The Joe Reis Show

Salma Bakouk (CEO of Sifflet) and I discuss the evolving data and AI landscape, the rise of data observability in the age of AI, balancing personal and professional life as a founder, and much more.

Jordan Morrow and I have a lot of experience with public speaking. We get a lot of questions about tips for public speaking.

In this episode, we chat about how to get started with public speaking, tips for awesome presentations and talks, and much more.

Simon Späti and I discuss various aspects of writing, data engineering, and the impact of AI on the writing process. Simon shares his journey from business intelligence to data engineering and his current focus on writing. We also discuss the future of writing in the age of AI. Enjoy!

Todd Beauchene joins me to chat about being an early employee at a tiny startup called Snowflake, modern data platforms, and much more.

In this episode, I explain how I use (and don't use) AI for writing.

The Data Cynic, aka, Matthew Kelliher-Gibson, and I chat about all things cynical about the data field, working in Dilbert-land, negotiation for data professionals, and much more.

My semi-regular co-host and bestie Carly Taylor joins me once again. This time we chat about the true cost of replacing engineers with AI. We explore the implications of mass layoffs, the importance of company culture, and the potential for burnout among tech workers.

It seems like quite a few data practitioners I talk with are miserable. Forced to deliver things ever faster, there's not enough time to THINK. Instead, the pressure is to deliver "stuff" in two-week sprints. In this episode, I rant about why not everything needs to be a sprint, and why we need to treat Deep Work versus Delivery Work differently.

John Thompson joins me to chat about his upcoming book, The Path to AGI, his book writing process, and much more.

John Thompson LinkedIn: https://www.linkedin.com/in/johnkthompson/

Things aren't great in the world of data practitioners. Lots of technical debt and lack of pride in one's craft. The pressure to do more with less only compounds this problem. And AI will make it far worse.

If you think the amount of poorly made data architecture and models is bad now, I think we're in for another level very soon. With AI CoPilot on the rise, right as data skills and knowledge decline, I'm increasingly worried we'll offload our critical thinking and skills to AI. The outcome will be an ever expanding Great Pacific Garbage Patch of AI Data Slop.

Join Eric Broda and me for a fascinating discussion on the cutting edge of AI! We chat about the rapidly evolving world of AI agent ecosystems, exploring their potential to transform industries and reshape the future of work.Eric shares his hard won perspective on the challenges and opportunities facing consultancies in the age of AI, and why he believes AI agents could signal a significant shift in the consulting landscape. We also discuss the ins and outs of running a successful consultancy in today's dynamic market.Key topics covered:-The rise of AI agent ecosystems and their implications-How AI is disrupting the traditional consulting model-The future of consultancies in an AI-driven world-Insights into building and managing a successful consultancy-Whether you're an AI enthusiast, a consultant, or simply curious about the future of agents, this conversation with Eric Broda offers valuable insights and thought-provoking perspectives.#AIAgents#ArtificialIntelligence#Consulting#FutureOfWork#AI#Business#Innovation#Technology

Hugo Bowne-Anderson and I chat about the future of AI and automation, agents, and much more.

Ghalib Suleiman, Kevin Connolly, and I have all run data teams. In this episode, we chat about a bunch of data team "what ifs" - running a data team, hiring and firing, and executive commitments

With AI becoming engrained in nearly every product, there's still a BIG problem - AI and product teams have trouble working together. Whether it's different communication and working styles, or lack of a common language, AI teams stall because they can't align with Product.

Anne-Claire Baschet & Yoann Benoit join me to chat about unifying AI and Product Development.

Article: AI & Product: Two Development Cycles to Unify
https://craftingdataproducts.substack.com/p/ai-and-product-two-development-cycles

David Jayatillake joins me to chat about semantic layers, assessing value in data work, AI, and much more.

David's LinkedIn: https://www.linkedin.com/in/david-jayatillake/

In data modeling - and pretty much anything else - do you choose "old school" or "new school"? In other words, do you move slow and methodically or fast?

Evan Wimpey joins me to chat about working as a professional comedian, some Bayesian jokes, and other stuff that happened before ;)

Tony Baer, Matt Housley, and Juan Sequeda and I recap our thoughts on Data Day Texas 2025.

My "twin" Carly Taylor joins me to chat about careers, why Big Tech and Ivy League schools are overrated, and much more.

Carly's Substack: https://carlytaylor.substack.com/

LinkedIn: https://www.linkedin.com/in/carly-taylor-data/

Remco Broekmans and I chat about data modeling and the business, Data Vault, and using AI to accelerate data modeling.

It's Friday and Eevamaija Virtanen and I are hanging out at Data Day Texas in Austin.

In this episode, we chat about her upcoming talk, "Bridge Skills."

Chip Huyen joins me to chat about AI Engineering, AI Agents, and much more.

Jamie Davidson (Chief Product Officer at Omni, Former VP of Product at Looker) joins me to chat about "modern" data modeling, going from a startup to Google and back to a startup, and much more.

Omni: https://omni.co/

Burnout is a big topic right now. Carly Taylor and Ghalib Suleiman join me to chat about burnout and ways to deal with it.

Sarah Levy (CEO of Euno) joins me to chat about modern data governance for analytics.

Euno - https://euno.ai/

Carsten Bange (Founder & CEO of BARC) joins me to chat about trends in data, analytics, and AI.

BARC website: www.barc.com

Carsten on LinkedIn: https://www.linkedin.com/in/carsten-bange/

BARC Data Culture Summit: https://barc.com/events/data-culture-summit%20/

BARC DATAfestival: https://barc.com/events/data-festival/

Will AI chatbots replace old school BI dashboards and reports? Or can they co-exist with one another? Will AI help the often poor adoption by the business of dashboards and reporting? I rant about this and more in this latest episode.

