The Joe Reis Show

It's all about acquisitions, acquisitions, acquisitions! Matt Housley joins me to tackle the biggest rumor in the data world this week: the potential acquisition of dbt Labs by Fivetran. This news sparks a wide-ranging discussion on the inevitable consolidation of the Modern Data Stack, a trend we predicted as the era of zero-interest-rate policy ended.We also talk about financial pressures, vendor exposure to the rise of AI, the future of data tooling, and more.

In this episode, I sit down with Saket Saurabh (CEO of Nexla) to discuss the fundamental shift happening in the AI landscape. The conversation is moving beyond the race to build the biggest foundational models and towards a new battleground: context. We explore what it means to be a "model company" versus a "context company" and how this changes everything for data strategy and enterprise AI. Join us as we cover:Model vs. Context Companies: The emerging divide between companies building models (like OpenAI) and those whose advantage lies in their unique data and integrations.The Limits of Current Models: Why we might be hitting an asymptote with the current transformer architecture for solving complex, reliable business processes. "Context Engineering": What this term really means, from RAG to stitching together tools, data, and memory to feed AI systems. The Resurgence of Knowledge Graphs: Why graph databases are becoming critical for providing deterministic, reliable information to probabilistic AI models, moving beyond simple vector similarity. AI's Impact on Tooling: How tools like Lovable and Cursor are changing workflows for prototyping and coding, and the risk of creating the "-10x engineer." The Future of Data Engineering: How the field is expanding as AI becomes the primary consumer of data, requiring a new focus on architecture, semantics, and managing complexity at scale.

In this episode, I sit down with Ole to discuss his new book, "Fundamentals of Metadata Management." We move past the simple definition of "data about data" to a more nuanced view of metadata as something that exists in two places at once , serving as a pointer to find information elsewhere.Ole introduces his core concept of the "MetaGrid"—the interconnected, yet siloed, web of metadata repositories that already exists within every large organization across various teams and technologies. He argues that the key to better metadata management is not to build a new monolithic system but to recognize, document, and integrate the MetaGrid that's already there, hiding in plain sight.The conversation also covers the impact of the AI hype cycle , the lessons learned from the Data Mesh movement , the sociological incentives that help or hinder metadata projects , and the cultural clash between the worlds of data engineering and library science.

The way we work is changing right in front of us. In this rant, I talk about how I'm seeing AI reshape how technical and non-technical people do their work. The bottom line - there's a lot of room to innovate and evolve your job.

In this discussion, I sit down with data veterans Remco Broekmans and Marco Wobben to explore why so many data projects fail. They argue that the problem isn't the technology, but a fundamental misunderstanding of communication, culture, and long-term strategy.The conversation goes deep into the critical shift from being a "hardcore techie" to focusing on translating business needs into data models. They use the classic "involved party" data modeling pattern as a prime example of how abstract IT jargon creates a massive disconnect with the business.Marco shares a fascinating (and surprising) case study of the Dutch Railroad organization, which has been engaged in an 18-year information modeling "program" - not a project - to manage its immense complexity. This sparks a deep dive into the cultural and work-ethic differences between the US and Europe, contrasting the American short-term, ROI-driven "project" mindset with the European capacity for long-term, foundational "programs".Finally, they tackle the role of AI. Is it a silver bullet or just the latest shiny object? They conclude that AI's best use is as an "intern" or "assistant", a tool to brainstorm, ask questions, and handle initial prototyping, but never as a replacement for the deep, human-centric work of understanding a business.Timestamps:00:00 - Introduction01:09 - Marco Wobben introduces his 25-year journey in information modeling.01:56 - Remco Broekmans reintroduces himself and his focus on the communication aspect of data.03:22 - The progression from hardcore techie to focusing on communication over technology.08:16 - Why is communication in data and IT projects so difficult? 09:49 - The "Involved Party" Problem: A perfect example of where IT communication goes wrong with the business.13:35 - The essence of IT is automating the communication that happens on the business side.18:39 - Discussing a client with 20,000 distinct business terms in their information model.21:55 - The story of the Dutch Railroad's 18-year information modeling program that reduced incident response from 4 hours to 2 seconds.27:25 - Project vs. Program: A key mindset difference between the US and Europe.34:18 - The danger of chasing shiny new tools like AI without getting the fundamentals right first.39:55 - Where does AI fit into the world of data modeling? 43:34 - Why you can't trust AI to be the expert, especially with specialized business jargon.47:18 - The role of risk in trusting AI, using a self-driving car analogy.53:27 - Cultural differences in work pressure and ethics between the US and the Netherlands.59:29 - Why personality and communication skills are more important than a PhD for data modelers.01:03:38 - What is the purpose of an AI-run company with no human benefit? 01:11:21 - Using AI as an instructive tool to improve your own skills, not just to get an answer.01:14:12 - How AI can be used as a "sidekick" to ask dumb questions and help you think.01:18:00 - Where to find Marco and Remco online

Are you a giver or a taker?It seems like every few months, I have to put out a PSA about how I get annoyed at unsolicited pitches from people who are one-sided and transactional. These people are takers. I've got no time for takers.Instead, pay it forward. Give away your ideas in free articles and videos. Mentor people. Create an open source project. Be a friend and a human without asking anything in return. Be a giver.

