for Microsoft for 31-plus years on every version of SQL Server shipped, from OS/2 1.1 to SQL Server 2025, including Azure SQL. Bob is a well-known speaker on SQL Server, Azure SQL, AI, and Microsoft Fabric, often presenting talks on new releases, internals, and specialized topics at events such as SQLBits, Microsoft Build, Microsoft Ignite, PASS Summit, DevIntersection, and VS Live. You can also learn Azure SQL from him on the popular series https://aka.ms/azuresql4beginners. You can follow him on X at @bobwardms or linkedin.com/in/bobwardms. Bob is the author of the books Pro SQL Server on Linux, SQL Server 2019 Revealed, Azure SQL Revealed with a 2nd edition, and SQL Server 2022 Revealed available from Apress Media. Topics of Discussion: [1:38] Bob reflects on nearly 30 years at Microsoft, growing alongside SQL Server since 1993. [4:16] Transitioning from engineering to advocacy: why Bob now focuses on helping developers unlock the power of SQL Server. [6:12] Debunking myths about SQL Server — yes, it's cloud-ready, developer-friendly, and supports containers and Linux. [10:15] Key tools and features for developers using SQL: containers, Bicep templates, SQLCMD, and DevOps pipelines. [16:23] SQL projects and source control: how modern database DevOps practices improve reliability and testing. [19:32] Common challenges in database development: fear of breaking production, limited test data, and cultural silos. [22:55] Bob's perspective on responsible database change management and the importance of a good rollback plan. [26:02] The evolution of developer tooling in SQL Server, and how Microsoft is making the CLI and APIs first-class citizens. [30:47] Advice for new developers: SQL isn't going anywhere, and it's easier than ever to get started. [34:00] Resources and community support: Bob highlights docs, GitHub samples, training courses, and his book. Mentioned in this Episode: Clear Measure Way Architect Forum Software Engineer Forum Programming with Palermo — New Video Podcast! Email us at programming@palermo.net. Clear Measure, Inc. (Sponsor) Bob Ward: SQL Server - Episode 321 Bob Ward LinkedIn Bob Ward MBob Ward — Microsoft | LinkedInicrosoft Azure SQL Revealed: The Next-Generation Cloud Database with AI and Microsoft Fabric Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.
Every once in awhile we release a new episode free for all to see, and today is that day! Please enjoy this episode, and if you find this interesting you may want to consider a subscription https://www.pointfree.co/pricing. --- We conclude our series on “modern persistence” with advanced queries that leverage reusable SQL builders, “safe” SQL strings, and complex aggregations, including JSON arrays and a query that selects many stats in a single query.
Nikolay and Michael discuss looking at queries by mean time — when it makes sense, why ordering by a percentile (like p99) might be better, and the merits of approximating percentiles in pg_stat_statements using the standard deviation column. Here are some links to things they mentioned:Approximate the p99 of a query with pg_stat_statements (blog post by Michael) https://www.pgmustard.com/blog/approximate-the-p99-of-a-query-with-pgstatstatementspg_stat_statements https://www.postgresql.org/docs/current/pgstatstatements.html Our episode about track_planning https://postgres.fm/episodes/pg-stat-statements-track-planning pg_stat_monitor https://github.com/percona/pg_stat_monitorstatement_timeout https://www.postgresql.org/docs/current/runtime-config-client.html#GUC-STATEMENT-TIMEOUT~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
This episode kicks off with a look at Rock RMS version 17.1 updates, including a fix for Insights reports and guidance on unknown marital statuses. The team also addresses and clears up rumors around vendor incentives. Then, John lays out your “Mission Possible” for the summer — with practical ideas to level up in SQL, Lava, UI styling, communication, learning management, and more. Discover how to grow, lead, and prepare for RX! Hosted on Acast. See acast.com/privacy for more information.