Dave Colls and David Tan join me to chat about building effective machine learning teams, the challenges they face, the 7 deadly wastes in data and ML, writing a book, and much more.

Get their book here: https://amzn.to/3DS2OMp

It's 2025! We made it! ;)

In this podcast, I rant about why data modeling matters more than ever, AI, and why humans will seek out "human" things in 2025 and beyond.

❤️ Your support means a lot. Please like and rate this podcast 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/

Simba Khadder (CEO of FeatureForm) joins me to chat about feature stores, reinforcement learning, surfing, and much more.

It's December 31, 2024. Gordon Wong and I wrap up 2024 and chat about what we're excited about in 2025 in data and otherwise.

❤️ 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/

Matt Housley and I have a LONG chat about working in consulting, leaving your job, AI, the job market, our thoughts on what's coming in 2025, and much more.

❤️ 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/

The first time I met Amr Awadallah, he struck me as a rare person genuinely curious about the world and how technology and AI impact it.

We discuss his early roots as an entrepreneur, the founding of Cloudera and Vectara, the challenges of AI in enterprises, what makes humans unique, and much more.

I had an interesting conversation yesterday with a young gentleman upgrading my Google Fiber. While he was originally pursuing a career as a software developer, he and his friends decided against it after seeing the progress of ChatGPT over the last couple of years.

As a father of two teenage boys, I often think about the nature of work, including whether writing code will be relevant for future generations. Here, I rant at least part (not all) of what's on my mind. This is a big topic, and you'll see me ranting more about it.

Tiankai Feng joins me to chat about the people side of data, aligning on the value of data strategy, and much more.

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/

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!

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..

Bethany Lyons and I chat about disrupting the recruitment industry, startups, and the future of work.

Last week I talked about how good you have to be at your job. Yesterday's OpenAI announcement of it's "reasoning" model, o1, got me thinking about how good AI needs to be to do our jobs.

Ergest Xheblati is a data architect and author of Minimum Viable SQL Patterns. We chat about the opportunities and challenges of SQL, things that don't change in tech and data, writing and publishing books, and much more.

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

Other links: https://www.ergestx.com/links/

Dylan Anderson is a UK-based data strategist. We chat about bridging the gap between data and strategy, why talking about business value is a waste of time, and much more.

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

Substack: https://thedataecosystem.substack.com/

While speaking to one of my best friends, who's worked as a pilot for over 30 years, he mentioned that a "good" pilot doesn't crash the plane. In tech and data, "good" is viewed differently. How good you have to be at your job?

Jordan Tigani is back to chat about why small data is awesome, data lakehouses, DuckDB, AI, and much more.

Motherduck: https://motherduck.com/

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

Twitter: https://twitter.com/jrdntgn?lang=en

Demetrios Brinkmann is the co-founder of the massively global MLOps Community. We chat about AI hype vs reality, building a global tech community, and ROI of AI projects, and much more.

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

MLOps Community: https://mlops.community/

"Do you and Zach Wilson hate each other?"

I get asked questions like this, and it makes me laugh. We're good friends for the record. Most people play zero sum games, where one person wins and another loses. Questions like this got me thinking about how content creation is a positive sum game. You can consume content from many people, and this benefits everyone. Here, I unpack the differences of zero sum and positive sum games.

Vinoo Ganesh is an open source enthusiast and contributor, and a data and ML engineer. We chat about strong open source communities, LLMs and AI, and much more.

Lekhana Reddy is a data content creator focusing on mindfulness. We chat about how mindfulness in technology is key, especially given the need to maintain humanity with the rise of AI.

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

Instagram: https://www.instagram.com/storytellingbydata/

Until recently, Nik Suresh wrote under a mysterious blog that had several viral posts, including the famous "I Will F*cking Piledrive You If You Mention AI Again." For the longest time, he was an underground sensation, with nobody (not even his friends) knowing his identity.

In this episode, we chat about his blog posts (I'm a huge fan), the realities of data science and data engineering, and much more. This is a very candid and fun chat where I'm actually the fanboy, so enjoy!

Blog: https://ludic.mataroa.blog/

I've been saying for years, most companies are barely doing BI, let alone AI. Last week, I posted about this on LinkedIn and it went viral. Here, I unpack what I mean by that post.

The post: https://www.linkedin.com/feed/update/urn:li:activity:7230408663125913600

Rehgan Bleile joins me to chat about the challenges and importance of AI governance and adoption. We also discuss the lack of representation of women in conferences, and efforts to create genuine opportunities for women speakers.

AlignAI: getalignai.com

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

Women in Analytics: https://www.womeninanalytics.com/

Most people approach their careers via 1:1 - 1 employee: 1 employer, 1 consultant: 1 client, etc.

In this rant, I discuss why you should consider 1:many as a way to level up your career and visibility.

Christian Steinart and I chat about data consulting in unsexy and niche industries (like roofing in Christian's case). We go through starting a consultancy, the mental game of running a business, getting leads, and much more.

LinkedIn: https://www.linkedin.com/in/christiansteinert96/

I've been head's down finishing my upcoming Data Engineering course on Coursera, and working on the new book. In this episode, I chat about the differences between courses and books, why high quality content matters more than ever in the age of AI, and much more.

Enroll in the new DeepLearning.AI Data Engineering Professional Certificate course here!

https://www.coursera.org/professional-certificates/data-engineering

Chris Bergh joins me to chat about all things DataOps. We also discuss lean, removing waste from data processes and teams, and much more.

