Chris Riccomini and I chat about building his latest project SlateDB, building data intensive infrastructure, writing, investing, and much more.

In this episode, I have a chat with Antti Rask, Juha Korpella, Niko Korvenlaita, Russell Willis, and Kosti Hokkanen. We chat about data, startups, and business in Finland and Europe.

Let's do things the right way, not just the fast way.

My works:

📕Fundamentals of Data Engineering: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/

🎥 Deeplearning.ai Data Engineering Certificate: https://www.coursera.org/professional-certificates/data-engineering

🔥Practical Data Modeling: https://practicaldatamodeling.substack.com/

🤓 My SubStack: https://joereis.substack.com/

I speak at a lot of conferences, and I've lost track of how many questions I've answered. Since conferences are top of mind for me right now, here are some tips for asking good (and bad) questions of speakers.

My works:

📕Fundamentals of Data Engineering: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/

🎥 Deeplearning.ai Data Engineering Certificate: https://www.coursera.org/professional-certificates/data-engineering

🔥Practical Data Modeling: https://practicaldatamodeling.substack.com/

🤓 My SubStack: https://joereis.substack.com/

Wes McKinney and I chat about Positron, Arrow, how he created Pandas and Arrow, and what makes him tick.

I've seen a TON of horror stories with tech debt and code migrations. It's estimated that 15% to 60% of every dollar in IT spend goes toward tech debt (that's a big range, I know). Regardless, most of this tech debt will not be paid down without a radical change in how we do things. Might AI be the Hail Mary we need to pay down tech debt? I don't see why not...

My works:

📕Fundamentals of Data Engineering: https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/

🎥 Deeplearning.ai Data Engineering Certificate: https://www.coursera.org/professional-certificates/data-engineering

🔥Practical Data Modeling: https://practicaldatamodeling.substack.com/

🤓 My SubStack: https://joereis.substack.com/

Anne-Claire Baschet and Yoann Benoit recently wrote a wonderful article called The Data Death Cycle, which describes the feedback loop of doom that many data teams find themselves in. Here, we discuss the Data Death Cycle in detail.

Article: https://medium.com/craftingdataproducts/the-data-death-cycle-6b10ef261d8e

Larry Burns and I chat about all things data teams—how they fail, their challenges, and how they can add value. To add value, we need to reimagine not only how we think about data but also how we manage knowledge.

Larry brings a fresh and battle-worn perspective to the data field, and if you work on or manage a data team, this conversation is worth a listen.

LinkedIn: https://www.linkedin.com/in/larryburnsdba/

This week I posted about how some major conferences charge a bunch of money for tickets and sponsorship, but don't pay speakers. As a speaker, I find this unethical and exploitative. Here, I unpack my thoughts on speaking at conferences. If you're a speaker, or want to become one, this is worth your time to listen.

My post: https://www.linkedin.com/posts/josephreis_this-morning-i-had-to-decline-a-speaking-activity-7252331326287011841-NPG6

Vijay Yadav (Director of Data Science at Merck) joins me to chat about a very interesting project he launched at Merck involving LLMs in production. A big part of this discussion is how to make data ready for generative AI.

This is a great example of an LLM-native use case in production, which are rare right now. Lots to learn from here. Enjoy!

LinkedIn: https://www.linkedin.com/in/vijay-yadav-ds/