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
Michel Tricot (CEO of Airbyte) joins me to chat about the impact of AI on the modern data stack, ETL for AI, the challenges of moving from open source to a paid product, and much more.
Airbyte & Pinecone - https://airbyte.com/tutorials/chat-with-your-data-using-openai-pinecone-airbyte-and-langchain
Note from Joe - I had audio issues cuz he got a new computer and didn't use the correct mic :(
Juan Sequeda and I chat about knowledge graphs (he's an OG in this area), the potential of LLMs on structured datasets, and much more. This is an honest, no-BS chat about the transition from a data-first world to a knowledge-first world. Enjoy!
LinkedIn: https://www.linkedin.com/in/juansequeda/
data.world: https://data.world/product/
website: https://www.juansequeda.com/
Nowadays, it seems like lots of people want to become data influencers and content creators. Ravit Jain and I chat about how to build an audience, the power of LinkedIn, and much more.
The Ravit Show: https://theravitshow.com/
LinkedIn: https://www.linkedin.com/in/ravitjain/
Boring is back. As technology makes the lives of data engineers easier with respect to solving classical data problems, data engineers can now move to tackle "boring" problems like data contracts, semantics, and higher-level and value-add tasks. This also sets us up to tackle the next generation of data problems, namely integrating ML and AI into every business workflow. Boring is good.
Medical data is a mess. Aaron Neiderhiser and Coco Zuloaga from Tuva Health are here (at my house) to answer why this data is insanely complex, and what can be done to fix it. They take a first-principles approach that you might find interesting.
In my travels and virtual conversations with data teams and practitioners around the world, the same thing keep popping up - data teams feel misunderstood and under-appreciated. If we're going to make progress as an industry, it's time to stop playing defense, and start playing offense.
David Foster just published the 2nd edition of his amazing book, Generative Deep Learning (O'Reilly 2023). We chat about a lot - running a consultancy, all things writing, the impact of AI on kids, why in-person events matter more than ever, and much more.
David's LinkedIn: https://www.linkedin.com/in/davidtfoster/
Book (Amazon): https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947
Applied Data Science Partners: https://adsp.ai/
The massive attention on AI means the data industry has a shot at finally succeeding with the things we've been struggling with for decades - "adding value", data quality and modeling, governance, etc. For AI to work, these things need to properly function. But are we up for the task? And if we can't get it right NOW, when?
Whenever Kevin and I get together, we "nerd snipe" each other. This conversation is no different, and it's a wide-ranging conversation about how the data landscape evolves alongside LLMs, education, startup mentorship, and the possible (looming?) startup mass extinction.
Kevin's LinkedIn: https://www.linkedin.com/in/kevinzenghu/
Metaplane: https://metaplane.dev/
Matt Housley hangs out at my house and we chat about Data NIMBYism and gatekeeping. What is that? Listen and find out.
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