DataKitchen: https://datakitchen.io/

DataOps Manifesto: https://dataopsmanifesto.org/en/

Hanging out in Berlin right now. Re-read Peopleware (originally released in 1987), and it got me thinking about what hasn't changed in tech and data. Namely, we tend to rush through things in the name of productivity instead of focusing on quality. Will AI help this? Maybe and maybe not.

I've been told that I "say the quiet parts out loud." It might be calling out "data science" and AI as overhyped, or anything else I've ranted about online over the years.

In this episode, I unpack what that means and why I do it.

Joseph Machado and I chat about teaching data engineering, trends in data engineering, and the state of data content and influencers.

LinkedIn: https://www.linkedin.com/in/josephmachado1991/

Website: https://www.startdataengineering.com/

Data can be a rough industry. Data professionals often feel alone and in need of someone to talk to. I've been hosting "data therapy" sessions for the last couple months. Here are my thoughts on them, and why I think they're critical to helping professionals feel heard and learn from others.

Join here: https://practicaldatamodeling.substack.com/

Paco Nathan and I chat about early chatbots, and all things AI, especially since the 1980s. We also riff on how we intersected in the early days of the Internet.

Paco is one of my faves, so expect him back for another interview soon.

X: https://twitter.com/pacoid

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

Shachar Meir joins me for a chat about the three missing ingredients in data, going out on your own, the state of the data industry, and much more.

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

Matthew Mullins joins me to chat about a recent "get off my lawn" post along the lines that a lot of things we're trying to reinvent in data have already been done, and we're generally overcomplicating the hell out of things. I agree with him. In this episode, we rant about a few of the topics in his post.

The post: https://www.linkedin.com/feed/update/urn:li:activity:7217116669188485121/

Lindsay Murphy joins me to chat about whether companies are ready for AI in their data and analytics workflows. The verdict - listen and find out more :)

Lindsay's LinkedIn: https://www.linkedin.com/in/lindsaymurphy4/

It's not enough to know or peddle one data modeling technique these days. That's like fighting in the UFC knowing only thumb-wrestling. The world is very complicated with respect to data. To be a data practitioner, you need to be awesome in not just one, but MANY data modeling techniques. This is what I call Mixed Model Arts, which will be discussed further soon. Anyway, don't be 1-dimensional. Know a lot about a lot.

What's up with Finland and data? I think Finland might have among the strongest contingencies of data practitioners in the world. Pound for pound, Finland might rule the planet for data competencies.

I chat with the The Finnish Data Mafia, jokingly my friends who are responsible for the upcoming Helskini Data Week.

Just wrapped up a course with Sol Rashidi on transitioning your career from practitioner to leader. The notion of "success" kept recurring, so I spend this podcast unpacking it. What is success and why should you figure out what it means for you?

This wasn't the interview I expected to do. I thought I'd interview Nick Freund about his startup, Workstream. Between the time we scheduled our podcast and when we hit the record button, he shut down his company. That's a pretty major shift, to say the least.

What's it like to shut down a company? Nick discusses the various pivots of his startup, trying to raise capital in a brutal funding environment, the data tooling landscape, the process of shutting down a company, and much more.

This is an emotional episode, and I'm glad we got the opportunity to make it happen. I feel like stories like Nick's are all too common, yet rarely vocalized in the brutally honest way that Nick describes his story.

Nick's LinkedIn: https://www.linkedin.com/in/nick-from-workstream/

I've had plenty of discussions over the last couple of weeks about data teams - what are they, and how do I measure their success? I dive into a distinction I make about data teams - enterprise vs product - and some key ways to gauge them.

Doug Needham is an OG DBA and data architect who built DataOps workflows back in Desert Storm (!) and has managed to stay very current with data to today. We talk about data architecture war stories, the hard work to do generative AI in the enterprise, and much more. Enjoy!

Some things happened over the last day that I need to call out. Women and other underrepresented groups need to be treated better in tech and data. Whether it's all-male panels at conferences or mansplaining on social media, I'm pretty embarrassed and irritated by how women are treated in our industry. My message for this episode - stop being a d*ck.

Juha Korpela is a world-renowned expert in conceptual data modeling. He joins me to discuss the power of conceptual data modeling, why the data modeling world is broken today, data products, and much more.

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

In this episode, I talk about why history matters for technology professionals. When you understand the history of technology, techniques, and approaches, you have the context to understand where they fit into your situation. Ignore history at your peril.

Yulia Pavlova (Director of Technical Innovation at Thomson Reuters) joins me to chat about the role of AI in disinformation/misinformation in the media, communicating complex topics to nontechnical people, and much more.

I personally consider the current state of the media as one of the central challenges today, and I learned a lot chatting with Yulia, who's innovating in this space.

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

Is data modeling a waste of time? I meet a number of people who say it is. In this episode, I dissect some of the arguments against data modeling, and give reasons why it matters more than ever today.

Safiyy Momen and I chat about the good and bad of the Modern Data Stack, controlling cloud costs, boring engineering, and much more.

LinkedIn: https://www.linkedin.com/in/safiyy-momen/

Gordon Wong and I chat about why most data teams aren't that valuable, and ways data teams can deliver more value.

Just got off the plane from Spain, and I'm quite jet lagged. Nonetheless, here's your Friday rant.

In this episode, I chat about an experience I had with someone telling me Python is a slow language. If you can't tell, I think programming language wars are dumb, and I give some reasons why.