SANS Internet Stormcenter Daily Network/Cyber Security and Information Security Stormcast
OctoSQL & Vulnerability Data OctoSQL is a neat tool to query files in different formats using SQL. This can, for example, be used to query the JSON vulnerability files from CISA or NVD and create interesting joins between different files. https://isc.sans.edu/diary/OctoSQL+Vulnerability+Data/32026 Mirai vs. Wazuh The Mirai botnet has now been observed exploiting a vulnerability in the open-source EDR tool Wazuh. https://www.akamai.com/blog/security-research/botnets-flaw-mirai-spreads-through-wazuh-vulnerability DNS4EU The European Union created its own public recursive resolver to offer a public resolver compliant with European privacy laws. This resolver is currently operated by ENISA, but the intent is to have a commercial entity operate and support it by a commercial entity. https://www.joindns4.eu/ WordPress FAIR Package Manager Recent legal issues around different WordPress-related entities have made it more difficult to maintain diverse sources of WordPress plugins. With WordPress plugins usually being responsible for many of the security issues, the Linux Foundation has come forward to support the FAIR Package Manager, a tool intended to simplify the management of WordPress packages. https://github.com/fairpm
This is episode 295 recorded on June 5th, 2025, where John & Jason talk the Power BI May 2025 Feature Summary including a new fabric roadmap tool, Copilot & AI enhancements, Translytical task flows, TMDL view enhancements, and more. For show notes please visit www.bifocal.show
Nikolay and Michael discuss logging in Postgres — mostly what to log, and why changing quite a few settings can pay off big time in the long term. Here are some links to things they mentioned:What to log https://www.postgresql.org/docs/current/runtime-config-logging.html#RUNTIME-CONFIG-LOGGING-WHATOur episode about Auditing https://postgres.fm/episodes/auditing Our episode on auto_explain https://postgres.fm/episodes/auto_explain Here are the parameters they mentioned changing:log_checkpointslog_autovacuum_min_duration log_statementlog_connections and log_disconnectionslog_lock_waitslog_temp_fileslog_min_duration_statement log_min_duration_sample and log_statement_sample_rate And finally, some very useful tools they meant to mention but forgot to! https://pgpedia.infohttps://postgresqlco.nfhttps://why-upgrade.depesz.com/show?from=16.9&to=17.5 ~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
Streamline your entire data workflow, from real-time change capture to querying across cloud and on-prem databases, without complex migrations or code changes using SQL Server 2025. This adds deep AI integration with built-in vector search and DiskANN optimizations, plus native support for large object JSON and new Change Event Streaming for live data updates. Join and analyze data faster with the Lakehouse shortcuts in Microsoft Fabric that unify multiple databases—across different SQL Server versions, clouds, and on-prem—into a single, logical schema without moving data. Build intelligent apps, automate workflows, and unlock rich insights with Copilot and the unified Microsoft data platform, including seamless Microsoft Fabric integration, all while leveraging your existing SQL skills and infrastructure. Bob Ward, lead SQL engineer, joins Jeremy Chapman to share how the latest SQL Server 2025 innovations simplify building complex, high-performance workloads with less effort. ► QUICK LINKS: 00:00 - Updates to SQL Server 2025 00:58 - Search and AI 03:55 - Native JSON Support 06:41 - Real-Time Change Event Streaming 08:40 - Optimized Locking for Better Concurrency 10:33 - Join SQL Server data with Fabric 13:53 - Wrap up ► Link References Start using SQL Server 2025 at https://aka.ms/GetSQLServer2025 ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics
Guest Alan Braithwaite, Co-founder and CTO @ RunReveal Topics: SIEM is hard, and many vendors have discovered this over the years. You need to get storage, security and integration complexity just right. You also need to be better than incumbents. How would you approach this now? Decoupled SIEM vs SIEM/EDR/XDR combo. These point in the opposite directions, which side do you think will win? In a world where data volumes are exploding, especially in cloud environments, you're building a SIEM with ClickHouse as its backend, focusing on both parsed and raw logs. What's the core advantage of this approach, and how does it address the limitations of traditional SIEMs in handling scale? Cribl, Bindplane and “security pipeline vendors” are all the rage. Won't it be logical to just include this into a modern SIEM? You're envisioning a 'Pipeline QL' that compiles to SQL, enabling 'detection in SQL.' This sounds like a significant shift, and perhaps not to the better? (Anton is horrified, for once) How does this approach affect detection engineering? With