Stay ConnectedI’m Joe Reis, co-author of Fundamentals of Data Engineering, creator of Mixed Model Arts, international speaker, and long-time observer of the tech industry’s favorite recurring mistakes.
Weekly broadcasts from somewhere on the road. Expect raw solo rants and long-form conversations with the engineers and leaders actually building things. I dive into data, AI, architecture, and career survival.
This podcast is invitation-only. Unsolicited guest pitches and PR outreach are not considered.
Copyright: © Joe Reis
It's been four years and a day since the Fundamentals of Data Engineering was published. The big mental models of the book are the Data Engineering Lifecycle and its Undercurrents. I also discuss what would change about the book if I write it again today.
Thanks to everybody who has supported the Fundamentals of Data Engineering over the years. It's super cool to see the massive impact that the book has had on the data industry and its practitioners around the world.
Are AI agents replacing traditional data engineering tasks?
I sat down with Matt Glickman, CEO & Co-founder of Genesis Computing and former Snowflake and Goldman Sachs executive, for a conversation about where data engineering is actually heading. Most organizations today are stuck in "demo mode" or using LLMs as expensive code completers, simply writing code faster to feed legacy data architectures. But if you look at the industry from first principles, humans were never meant to be data machinery.
In this episode, Matt and I break down how true agentic data systems work, why current enterprise architectures will fail under agent-scale query volumes, and what it really takes to capture institutional knowledge before it leaves the building.
We also cover Matt’s front-row seat at Goldman Sachs during the 2008 financial crisis, his early days turning Snowflake into an enterprise giant, and how AI-native companies will outpace traditional enterprises. If you're building, architecting, or leading teams in the AI and data space, this is a conversation you don't want to miss.
The Joe Reis Show is invitation-only. Unsolicited guest pitches and PR outreach are not considered.
Here's a new show where I quickly cover a mental model that applies to your work as a data leader or practitioner.
Kicking things off is Conway's Law.
It's been a crazy summer for AI. AI enthusiasm remains high, but the market is starting to ask far harder questions. Semiconductor stocks are in a bear market. IBM’s shares fell sharply. Data-center projects are facing political and community resistance. Enterprises are adopting AI, but many are struggling to turn experimentation into measurable returns.
In this Freestyle Friday episode, I look at why these signals do not necessarily mean an AI winter. Instead, they suggest that AI is moving from a "possibility market" into an "accountability market", where capability, economics, infrastructure, and organizational reality finally have to reconcile. Put another way, things are going to get real.
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Sponsor: Fivetran
With the rise of AI and agents, having centralized, trustworthy data is the absolute foundation for building tools and training models. Fivetran automates your data pipelines, removing the need to build fragile connectors so your data arrives clean and reliable. By handling the messy infrastructure behind the scenes, Fivetran allows your team to focus on building the future. Visit fivetran.com to learn more.
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Sponsor: Revefi
Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure. Start saving money today at revefi.com/ai-dba
A short rant based on my new article this week, "The Database Is Not the Data Model"
"In discussions with data practitioners, I keep seeing the same confusion. Someone pulls up a DDL file, a folder of dbt or SQL, or an ERD reverse-engineered from a Postgres instance and says: “Here’s our data model.”
Not to be pedantic, but that’s a schema. Schemas are great. But data modeling is more than just schema design."
Let’s dive into the difference in this podcast and the article.
Article: https://practicaldatamodeling.substack.com/p/the-database-is-not-the-data-model
In this episode, I chat with Steve Brown, an AI futurist, independent consultant, and former professional at Intel and Google DeepMind. Steve shares his insights on helping global leadership teams decode the future of artificial intelligence.
We dive into the common pitfalls of corporate AI adoption, including why so many AI initiatives fail due to poor communication and a lack of cultural integration.
Steve outlines a definitive three-step path to AI transformation, emphasizing the critical difference between 20th-century cost-cutting and 21st-century labor amplification. The conversation also explores how AI is reshaping middle management, the distinct differences between "AI-first" and "AI-native" businesses, and the staggering acceleration of physical AI and humanoid robotics.
In this Freestyle Friday episode, I break down results from the June 2026 Pulse Survey on organizational dysfunction among data engineers. I also dig into why data product ownership (or lack thereof) is one of the fundamental issues standing between companies and success with data and AI.
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Sponsor: Fivetran
With the rise of AI and agents, having centralized, trustworthy data is the absolute foundation for building tools and training models. Fivetran automates your data pipelines, removing the need to build fragile connectors so your data arrives clean and reliable. By handling the messy infrastructure behind the scenes, Fivetran allows your team to focus on building the future. Visit fivetran.com to learn more.
-----------------------
Sponsor: Revefi
Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure. Start saving money today at revefi.com/ai-dba
I walk through exactly how I use AI for idea generation, review, editing, and drafting on my upcoming book, Mixed Model Arts. This is also the first in a new Wednesday format where I answer listener questions about what I'm building.
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Mixed Model Arts will be out soon. If you want to get a good deal on the e-book and on-demandcourse, please go to Practical Data Modeling.: https://practicaldatamodeling.substack.com/
Kirill Bobrov, a senior data engineer at Spotify and author of the blog Luminousmen, joins me to talk about his viral "Drunk Post" repost, whether the AI boom is real infrastructure or another bubble, why engineering judgment can't be automated away, and the process of writing a technical book on concurrency.
Luminousmen: https://luminousmen.com/
With AI reshaping employer expectations, junior candidates in data are facing real anxiety about the job market. I share the technical and personal skills I'd focus on, my top five traits I look for in a candidate, and how to build a network from scratch.
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Sponsor: Revefi
Save serious money on your cloud costs with Revefi’s new autonomous AI DBA, a tool built to handle the gritty reality of cloud data management so you can stop babysitting your infrastructure.
With a five-minute, zero-touch setup, it deploys 18 specialized agents across your data estate to automatically manage FinOps, performance tuning, and data quality. If you want to cut your cloud costs by 30% to 70% and get back to actual data architecture, check out what they are building at revefi.com/ai-dba
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