Roman Yampolskiy is an AI safety researcher who's deeply concerned with the dangers of General Super Intelligence. We chat about why he doesn't think humanity has much time left, and what we can do about it.

Twitter: https://twitter.com/romanyam?lang=en

I'm sitting in the Amsterdam Airport (Schipol) and wrote some of my book on the flight over to Europe. In this episode, I'll talk briefly about my book writing process, and how it differs today from when I wrote Fundamentals of Data Engineering.

Jarod Santo and Adam Stacoviak from The Changelog join me for 1.5 hours of free-flowing chats about planned obscelescene, old school vs new school consumer tech, the XZ Backdoor incident, the job market doldrums (plus tips for finding work and starting a biz), and being unemployable.

Jarod and Adam are two of my favorite people to talk with, since we can literally chat about anything for hours. Enjoy!

Changelog: https://changelog.com/

In today's Practical Data Modeling group discussion, we chatted about how to get buy-in for data modeling. The question was intentionally vague, because context is key. I give some thoughts on this topic, and how you can generalize this to most situations where you need to get buy-in.

Practical Data Modeling: https://practicaldatamodeling.substack.com/

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

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/

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.

I consider Steve Hoberman to be one of the original data modelers, having practiced and taught data modeling since the 1990s. He also runs the venerable Technics Publications, which I consider the foremost publishers of data-oriented books.

Steve and I discuss data modeling's past, present, and future. If you're into data modeling, this is a must-listen. Enjoy!

Technics Publications: https://technicspub.com/

Steve Hoberman LinkedIn - https://www.linkedin.com/in/stevehoberman/

Andrew Meister and I chat about removing wasted notions and bad workflows, aka "clunk."

Today's rant is a random grab bag of stuff - my thoughts on Data Day Texas, updates to Practical Data Modeling, and more.

Roy Hasson and I chat about career progressions in data and technology, open table formats (Iceberg), and more.

Are your outputs generating the right outcomes? I'm in Austin for Data Day Texas, and I reflect on this topic via a conversation I had last night with Juan Sequeda, Tim Gasper, and Santona Tuli.

In 2024, outcomes will matter more than ever. What are you doing to drive the right outcomes for your organization?

Ari Kaplan (Head of Evangelism at Databricks) joins me to chat about all things data intelligence, data lakehouses, the role of evangelism in tech companies, automation, and much more.

Jordan Morrow and I hung out at my house and chatted about all things data literacy, the great outdoors, and more. Enjoy!

What am I seeing in data engineering in 2024? Listen and find out.

Steve Nouri has a massive following on LinkedIn, travels the world speaking at conferences and advising companies, and most recently created a community - Generative AI, on LinkedIn. We chat about being an influencer, building and growing a community, and much more.

I often get some questions - What happened to data modeling? Where do I learn data modeling? Where the heck is your new book?

Well, at least some of your questions will be answered in this podcast.

I'm launching a new project called Practical Data Modeling on Substack. You'll get weekly articles, early chapters of my new data modeling book, community discussions, and much more.

Subscribe to Practical Data Modeling: https://practicaldatamodeling.substack.com/

It's that time of year - lots of predictions and resolutions. I rant about my thoughts on New Years predictions and resolutions, stuff I'm stoked on in 2024, and more.

I sincerely appreciate each and every one of you for your support. It means a ton.

Happy 2024!!!

Will Gaviria Rojas (co-founder of CoactiveAI) and I sit in a hotel bar and chat about the amazing world of unstructured data and AI. We chat about searching unstructured datasets (without metadata), the state of AI, and much more. Will is one of my favorite people in general, so I really enjoyed our chat. Enjoy!

What happens when AI can generate content about whatever - or whoever - whether it's true or not? Welcome to 2023 and beyond.

I think the Internet is about to become a sea of complete sh*t of uninteresting and inaccurate content. The value of most content on the Internet will be less than worthless.

What can content creators and authors do? I provide some ideas...

Just wrapped up a couple of days at the CDO/CAIO Summit in Boston. There are a lot of corporate mandates to "do AI", but also a disconnect in the support and empowerment required to succeed. I unpack some observations of this disconnect, and why it reminds of of the South Park Underpants Gnomes - "AI...?...Profit"

Eleanor Thompson is the most knowledgeable person I know on the topic of vendor partnerships. We dive into the details of what makes a great partnership, tips on starting a partner program, certifications, and much more.

Are you a technology or data vendor looking to start a partnership program, or improve your existing one? Or a consulting company looking to partner with a vendor? Having worked with many partners, I can tell you that partnerships have always had ups and downs. I wish I'd listened to this episode several years ago. Enjoy!

Ben Rogojan, aka Seattle Data Guy, needs no introduction. He's a force of nature in the data engineering content world.

We chat about his origin story (sort of like a superhero), content creation, consulting, cooking, and much more.

You may have heard of Live Action Role Playing (LARP), where people dress up like fantasy characters and sword fight in a park or a Wal Mart parking lot. Some data teams LARP too...

Data LARPs are a real thing, often identified by pretending to do "data stuff", while never actually pushing anything into production. Are you Data LARPing? If so, what can you do? Listen and find out.

Matt Harrison is the author of many of the most successful Python books, including Effective Pandas, Effective XGBoost, The Machine Learning Pocket Reference, and many more. I consider him the top author of Python books and content on the planet.

He stopped by my house to chat about self-publishing technical books, the pros and cons of using a publisher, book piracy, and much more. We both talk about our experiences as best-selling technical authors, and don't hold back in this wide ranging and very candid conversation. Enjoy!

Note - my audio got a bit clippy in spots. Sorry if I blew up your speaker.