Sigma HQ support out-of-the-box, and the ability to convert SPL to Sigma, you're clearly aiming for interoperability. How crucial is this approach in your vision, and how do you see it benefiting the security community? What is SIEM in 2025 and beyond? What's the endgame for security telemetry data? Is this truly SIEM 3.0, 4.0 or whatever-oh? Resources: EP197 SIEM (Decoupled or Not), and Security Data Lakes: A Google SecOps Perspective EP123 The Good, the Bad, and the Epic of Threat Detection at Scale with Panther EP190 Unraveling the Security Data Fabric: Need, Benefits, and Futures “20 Years of SIEM: Celebrating My Dubious Anniversary” blog “RSA 2025: AI's Promise vs. Security's Past — A Reality Check” blog tl;dr security newsletter Introducing a RunReveal Model Context Protocol Server! MCP: Building Your SecOps AI Ecosystem AI Runbooks for Google SecOps: Security Operations with Model Context Protocol
Nikolay and Michael discuss moving off managed services — when and why you might want to, and some tips on how for very large databases. Here are some links to things they mentioned:Patroni https://github.com/patroni/patronipgBackRest https://github.com/pgbackrest/pgbackrestWAL-G https://github.com/wal-g/wal-gHetzner Cloud https://www.hetzner.com/cloudPostgres Extensions Day https://pgext.daypg_wait_sampling https://github.com/postgrespro/pg_wait_samplingpg_stat_kcache https://github.com/powa-team/pg_stat_kcacheauto_explain https://www.postgresql.org/docs/current/auto-explain.htmlFivetran https://www.fivetran.compgcopydb https://github.com/dimitri/pgcopydbKafka https://kafka.apache.orgDebezium https://debezium.iomax_slot_wal_keep_size https://www.postgresql.org/docs/current/runtime-config-replication.html#GUC-MAX-SLOT-WAL-KEEP-SIZElog_statement DDL https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-STATEMENTPgBouncer pause/resume https://www.pgbouncer.org/usage.html#pause-db~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
Imagine writing SQL and getting instant results as you type? Yes, this is reality now. It's amazing!DuckDB/MotherDuck's Instant SQL made a big splash at last month's Data Council. Hamilton Ulmer gives a demo of Instant SQL at the Practical Data Community.----------------------------Instant SQL: https://motherduck.com/blog/introducing-instant-sql/Practical Data Community Discord: https://discord.gg/gNfw5AKWSK
In this kickoff episode of Decoded, Phillip Jackson sits down with Pini Yakuel to explore the concept of "positionless marketing" — a radical rethinking of how marketing teams operate in an AI-powered world. Drawing inspiration from the evolution of positionless basketball, Pini argues that marketing, like sports, is evolving toward roles defined by agility and capability, not titles or silos. The conversation weaves through leadership, startup culture, and how Optimove is enabling marketers to work faster, smarter, and more autonomously.Key TakeawaysPositionless marketing is a mindset — It's about autonomy, adaptability, and eliminating bottlenecks, not just rearranging the org chart.Modern teams thrive when roles are fluid — Inspired by positionless basketball, today's marketers succeed through cross-functionality and creative flexibility, not rigid specialization.Gen AI is the new creative exoskeleton — Like an Iron Man suit, AI tools enhance marketers' abilities, enabling faster, smarter, and more creative execution.Speed is the native language of startups — Startups operate positionlessly by necessity, while legacy orgs must dismantle silos and empower self-service to keep up.Positionless isn't chaos—it's craftsmanship — The best managers focus less on blocking and tackling, and more on elevating outcomes by distributing capability and unlocking human potential at scale.Key Quotes[00:12:25] “Let's look at the Renaissance man... the celebration of the wide gamut of human talent — that's what this could be.” – Pini[00:24:53] “It's not that departments will disappear. It's that the type of work they do will start to change.” – Pini[00:26:23] “Almost every person in our exec team started their job at Optimove by writing SQL.” – Pini[00:30:12] “A team should be small enough to be fed by two pizzas — and fully autonomous.” – Pini (on the Bezos principle)[00:34:07] “You're already positionless — that's why you get to focus on what actually matters: the work.” – Pini, on Phillip's agile team setupAssociated Links:Learn more about Optimove's platformsLearn more about Positionless MarketingCheck out Future Commerce on YouTubeCheck out Future Commerce+ for exclusive content and save on merch and printSubscribe to Insiders and The Senses to read more about what we are witnessing in the commerce worldListen to our other episodes of Future CommerceHave any questions or comments about the show? Let us know on futurecommerce.com, or reach out to us on Twitter, Facebook, Instagram, or LinkedIn. We love hearing from our listeners!