In this episode, I sit down with Wendy Turner-Williams, a distinguished tech leader and executive with a deep history at companies like Microsoft and Salesforce. She's of the original minds behind what became Azure Data Factory, among other foundational tech. In this wide-ranging conversation, Wendy charts the trajectory from the early days of the Internet to the current AI-driven hype cycle and looming crisis. She explains how these tools of innovation are now being turned against the workforce and why this technological revolution is fundamentally more disruptive than anything that has come before. This episode is a candid, unfiltered discussion about the real-world impact of AI on jobs, the economy, and our collective future, and a call for leaders to act before it's too late.Timestamps:00:22 - Catching up: The tough job market and writing new books. 05:49 - Wendy's impressive career history at Microsoft, Salesforce, and Tableau. 06:17 - The origin story of Azure Data Factory and other foundational projects at Microsoft. 09:18 - A personal story about the challenges of being a woman in Big Tech in the early days. 13:02 - A look back at a favorite early-career project: Digitizing physical maps with nascent GPS technology in 2001. 18:11 - The state of the tech industry: "Tech is cannibalizing itself because of AI." 20:31 - The massive, impending shock to the job market and why AI is different from previous industrial revolutions.27:26 - Why the "human in the loop" is a temporary and misleading solution. 29:55 - Breaking down the numbers: The staggering quantity of white-collar jobs projected to be eliminated. 36:37 - Why leaders are failing to act and conversations are happening behind closed doors without solutions. 38:25 - Discussing potential solutions: Should companies have quotas for their human workforce? 45:21 - The need for "truth tellers" and leaders who are willing to question the current path and drive human-centric transformation. 53:15 - The grim reality for recent graduates with computer science degrees who can't find jobs. 56:22 - The risk of IP hoarding and engineers deliberately crippling systems to protect their jobs.01:00:20 - Final thoughts: Are we waiting for a "let them eat cake" moment before we see real change?

Matt Housley joins me for our monthly chat. This time, we discuss my article The Pedantic Layer, fraud, and more.

There are very few people like Stephen Brobst, a legendary tech CTO and "certified data geek," Stephen shares his incredible journey, from his early days in computational physics and building real-time trading systems on Wall Street to becoming the CTO for Teradata and now Ab Initio Software. Stephen provides a masterclass on the evolution of data architecture, tracing the macro trends from early decision support systems to "active data warehousing" and the rise of AI/ML (formerly known as data mining). He dives deep into why metadata-driven architecture is critical for the future and how AI, large language models, and real-time sensor technology will fundamentally reshape industries and eliminate the dashboard as we know it. We also chat about something way cooler, as Stephen discusses his three passions: travel, music, and teaching. He reveals his personal rule of never staying in the same city for more than five consecutive days since 1993 and how he manages a life of constant motion. From his early days DJing punk rock and seeing the Sex Pistols' last concert to his minimalist travel philosophy and ever-growing bucket list, Stephen offers a unique perspective on living a life rich with experience over material possessions. Finally, he offers invaluable advice for the next generation on navigating careers in an AI-driven world and living life to the fullest.

The AI hype cycle seems to be calming down a bit, and this is awesome. Humanity always craves another hype cycle, so I give some ideas on what I think is next after AI.

Ever wonder how companies in the crowded data and AI space build powerful alliances to drive revenue and growth? In this episode, I sit down with Eleanor Thompson, a partnerships expert based in London and founder of a successful partnerships consultancy. Drawing from her experience running the partner program at Fivetran during its hyper-growth phase, Eleanor shares the essential strategies for building a successful partnership ecosystem from the ground up. We also also discuss the mental fortitude required for entrepreneurship, drawing surprising parallels between running a business and competing in high-intensity fitness events like Hyrox. Tune in to learn:The fundamental reasons why partnerships are critical for expanding your reach and generating revenue. When is the right time for a startup to focus on partnerships (hint: it's not day one). Eleanor's "4A" framework (Alignment, Ability, Audience, Accountability) for identifying the perfect partner. The key roles, from Partner Sales Engineers to Partner Ops, needed to build a successful partnerships team. Red flags to watch for when a potential partner is more focused on margin than customer value. How AI can be used to identify ideal partners and even predict their future success. Find Eleanor Thompson online:LinkedIn: Eleanor Thompson Website: https://branchworks.io #BusinessPartnerships #DataEngineering #AI #Entrepreneurship #Tech #Startup #GoToMarketTimestamps00:49 - Who is Eleanor Thompson? 02:25 - Why Do Business Partnerships Exist? 05:26 - When is the Right Time for a Company to Start Building Partnerships? 06:40 - The 4A Framework for Defining Your Ideal Partner Profile 08:20 - Joe's Experience Partnering with Big Tech 12:33 - How to Structure a Partnerships Team in a Growing Tech Company 20:49 - What is Partner Operations and Why is It a Critical Hire? 22:30 - The Importance of Trust and Referral Fees 25:15 - Eleanor's Journey as an "Accidental CEO" 30:10 - The Mental Fitness and Resilience Required to Be a Founder 41:20 - How to Use AI in Your Partnership Strategy 45:00 - How to Spot a Good Partner on the Very First Call 46:33 - Red Flags to Watch For in Potential Partners 51:35 - How Fitness and Hyrox Competitions Fuel Business Success 59:45 - Where to Find Eleanor Thompson