Karin Wolok and I chat about all things devrel - what it is, why it matters, the good the bad, and everything in between. We also talk about her time in the music industry, which is a fascinating side thread. Karin is amazingly smart, energetic, and fun to talk with. Enjoy!

Note - this was recorded right after the awesome DEWCon, the premier data engineering conference in Bangalore, India. Shoutout to everyone there!

Like most of you, I spent last weekend and the earlier part of this week following the OpenAI drama. Plot-twists galore! Every minute seemed like a new adventure. In the midst of the plot twists and turns, I noticed quite a few people saying, "This was all predictable", and then offering prognostications, most of which turned out to be very wrong. If even the OpenAI insiders couldn't figure out what was going on, how would the person on the street?

It's a good reminder that you need to approach the world with a sense of humility and try not to be a know-it-all. Don't be afraid to say "I don't know."

I recap two events from this week - Matillion's Data Unlocked and the first MLOps Community event in Silicon Valley, hosted at Coactive.ai's office. Lots to unpack in 8 minutes, so let's get going.

Peggy Tsai joins me to chat about a ton of hard topics. We chat about how to set up Chief Data Officers for success (not easy), data maturity, data and ESG, and a ton more. Enjoy this very wide-ranging chat.

Katharine Jarmul and I chat about the Biden AI Executive Order. Enjoy!

Dave McComb (CEO of Semantic Arts) is one of the most thoughtful and original people I know in the data space, having pioneered the use of semantics and knowledge graphs in the 1990s. We talk about a wide range of topics, including interoperability at the data level, the data-centric revolution, and much more.

Note - there was a weird and unresolved connection issue that caused some static at varying points during our call.

Marketing is a well-established field, yet there is still a massive gulf between CEO and CMO expectations. If CMOs are still trying to get traction with their CEOs, what hope do Chief Data Officers have? I rant about this.

WSJ article: Divide Between CMOs and CEOs is Growing: https://www.wsj.com/articles/divide-between-cmos-and-ceos-is-growing-research-finds-a73374f4?st=bcayeo7wc9j18fd&reflink=desktopwebshare_permalink

Bill Inmon and I sat down in his home over a weekend to tell me the history of the data industry. This is a very rare and wide-ranging conversation about the dawn and evolution of the data industry, straight from the godfather himself.

In my global travels, I've spoken to countless practitioners and leaders in the data field. I discuss two common themes of questions I get - "How do I work with the business?" and "What tools should I use?"

Matt Sharp and Chris Brousseau join me to chat about writing their new book "LLMs in Production" (Manning). What's it like to write a book in a field that's changing at light speed? How do two people write a book together? We dive into this and much more.

Note - we recorded this outside at the Utah State Capitol. There's a bit of background noise, but it hopefully doesn't distract from the conversation. It was too nice of a day to be stuck inside :)

Johnny Graettinger (CTO of Estuary) joins the show to give a clinic on streaming and immutable logs. We cover a lot of ground in this technical deep dive. Enjoy!

Estuary: https://estuary.dev/

Gazette: https://gazette.readthedocs.io/en/latest/

Github (Estuary Flow): https://github.com/estuary/flow

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

Just wrapped up DEWCon, a great data engineering conference in Bangalore, India. I've participated in most data engineering events around the world this year, and I keep seeing the same things - data engineers seek guidance, mentorship, and a sense of best practices. I think we can build a global data engineering community that helps data engineers level up and share their experiences.

Bob Muglia joins the show to talk about why we need to move beyond SQL, semantic models, the power of knowledge graphs, the past, present, and future of databases, and much more.

Bob has a storied history in the data space, having significant involvement with Microsoft Office, SQL Server, Snowflake (former CEO), and much more. It's rare to meet someone with such a deep involvement in the creation of our industry, and this was a fascinating conversation.

Also, check out Bob's book, "The Datapreneurs." One of the best books about the data industry I've read in ages.

LinkedIn: https://www.linkedin.com/in/bob-muglia/

X: https://twitter.com/Bob_Muglia

Book: https://www.amazon.com/Datapreneurs-Promise-Creators-Building-Future-ebook/dp/B0BZQFJ5RP

As I've ranted for a while now, I think our biggest challenge as an industry is the knowledge and skills to do our properly do our jobs. Too often, I see data professionals flounder on seemingly simple problems, even using the hottest, coolest technologies. Don't blame the tool, blame the user.

How do you sharpen your skills? I give some advice in this episode.

Egor Gryaznov joins me to chat about the "Non-Modern Data Stack", getting out of our data bubble, and much more. If you like a refreshing conversation talking about the past, present, and future of our industry, this is for you.

BigEye: https://webflow.bigeye.com/

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

Vendors are an integral part of conferences (they pay for them, for one). But what happens when vendors face a tough market? Between higher interest rates, a tough funding environment, and a lukewarm market for what vendors are selling, what happens? I unpack some thoughts on what I think 2024 will look like for vendors at conferences.

Jason Taylor and I chat about low-key data happy hours, getting outside of your comfort zone, finding new ideas, the divides in the data space, fighting dumpster fires, and much more. This is a wide-ranging chat about a lot of key topics in the data space. Enjoy!

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

Michel Tricot (CEO of Airbyte) joins me to chat about the impact of AI on the modern data stack, ETL for AI, the challenges of moving from open source to a paid product, and much more.