Nikolay and Michael discuss heavyweight locks in Postgres — how to think about them, why you can't avoid them, and some tips for minimising issues. Here are some links to things they mentioned:Locking (docs) https://www.postgresql.org/docs/current/explicit-locking.htmlPostgres rocks, except when it blocks (blog post by Marco Slot) https://www.citusdata.com/blog/2018/02/15/when-postgresql-blocks/Lock Conflicts (tool by Hussein Nasser) https://pglocks.org/log_lock_waits (docs) https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-LOCK-WAITSHow to analyze heavyweight lock trees (guide by Nikolay) https://gitlab.com/postgres-ai/postgresql-consulting/postgres-howtos/-/blob/main/0042_how_to_analyze_heavyweight_locks_part_2.mdLock management (docs) https://www.postgresql.org/docs/current/runtime-config-locks.htmlOur episode on zero-downtime migrations https://postgres.fm/episodes/zero-downtime-migrations~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork
In Episode 209 of Manufacturing Hub, we sit down with Dylan to explore the full spectrum of automation—from his early hands-on experiences in PLC programming all the way to architecting full-scale SCADA and MES systems. If you're looking to understand what it really takes to grow a career in industrial automation, this conversation delivers raw insights, practical lessons, and battle-tested strategies from the plant floor to the boardroom.Dylan shares how his career evolved from service technician to systems integrator, detailing the learning curve involved in jumping between platforms like Ignition, FactoryTalk, Wonderware, and SQL databases. We dig into real-world project challenges, the importance of simulation and testing, and what it means to deliver systems that operators actually enjoy using. Along the way, Dylan offers valuable advice on how to learn faster, deal with unclear project scopes, and design better user interfaces by borrowing principles from modern UX and UI design.We also examine:Why ownership and internal technical teams are critical for end usersThe importance of interoperability and avoiding vendor or integrator lock-inHow project creep really happens and what you can do about itVisualization trends in SCADA and HMI systems, including practical opinions on high-performance design and AR/VRData strategies for manufacturing, from pipe-level decisions to planning for future use casesDylan's new venture, Abelara, and how it helps manufacturers align executive vision with plant-floor executionThis episode is a must-listen for engineers, integrators, and manufacturing leaders looking to modernize their operations while keeping both usability and scalability in mind. Whether you're early in your automation career or navigating complex transformation efforts, you'll walk away with insights you can apply immediately.⏱ Timestamps:00:00 – Introduction00:08 – What is Manufacturing Hub? Meet Dylan, our guest02:00 – Dylan's career path from tech school to SCADA systems04:00 – Early project experience and rapid on-the-job learning06:30 – Moving from PLCs to SCADA and MES development08:20 – Learning without mentors: forums, support lines, and trial by fire10:10 – Challenges and opportunities with modern control platforms12:00 – Vendor openness, interoperability, and practical system limitations15:00 – Scope creep and how to reduce it with better project planning17:00 – The role of simulation and show-and-tell in successful startups20:00 – Getting end user buy-in from operators to executives22:15 – UI and UX in industrial systems: beyond standards and templates26:00 – Why most HMI screens are outdated and how to improve them30:00 – Using consumer design trends in industrial HMI development33:00 – Ownership vs. partnership: the evolving role of integrators36:00 – Visualization tools: what's working and what still needs to improve40:00 – Data in manufacturing: planning, silos, and interoperability45:00 – Why planning trumps tools and how to avoid duplicate systems49:00 – Real talk on end user responsibility and integrator lock-in54:00 – How local integrators can thrive in a reshoring environment57:30 – Early signs and implications of reshoring in manufacturing01:01:00 – Introducing Abelara: Dylan and Glenn's new consulting venture01:04:00 – Book recommendation: Silos, Politics and Turf Wars01:06:00 – Career advice for new engineers: learn by doing01:09:00 – Final thoughts and how to connect with Dylan and Abelara
