Vishnu Vasanth (e6Data) and I chat about what's next for analytical query engines, shifting left, the Indian tech scene, and much more.

Vishnu is very wise and has a very deep technical vision for where the industry needs to go. I very much agree with his vision. Enjoy!

e6Data: https://www.e6data.com/

LinkedIn: https://www.linkedin.com/in/vishnu-vasanth-5329233/

There's the interview you think you're going to have, then there's the interview you get. This is one of those, in the best way possible. I expected to chat about his time at Snowflake. We didn't even get past his early days building data warehouses because it was so fascinating. Did you know Kent is arguably one of the very first practitioners (probably an accidental inventor) of DataOps?

This is sort of a "prequel" episode. Kent Graziano and I chat about his early days as a data practitioner.

Sometimes I feel like the data world is stuck in a world of tabular data (rows and columns). This has been the data world for decades. Let's think bigger. We've moved beyond data fitting into lakes.

With the capability of AI to unlock the power of unstructured data (audio, images, video), it's time to start thinking about data oceans...

Keith Belanger is an OG data modeling practitioner, having been in the game for decades.

We chat about a wide range of data modeling topics.

What's changed and what's stayed the same? How to model data to fit the business's needs. Agile data modeling. When it works, when it doesn't. Data modeling for data mesh and decentralization. The art of data modeling How to teach conceptual data modeling to new practitioners

Keith brings a wealth of experience and a practical, no-nonsense perspective. If you're interested in data modeling, don't miss this!

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

This morning, the Practical Data Modeling Community held its first group discussion (to be posted very soon). People from all sorts of organizations (biggest companies in the world, universities, small companies) discussed how the approach analytical data modeling.

My major takeaway - your mileage will vary. There's the ideal way of data modeling we're taught, and there's reality. Everyone's situation is different and there's no one-size-fits-all approach that will work for everyone.

The discussion was awesome, and we'll do it again soon. If you're not part of the Practical Data Modeling Community, please join here: https://practicaldatamodeling.substack.com/

Kishore Aradhya and I both teach, and we agree this is a very difficult landscape to determine what and how to teach. Against the backdrop of generative AI, we discuss the role of universities in teaching tech and data, the role of a teacher, how to teach data, and much more.

DSPY - https://github.com/stanfordnlp/dspy

Toby Mao started his data tooling company, SQLMesh, in 2022, when investing in data tools was unfashionable. Yet, he's managed to get traction with SQLMesh and is on a mission to simplify data transformations and make data easier to work with. We also chat about experimentation best practices, which he learned at some of the biggest tech companies in the world.

This is definitely a great episode if you're interested in startups, data tools, experiments, driving cars, and much more.

Matt Housley hangs out at my house, and we have a random chat about all sorts of stuff - fads in data, data and ML engineering, tech hubs, and more. If you want a glimpse into the sorts of chats that Matt and I have all the time, here you go.

There's an inverse relationship between the value you add and how much you need to tell people about it. If you're adding value, you'll know - you don't need to talk about it. You're doing it. Also, the same goes with "data." If you're putting "data" as the center of the conversation, you just lost the game.

Angel Narciso and I hung out at LEAP Riyadh, alongside 215K attendees (wtf?). We chat about all sorts of stuff in the data world, including some blunt convos on the modern data stack and AI, among other things.

I often get questions about how I write and advice on how one might go about becoming a "writer." In this episode, I talk a bit about my writing process and why you (yes you) should also write.

This will be the first in a few episodes and blog posts where I talk about the writing and content creation process, as I get a ton of questions about this. Thanks for your questions and support!

Jess Haberman and I chat about how to negotiate a book deal. She's been in publishing for ages and knows her stuff!

Also, I wish I had this episode handy while I was shopping around Fundamentals of Data Engineering, because Jess agreed to publish my book while she was at O'Reilly ;)

We also talk about how AI will change publishing.

Had a great chat with Keith Belanger yesterday (podcast dropping soon) about how conceptual data modeling fell by the wayside. All too often, people seem focused on physical data modeling. This is a shame, because conceptual is the art and lifeblood of data modeling. As an industry, we need to learn to see (again).

Sadie St. Lawrence chat about all sorts of stuff - mind and machines, community building, optimizing time, focus, and social capital, and much more.

Women in Data: https://www.womenindata.org/

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

Is it better to make a perfect or good enough data model? It depends…

Christian Bourdeau and I chat about all things data careers - getting hired, getting fired, and finding your gig. We also chat about 75 Hard, lifting weights (we're bros), hackathons, teaching, and much more.

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

Zach Zeus and I chat about trust architecture and how it can work to improve ESG impacts in supply chain. This is an incredibly important topic with massive global impact, cuz climate change.

LinkedIn: https://www.linkedin.com/in/zachary-zeus/

Recommendation 49: https://unece.org/circular-economy/news/unece-support-scaling-transparency-sustainable-value-chains

I'm chilling in Verbier, Switzerland at Skiers in Data (SKID). In this episode, I chat about the various types of debt - technical, data, and organizational debt.

Annie Nelson and I chat about her path to data analytics, writing her new book, "How to Become a Data Analyst", bad career advice, rock climbing, and more.

LinkedIn: https://www.linkedin.com/in/annie-nelson-analyst/

TikTok: https://www.tiktok.com/discover/annie-nelson-data-analytics

Book: https://www.amazon.com/How-Become-Data-Analyst-Low-Cost/dp/1394202237

Christophe Blefari and I chat about why teaching data engineering is so damn hard, how generative AI will change technology and data education, and more.

Site: https://www.blef.fr/

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

Imagine you're dropped into the middle of a failed data project - no data team, no documentation, and other horrific things - and have to figure out a way to make it work. What would you do?

Gordon Wong and I chat about various aspects of how we'd handle this scenario.

Alex Freberg, aka Alex The Analyst, chats with me about playing the long game with content, empathizing with his audience, how he grew a massive YouTube following, his new Analyst Builder courses, and much more.

YouTube: https://www.youtube.com/@AlexTheAnalyst

LinkedIn: https://www.linkedin.com/in/alex-freberg/

Analyst Builder: https://www.analystbuilder.com/

Wendy Turner-Williams joins me to chat about her new project and communty, The Association.ai, unleashing generative AI in organizations, starting and building a community, and much more.

LinkedIn: https://www.linkedin.com/in/wendy-turner-williams-8b66039/

The Association: https://theassociation.ai/

My voice is sort of working, and I chat about Tristan Handy's article that raised quite a ruckus this week, "Is the "Modern Data Stack" Still a Useful Idea?"

In the end, the Modern Data Stack won - people use the cloud for analytics. And everything ends, so I'm excited for what's next.

Article: https://roundup.getdbt.com/p/is-the-modern-data-stack-still-a?r=oc02

Randy Bean and I discuss why generative AI is making companies more data-oriented, the latest Wave Stone Data and AI Leadership Executive Survey, his career and writing process and much more.