Airbyte & Pinecone - https://airbyte.com/tutorials/chat-with-your-data-using-openai-pinecone-airbyte-and-langchain

Note from Joe - I had audio issues cuz he got a new computer and didn't use the correct mic :(

Juan Sequeda and I chat about knowledge graphs (he's an OG in this area), the potential of LLMs on structured datasets, and much more. This is an honest, no-BS chat about the transition from a data-first world to a knowledge-first world. Enjoy!

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

data.world: https://data.world/product/

website: https://www.juansequeda.com/

Nowadays, it seems like lots of people want to become data influencers and content creators. Ravit Jain and I chat about how to build an audience, the power of LinkedIn, and much more.

The Ravit Show: https://theravitshow.com/

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

Boring is back. As technology makes the lives of data engineers easier with respect to solving classical data problems, data engineers can now move to tackle "boring" problems like data contracts, semantics, and higher-level and value-add tasks. This also sets us up to tackle the next generation of data problems, namely integrating ML and AI into every business workflow. Boring is good.

Medical data is a mess. Aaron Neiderhiser and Coco Zuloaga from Tuva Health are here (at my house) to answer why this data is insanely complex, and what can be done to fix it. They take a first-principles approach that you might find interesting.

In my travels and virtual conversations with data teams and practitioners around the world, the same thing keep popping up - data teams feel misunderstood and under-appreciated. If we're going to make progress as an industry, it's time to stop playing defense, and start playing offense.

David Foster just published the 2nd edition of his amazing book, Generative Deep Learning (O'Reilly 2023). We chat about a lot - running a consultancy, all things writing, the impact of AI on kids, why in-person events matter more than ever, and much more.

David's LinkedIn: https://www.linkedin.com/in/davidtfoster/

Book (Amazon): https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947

Applied Data Science Partners: https://adsp.ai/

The massive attention on AI means the data industry has a shot at finally succeeding with the things we've been struggling with for decades - "adding value", data quality and modeling, governance, etc. For AI to work, these things need to properly function. But are we up for the task? And if we can't get it right NOW, when?

Whenever Kevin and I get together, we "nerd snipe" each other. This conversation is no different, and it's a wide-ranging conversation about how the data landscape evolves alongside LLMs, education, startup mentorship, and the possible (looming?) startup mass extinction.

Kevin's LinkedIn: https://www.linkedin.com/in/kevinzenghu/

Metaplane: https://metaplane.dev/

Matt Housley hangs out at my house and we chat about Data NIMBYism and gatekeeping. What is that? Listen and find out.

Gordon Wong has led data teams of all sizes, across many well-known companies. I consider him a Yoda in the data field, and this is a glimpse into the monthly chats that Gordon and I have. Unfiltered and uncut. Enjoy.

Please note - we had to cut it a bit short, as Gordon had another call at the top of the call. Again, this is literally a look at a normal chat that Gordon and I engage in.

This week I had the opportunity to give two talks to two very different groups. Earlier in the week I spoke with a group of senior and above software engineers and leaders in Utah. Yesterday I spoke with a group of data engineers and leaders in Atlanta.

The common theme? There's a big divide between dev and data, and data's often on the losing side of this divide. For data teams to be more successful, we need to close the divide and collaborate more closely with dev, and vice versa.

Vin and I chat about the challenges of writing books, how companies can mature with data science, why data scientists need to learn strategy, and much more.

(We experienced a slight internet delay around the 15:30 mark, otherwise great)

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

Book: https://www.amazon.com/Data-Profit-Businesses-Leverage-Bottom/dp/1394196210

Site: https://www.datascience.vin/

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Scott Taylor (aka the Data Whisperer) is an OG in data content, speaking, and storytelling. He's been keynoting data events since the 1990s and keeps sharpening his game. Scott is someone I look up to, and I always enjoy our chats.

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

Website: https://www.metametaconsulting.com/

YouTube: https://www.youtube.com/channel/UCVQ1YhjNqc77GVsb3Xs4tvw

(Note - there's a very slight interruption with our internet connection at the 35 minute mark)

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Kai Zenner has been working on the EI AI Act for a while, and we chat about his perspective on its evolution, challenges, and potential. Along the way, we discuss why the EU AI Act differs from GDPR, why regulating a quasi-global piece of legislation is very difficult, and much more.

I admit, politics and regulation are way outside my wheelhouse, and I learned a ton in this discussion. Given the impact the EU AI Act will affect the work of everyone involved with data, I think you'll learn a thing or two about not just the act itself, but also how the "sausage is made", so to speak. Enjoy!

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

Twitter: https://twitter.com/ZennerBXL

Site: https://www.kaizenner.eu

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Ryan Boyd and I chat about the evolution and future of databases, the pendulum between single-server and distributed computing, DuckDB and Motherduck, and much more.

We also talk about developer relations, which I consider Ryan as one of the OG's in the field.

Note - this was recorded the week of Databricks Summit 2023.

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Is Kimball still relevant? Or should we just throw columnar storage and unlimited compute to solve our analytical needs?

Because I like to live on the edge, I respond to a comment online that I think highlights the rot in our industry as it relates to how we view data modeling today.

Data Modeling With Joe Reis - Understanding What Data Modeling Is And Where It's Going (Seattle Data Guy): https://www.youtube.com/watch?v=NKo02ThtAto

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Joshua Bowles is a linguist and data scientist turned software engineer. This is a wide-ranging chat between two old-school data scientists/ML practitioners about the past, present, and future of ML and AI.

LinkedIn: https://www.linkedin.com/in/joshua-bowles-ailgroup/

Mastadon: https://infosec.exchange/explore

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Benny and I chat about whether data is a profession (in the traditional sense), moving from CDO at a large company to solo consulting, building an audience and staying consistent with content, and much more.

