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
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/
In my newsletter last week, I wrote "Data’s still a mess. Most data initiatives fail. Data teams are seen as a cost center and not getting the support they deserve. Same as it ever was."
Here, I unpack those four sentences. Data teams need to stop stop playing to not lose. Instead, they need to play to win!
Navnit Shukla is a solutions architect with AWS. He joins me to chat about data wrangling and architecting solutions on AWS, writing books, and much more.
Navnit is also in the Coursera Data Engineering Specialization, dropping knowledge on data engineering on AWS. Check it out!
Data Wrangling on AWS: https://www.amazon.com/Data-Wrangling-AWS-organize-analysis/dp/1801810907
LinkedIn: https://www.linkedin.com/in/navnitshukla/
I've spent the last three weeks visiting the UK, Australia, and New Zealand. Here are my observations and anecdotes about the data and ML/AI industry from countless chats with executives, practitioners, and pundits.
Ilya Reznik has been in the ML game for ages, having worked at Adobe and Twitter and led teams at Meta, among others.
We chat about leading ML teams, AI today, creating content, and much more.
LinkedIn: https://www.linkedin.com/in/ibreznik/
As I travel this Fall, I'm reminded that most people don't work at fancy tech companies. Most people work at traditional companies with "boring" data and tech stacks. And that's OK. Boring is good.
Jordan Morrow has written a ton, including four books. We chat about the process of writing books, the ins and outs of working with a publisher, the role of AI in writing, and much more. If you're interested in writing a book, this is a crash course in what you should know. Enjoy!
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