Valentin Becerra and I chat about DJing, AI, space engineering, and much more.

Multi-tenancy in databases is very difficult to pull off at scale. Gwen Shapira and I chat about multi-tenant databases at Nile (and elsewhere), AI, RAG, and much more.

While at lunch with a friend today, the question came up of whether he should invest his time into content (videos and courses) or consulting. Having run a consultancy (and exiting the consulting game), I quipped that consulting often has a negative net present value. What do I mean? Listen on...

Note - I'm trying out a new format where I'll record and post episodes whenever I feel like it (novel idea). Not sure about the cadence yet, so stay tuned. This might mean that non-guest podcasts simply have a topic associated with the title.

What if the world's metadata were interconnected in a decentralized way? This is part of the vision of Ole Olesen-Bagneux's Meta Grid. We have a chat about what it is, and its implications for the data industry.

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/

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.

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!

Venkat Subramaniam is a programmer, author, speaker, and founder of Agile Developer, Inc. I've seen him speak several times, and was always blown away by his passion and technical depth. So, I was excited to have him on the podcast.

We chat about agile development in the real world, learning to do less, and much more. Venkat is extremely wise, and I very much enjoyed our discussion. Enjoy!

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

Twitter: https://x.com/venkat_s

Uncle Rico is a character in the movie Napoleon Dynamite, who is stuck in the past, reminiscing about his days as a high school football star. If only he'd won the game and went to the state championship. Some of the data industry reminds me of Uncle Rico.

During a recent panel, there was a question about whether AI can help with data management (governance, modeling, etc).

Some people were quick to dismiss this, saying that machines are no substitute for humans in their understanding and translating of "the business" to data.

Yet why are we still perpetually stuck in the mode of "80% of data projects fail"? Might AI/ML help data management move out of its rut? Or will it stay stuck in the past?

Also, please check out my new data engineering course on Coursera!

https://www.coursera.org/learn/intro-to-data-engineering

Paco Nathan is a national treasure. He's not only an OG in the field of AI, but he's also instrumental in early hacker and cyberpunk culture.

When I first met Paco, it suddenly clicked that I'd seen his name in various cyberpunk and alternative zines back in the 1990s. We have a chat all sorts of crazy stuff, and I feel like we only got to 5% of the stories..