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

Elevating Data to a Profession (link): https://www.datent.com/p/elevating-data-to-a-profession-why

Blog: https://www.datent.com

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

Subscribe to my Substack: https://joereis.substack.com/

Imagine two extremes. On one end, data modeling is done perfectly and harmoniously across the data lifecycle. On the other end, data modeling is ignored and thrown into the dustbin of history. Along this spectrum, where do you think we are as a data industry?

I'm leaving this question open-ended right for now and would appreciate your thoughts.

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If you like this show, give it a 5-star rating on your favorite podcast platform.

Purchase Fundamentals of Data Engineering at your favorite bookseller.

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Ranjith Raghunath is a customer experience (CX) wizard, and we chat about why CX is important, modeling the customer journey process, the impact of AI on CX, and much more.

Ranjith's LinkedIn: https://www.linkedin.com/in/ranjith-raghunath/

CX Data Labs: https://www.cxdatalabs.com/

Note - there were some technical difficulties around the 44:00 minute mark. Nothing major though.

Peter Hanssens is the founder of DataEngBytes, the forward-thinking conference on all things data engineering. In this chat, we talk about what to expect at the 2023 edition of DataEngBytes, the tech scene in Australia, his views on the current and future field of data engineering, and much more.

#data #dataengineering #dataengbytes

It's no secret that AI is useful, but it's super hard to cut through the BS.

Maya Mikhailov and I chat about the very confusing ML/AI landscape. This is a great discussion that offers many pointers if you want to leverage ML and AI, but you're unsure how to do so.

Maya's LinkedIn: https://www.linkedin.com/in/mayam/

Savvi AI: https://www.savviai.com/

#ai #ml #data #datascience

I'm starting to see more and more discussions about soft skills and people skills. In this episode, I talk about why tech skills are table stakes, and soft skills are where you need to level up if you want to boost your career.

Office Space clip: https://www.youtube.com/watch?v=hNuu9CpdjIo

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Tristan Handy and I chat about balancing competing tensions, both personally and leading dbt Labs. We also discuss the power of organizational behavior, naming problems to solve, and home remodeling.

This is different from the normal interviews you'll hear with Tristan, and I hope you enjoy it!

#dbtlabs #data #analyticsengineering

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After a whirlwind of conferences with the two "big players in the data space," I share some thoughts on how we can improve the conferences targeted at the data engineering community.

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Solomon Kahn has led data teams at startups and big companies. We talk about the advantages of being a data person in a big company, what makes a good data team, why he thinks embedded analytics suck, his new startup Delivery Layer, and much more.

Delivery Layer: https://www.deliverylayer.com/

Solomon's LinkedIn: https://www.linkedin.com/in/solomonkahn/

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Whenever Kris and I chat, there's an agenda, which is totally useless. Every single time we've talked, the conversation goes into different (I'll argue better) directions. In this episode, Kris and I delve into the art and craft of programming, finding your tribe as a developer advocate, and so much more. I hope you enjoy this great and meandering conversation.

Developer Voices podcast: https://open.spotify.com/show/2gXhwz0AQRv2cvw61kobE5

Kris's LinkedIn: https://www.linkedin.com/in/krisjenkins/

Kris's Twitter: https://twitter.com/krisajenkins

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I've spoken earlier about why I think data modeling is on life support, and if not dead, definitely a zombie. In this rant, I explore how we got here, the consequences, and what we can do to resurrect data modeling.

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Veronika Durgin joins me to chat about learning and adapting in a fast-changing world, handling vendors, and much more.

Veronika's LinkedIn: https://www.linkedin.com/in/vdurgin/

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Paul Blankley and Ryan Janssen are the co-founders of Zenlytic. They started a BI company with an LLM-first approach (back before LLM's were insanely cool). We talk about the future of BI, and how LLM's will change the face of data and analytics.

Zenlytic: https://www.zenlytic.com/

Paul's LinkedIn: https://www.linkedin.com/in/paulblankley/

Ryan's LinkedIn: https://www.linkedin.com/in/janssenryan/

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Karl "Ivo" Sokolov and I are good friends, and we chat about how he views innovation in Europe, regulations, Western and Eastern Europe, moving from services to a product, and much more. Ivo is a straight shooter, and I always enjoy chatting with him. Enjoy!

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The word "model" is used a lot by data professionals. There are dbt models, machine learning models, relational models, and conceptual, logical, and physical models. My concern is we're missing the bigger picture of what data modeling was initially supposed to accomplish, which was to represent reality and structure it as data. The bigger implication is that our various "models" will become too myopic and miss the larger broader context of the reality of how we use data to serve our organizations.

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George Park is a Czechia-based data engineer. We chat about automation, change management and culture, data modeling (Data Vault in particular), and much more. George lives on the front lines of helping customers, so this is a good discussion if you like to hear from a real practitioner.

George's LinkedIn: https://www.linkedin.com/in/george-park-599846136/

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There's a rush from many countries to regulate AI. Murielle Popa-Fabre is an NLP and ML expert currently working for the Council of Europe, building an international Framework Convention on AI that will touch a wider number of countries (46) than the EU AI Act (only for EU countries). We chat about her path from academia to working in regulation, and the upcoming EU, the Council of Europe, and G7 regulations on AI. These regulations will have a historical impact on what happens next with AI, and it will be very interesting to see where things go from here. Murielle's LinkedIn: https://www.linkedin.com/in/murielle-popa-fabre-b563187b/

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Rebecca Taylor has a ton of experience with ML, having worked as lead, and senior ML engineer, and holds a PhD in Bayesian Inference. We chat about making ML succeed in larger companies. This is a skill in itself, often requiring stakeholder management and getting buy-in. If you're in a similar situation, this episode is for you!