Is reality setting in for the AI bubble? Who the hell knows. This is definitely the most bipolar bubble I've ever seen, making the dotcom bubble look downright tame. In this rant, I discuss why AI is in its televangelist moment, and why a reality check is necessary to keep real progress on AI on track.

What are the hidden dangers lurking beneath the surface of vibe coded apps and hyped-up CEO promises? And what is Influence Ops?I'm joined by Susanna Cox (Disesdi), an AI security architect, researcher, and red teamer who has been working at the intersection of AI and security for over a decade. She provides a masterclass on the current state of AI security, from explaining the "color teams" (red, blue, purple) to breaking down the fundamental vulnerabilities that make GenAI so risky.We dive into the recent wave of AI-driven disasters, from the Tea dating app that exposed its users' sensitive data to the massive Catholic Health breach. We also discuss why the trend of blindly vibe coding is an irresponsible and unethical shortcut that will create endless liabilities in the near term.Susanna also shares her perspective on AI policy, the myth of separating "responsible" from "secure" AI, and the one threat that truly keeps her up at night: the terrifying potential of weaponized globally scaled Influence Ops to manipulate public opinion and democracy itself.Find Disesdi Susanna Cox:Substack: https://disesdi.substack.com/Socials (LinkedIn, X, etc.): @DisesdiKEY MOMENTS:00:26 - Who is Disesdi Susanna Cox?03:52 - What are Red, Blue, and Purple Teams in Security?07:29 - Probabilistic vs. Deterministic Thinking: Why Data & Security Teams Clash12:32 - How GenAI Security is Different (and Worse) than Classical ML14:39 - Recent AI Disasters: Catholic Health, Agent Smith & the "T" Dating App18:34 - The Unethical Problem with "Vibe Coding"24:32 - "Vibe Companies": The Gaslighting from CEOs About AI30:51 - Why "Responsible AI" and "Secure AI" Are the Same Thing33:13 - Deconstructing the "Woke AI" Panic44:39 - What Keeps an AI Security Expert Up at Night? Influence Ops52:30 - The Vacuous, Haiku-Style Hellscape of LinkedIn

What's the nature of skills and competency when we're all just AI enabled button pushers? I rant about why we need to retain our sense of competence and uniqueness in an age where everything is rapidly becoming a sea of sameness and lameness.

Is AI the silver bullet for modernizing our aging software systems, or is it a fast track to creating the next generation of unmaintainable "slopware"?In this episode, I sit down with Marianne Bellotti, author of the amazing book "Kill It With Fire," to discuss the complex reality of legacy system modernization in the age of AI. We explore why understanding the cultural and human history of a codebase is critical, and how the current AI hype cycle isn't a silver bullet for legacy IT modernization efforts.Marianne breaks down a recent disastrous "vibe coding" experiment, the risk of replacing simple human errors with catastrophic automated ones, and the massive disconnect between the promises of AI agents and the daily reality of a practitioner just trying to get a service account from IT.Join us for a pragmatic and no-BS conversation about the real challenges in software, the practical ways to leverage LLMs as an expert partner, and why good old-fashioned systems thinking is more important than ever.Find Marianne Bellotti:Socials: @BellmarWebsite: https://belladotte.tech/Book, "Kill It With Fire": https://nostarch.com/kill-it-fire

Matt Housley joins me to chat about whether it matters that AI is PhD level, clanker content (the new term for AI slop), a retrospective on Fundamentals of Data Engineering, and much more.

Shane Gibson just published a book - The Information Product Canvas. We discuss his journey as an author and publisher, why shared language is critical in data projects, the iterative processes that can enhance data team efficiency, and the role of canvases in business strategy. We also get into how AI might evolve the landscape of data and publishing.

Does today's use of AI coding agents remind you of a drunken high school or college party? Just like people discovering drugs and alcohol for the first time, I feel like the tech and data industry is in a similar place. "Just vibe..." is the mantra now.But when I talk with developers and data practitioners in private, I get different vibes. There's definitely a concern that we're collectively building lots of slopware and are setting ourselves up for trouble as an industry.

Kostas Pardalis (Co-Founder @ Typedef) joins me to chat about the rapid evolution of AI and data infrastructure, next generation architectures, and much more

In this episode, I unpack how Big Tech is using massive AI investment to justify mass layoffs. All of this AI projects keep failing for the same reasons every other tech wave and IT project has: bad data, org problems, etc. Will AI be different?