In this episode of our InfoSecurity Europe 2024 On Location coverage, Marco Ciappelli and Sean Martin sit down with Professor Peter Garraghan, Chair in Computer Science at Lancaster University and co-founder of the AI security startup Mindgard. Peter shares a grounded view of the current AI moment—one where attention-grabbing capabilities often distract from fundamental truths about software security.At the heart of the discussion is the question: Can my AI be hacked? Peter's answer is a firm “yes”—but not for the reasons most might expect. He explains that AI is still software, and the risks it introduces are extensions of those we've seen for decades. The real difference lies not in the nature of the threats, but in how these new interfaces behave and how we, as humans, interact with them. Natural language interfaces, in particular, make it easier to introduce confusion and harder to contain behaviors, especially when people overestimate the intelligence of the systems.Peter highlights that prompt injection, model poisoning, and opaque logic flows are not entirely new challenges. They mirror known classes of vulnerabilities like SQL injection or insecure APIs—only now they come wrapped in the hype of generative AI. He encourages teams to reframe the conversation: replace the word “AI” with “software” and see how the risk profile becomes more recognizable and manageable.A key takeaway is that the issue isn't just technical. Many organizations are integrating AI capabilities without understanding what they're introducing. As Peter puts it, “You're plugging in software filled with features you don't need, which makes your risk modeling much harder.” Guardrails are often mistaken for full protections, and foundational practices in application development and threat modeling are being sidelined by excitement and speed to market.Peter's upcoming session at InfoSecurity Europe—Can My AI Be Hacked?—aims to bring this discussion to life with real-world attack examples, systems-level analysis, and a practical call to action: retool, retrain, and reframe your approach to AI security. Whether you're in development, operations, or governance, this session promises perspective that cuts through the noise and anchors your strategy in reality.___________Guest: Peter Garraghan, Professor in Computer Science at Lancaster University, Fellow of the UK Engineering Physical Sciences and Research Council (EPSRC), and CEO & CTO of Mindgard | https://www.linkedin.com/in/pgarraghan/ Hosts:Sean Martin, Co-Founder at ITSPmagazine | Website: https://www.seanmartin.comMarco Ciappelli, Co-Founder at ITSPmagazine | Website: https://www.marcociappelli.com___________Episode SponsorsThreatLocker: https://itspm.ag/threatlocker-r974___________ResourcesPeter's Session: https://www.infosecurityeurope.com/en-gb/conference-programme/session-details.4355.239479.can-my-ai-be-hacked.htmlLearn more and catch more stories from Infosecurity Europe 2025 London coverage: https://www.itspmagazine.com/infosec25Catch all of our event coverage: https://www.itspmagazine.com/technology-and-cybersecurity-conference-coverageWant to tell your Brand Story Briefing as part of our event coverage? Learn More
PostgreSQL is an open-source database known for its robustness, extensibility, and compliance with SQL standards. Its ability to handle complex queries and maintain high data integrity has made it a top choice for both start-ups and large enterprises. Heikki Linnakangas is a leading developer for the PostgreSQL project, and he's a co-founder at Neon, which The post Building PostgreSQL for the Future with Heikki Linnakangas appeared first on Software Engineering Daily.
Hey friends! Today Joe “The Machine” Skeen and I tackled GOAD (Game of Active Directory) again – this time covering: SQL link abuse between two domains Forging inter-realm TGTs to conquer the coveted sevenkingdoms.local! Join us next month when we aim to overtake essos.local, which will make us rulers over all realms!
