This morning, a great article came across my feed that gave me PTSD, asking if Iceberg is the Hadoop of the Modern Data Stack?

In this rant, I bring the discussion back to a central question you should ask with any hot technology - do you need it at all? Do you need a tool built for the top 1% of companies at a sufficient data scale? Or is a spreadsheet good enough?

Link: https://blog.det.life/apache-iceberg-the-hadoop-of-the-modern-data-stack-c83f63a4ebb9

❤️ If you like my podcasts, please like and rate it on your favorite podcast platform.

🤓 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/

Health insurance denials are a big topic in America right now. Holden Karau joins me to talk about using AI to appeal health claim insurance denials.

Fight Health Insurance: https://fighthealthinsurance.com/

I’ve been asked many questions about building a personal brand for the last few weeks. Perhaps it’s the uncertain job market, people wanting to branch out, or something else. I’m unsure what’s in the air right now.

In this episode, I share some thoughts on building a personal brand.

Hannes Muhleisen is the creator of DuckDB and CEO of DuckDB Labs. We finally got a chance to meet in person at the Forward Data Conference in Paris. We hit it off immediately, and at times, I felt like I was talking with my long lost brother. Hannes is a very cool guy!

While at the conference, we recorded a chat about all things DuckDB, the challenges of data lakehouses and open table formats, local-first tech, and much more. 🦆 🐥

Dave and Johnny run Estuary, a data integration company focused on real-time ETL and ELT. We're also friends, so we decided to have a chat.

In this episode, we chat about the current state of the data integration space, running a startup while raising kids, and much more.

Estuary

Regular guest Gordon Wong joins me for a half hour to chat about tech stacks for analytics, semantic layers, and much more.

Gordon's LinkedIn: https://www.linkedin.com/in/gordonhwong/

Bill Inmon is considered the father of the data warehouse. I just got back from spending a couple of days with Bill, and we discussed the history of the data industry and the data warehouse. On my flight back, I realized people could benefit from a short version of our conversation.

In this short chat, we discuss what a data warehouse is (and is not), Kimball and Inmon, the origins of the data warehouse, and much more.

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

For decades, siloed data modeling has been the norm: applications, analytics, and machine learning/AI. However, the emergence of AI, streaming data, and “shifting left" are changing data modeling, making siloed data approaches insufficient for the diverse world of data use cases. Today's practitioners must possess an end-to-end understanding of the myriad techniques for modeling data throughout the data lifecycle. This presentation covers "mixed model arts," which advocates converging various data modeling methods and the innovations of new ones.

Speaker: Joe Reis Author Nerd Herd

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

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/