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
We've got so many awesome tools and technology, but I often don't think we know how to properly use them. In this episode, I discuss what I think is one of the biggest problems in the data industry - the skills gap.
Albert and I discuss the do's and don't of finding a job in the data field. Do you do a portfolio project? What's the market like these days? This and more are discussed. Enjoy.
Tanya Bragin and I have a wide-ranging chat about the tension of open source and commercial products, Clickhouse, aligning marketing and product, and how she manages her time.
People often ask me for career advice. In a tough job market where people are sending out thousands of resumes and hearing nothing back, I notice a lot of people have weak networks and are unknown to the companies they're applying to. This results in lots of frustration and disappointment for job seekers.
Is there a better way? Yes. People need to know who you are. Obscurity is your enemy.
Also, the name of the Friday show changed because I can't seem to keep things to five minutes ;)
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/
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/
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