Talk Python To Me - Python conversations for passionate developers
Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Guests: Paul on X: @paulweveritt Paul on Mastodon: @pauleveritt@fosstodon.org Dave Peck on Github: github.com Jim Baker: github.com PEP 750 – Template Strings: peps.python.org tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org How To Teach This: peps.python.org PEP 501 – General purpose template literal strings: peps.python.org Python's new t-strings: davepeck.org PyFormat: Using % and .format() for great good!: pyformat.info flynt: A tool to automatically convert old string literal formatting to f-strings: github.com Examples of using t-strings as defined in PEP 750: github.com htm.py issue: github.com Exploits of a Mom: xkcd.com pyparsing: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy
In this episode, Dr. Bill Keith breaks down the exact AI tools he's using to streamline everything from onboarding new patients to managing phone calls and analyzing data from Open Dental. He shows how tools like ChatGPT, Canva, and a custom voice assistant named Abby are changing the game in patient communication, scheduling, insurance verification, and marketing—without needing a tech background or a programming degree. From writing SQL queries in seconds to having AI bots summarize 200-page legal contracts, this is a tactical breakdown of how to bring cutting-edge automation into a dental practice today. EPISODE RESOURCES https://www.truedentalsuccess.com Dental Success Network Subscribe to The Dentalpreneur Podcast
Dr. Bill Keith opens the doctor's session by pulling back the curtain on how a 20-op, high-efficiency practice outside Kansas City is using AI to run tighter systems, make faster decisions, and actually reduce day-to-day chaos. With a background in accounting and finance. Dr. Keith breaks down complex concepts like SQL queries, Open Dental integration, and real-time marketing automation into clear, actionable takeaways for any owner-operator looking to level up their tech stack. This talk covers everything from building a custom AI receptionist to analyzing big data in minutes with ChatGPT. EPISODE RESOURCES https://www.truedentalsuccess.com Dental Success Network Subscribe to The Dentalpreneur Podcast
You're outsourcing your brain to AI. Bad idea?AI can write your SQL queries. Build your dashboards. Even brainstorm your next big idea.It's saving you hours. Maybe days.But here's the catch—it's also stealing your critical thinking. Making you reliant.Maybe even... dumber.Sumit Gupta knows this first-hand. He's built data strategies at Notion, Snowflake, and Dropbox. And, he's here to break down how AI is both supercharging productivity and quietly eroding our problem-solving skills.Are we trading our brains for convenience? Let's find out.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the conversation and ask Jordan and Sumit questionsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Personal use of Generative AI and productivity vs. intelligence dichotomyIntroduction of Sameet Gupta as a guestRole and experience of Sameet Gupta at NotionThe impact of AI on productivity and critical thinkingExamples of AI tools used by Sameet GuptaChallenges of balancing AI use with retaining critical skillsPotential risks and costs of over-reliance on AIWhite coding and its implicationsRecommendations and personal strategies to maintain skills alongside AI useThe influence of AI on different age groups, particularly studentsDiscussion on cost implications of using AI improperlyNotion's capabilities in enhancing productivity and retentionThe future impact of AI on knowledge workers and the workforcePractical advice for business leaders on AI integration and maintaining productivityTimestamps:00:00 "Using AI to Stay Sharp"06:02 Streamlining Dashboards with AI8:48 "GPT for Quick Code Debugging"12:34 Guardrails Needed for Costly AI Mistakes15:27 AI for Repetitive Tasks18:35 Growing Business with AI Expertise21:20 AI's Impact on Younger Generation26:04 AI's Impact on Future Workforce28:12 "Notion: Beyond Note-Taking"31:22 "Validate or Lose Job Security"32:25 Balancing Productivity and UniquenessKeywords:Generative AI, large language models, productivity, dumber, balance, knowledge work, NVIDIA conference, GTC, OpenAI, advanced AI models, voice models, transcription, text to speech, API, real-time streaming, customizable voice presets, word error rate, noisy environments, 100 plus languages, competition, Gmail, Google, AI-powered search, email results, keyword search, Amazon, Claude, real-time access, web search feature, AI assistant, misinformation, AI hallucinations, Brian, Midroll, NSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner
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