Rebecca's LinkedIn: https://www.linkedin.com/in/rebecca-taylor-ml-ai/

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Today was pretty cool. My son just finished 6th grade, so proud parent moment. Last week, I spoke to his class about AI. It was a very fun experience and the class was extremely curious about what AI is and how it might impact their lives. In this episode, I discuss some of the things I talked about. I think the lessons apply equally well to adults.

John Kutay and I chat about podcasting and being influencers, a deep dive into change data capture (CDC), Zero ETL, data sharing, Data Mesh, being a vendor in a crowded market, music and art, and more. John is very eclectic, and always a joy to learn from.

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

Twitter: https://twitter.com/JohnKutay

Soundcloud: https://soundcloud.com/jkxo

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In most companies, there's a division between software and data. This needs to end. How to get there?

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John Giles is a legend in the data modeling world, having authored "Nimble Elephant" and "The Elephant in the Fridge," and written extensively on the topic. John and I discuss the power of using data model patterns, enterprise data modeling, "data town plans," and much more. It was an honor to chat with John, and I felt like I was conversing with someone who comes from a more advanced dimension than most data practitioners. You'll learn a ton from this discussion.

John's website: https://www.countrye.com.au/

Books: Nimble Elephant (https://www.amazon.com/Nimble-Elephant-Delivery-Pattern-based-Approach/dp/1935504258), The Elephant in the Fridge (https://www.amazon.com/Elephant-Fridge-Success-Building-Business-Centered/dp/1634624890)

LinkedIn: https://www.linkedin.com/in/john-giles-data/

#data #datamodeling

Brian Greene and I chat about how software and data engineering interact, the team dynamics of a startup, and much more. Brian is a no-BS straight shooter, and you'll learn a ton in this discussion.

Brian's LinkedIn: https://www.linkedin.com/in/theotherbriangreene/

Neuronphere: https://www.neuronsphere.io/

#data #dataengineering #neutronsphere

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Aaron Wilkerson and I chat about data strategy and leadership, business value and alignment, and the eternal question in our industry - why do data leaders struggle so hard?

Aaron's LinkedIn: https://www.linkedin.com/in/aaron-wilkerson-81bb21a/

#datastrategy #data #leadership

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In this episode, I go through the reasons why I put a stop to most meetings going forward. I urge you to figure out how you can better control your time since it's the one thing you never get back.

Also, I've got a new weekend newsletter dropping tomorrow! Sign up at joereis.substack.com

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Data products are a very popular topic these days. The challenge is we need new thinking and approaches that differ from how we've worked with data in the past. Jon Cooke and I chat about the mindset shift needed to make data products successful.

Jon Cooke's LinkedIn: https://www.linkedin.com/in/jon-cooke-096bb0/

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I recap the Joe Reis + dbt roadshow in Denver (thanks to everyone who showed up) and discuss the divide between IT and "The Business."

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John O'Gorman and I discussed his career as a very early data OG, how he might have created one of the first analytical stores, the fundamentals of semantics, data products, and much more.

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Sarah Floris (aka The Dutch Engineer) is prolific with creating content aimed at DataOps and data engineering. In this wide ranging chat, we cover content platforms for technical creators, podcasting, data engineering vs ML engineering, why DataOps is awesome, courses, layoffs, and much more.

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I've been spending some time in Europe, and most recently returned from Germany the other day. There are definitely some differences between the US and Europe, particularly in how each conducts business and regulation. Tune in for my thoughts on these differences.

#data #dataengineering #datascience

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Katharine Jarmul (Principal data scientist at Thoughtworks and author of Practical Data Privacy (O’Reilly, 2023)) and I chat about all things data privacy. She brings battle-tested experience and unique perspectives in the areas of ML/AI privacy, AI risk, regulation, and much more. I learned a ton, and I hope you do too!

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

Twitter: https://twitter.com/kjam

Probably Private newsletter: https://probablyprivate.com/

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The economic downturn is affecting the tech and data industry in a major way. Lots of layoffs, consolidation, and pain. But is this also a good thing for the industry? Listen and get my opinion in this nerdy rant about the economy, interest rates, and doing more with less.

#data #dataengineering #datascience

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Neelesh Salian joins the show to discuss the rise of data engineering, where the data landscape is heading, and career growth as an engineer. Neelesh has a ton of experience in the technology space, and you'll learn a lot from his wisdom.

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

Substack: https://hysterical.substack.com/

Twitter: https://twitter.com/neelesh_salian

#dataengineering #data #careeradvice #softwareengineering #swe

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I teach data at the University of Utah, and I’m also on the board of advisors for my department. What curriculum and approach do I advise universities use for teaching data and technology? Listen and find out.

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Ken Jee joins the show to chat about how he makes awesome content, podcasting and being authentic, jiu jitsu, maximizing your time, and adapting to AI.

#datascience #kenjee #data #ai

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ChatGPT was the iPhone moment for AI, and things are moving insanely quickly. What do generative AI models mean for us, especially children, who are arguably the last of the Pre-AI generation? I dive into some thoughts this week about how we need to work alongside the machines, the impact of generative AI on kids, and so on. Buckle up. We are in for a very interesting next few years as we sort out where AI fits into our day-to-day lives.

#data #datascience #dataengineering #chatgpt #ai

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Ryan Dolley and I chat about why BI needs to evolve, moving beyond dashboards, the impact of generative AI on analytics, SuperDataBros, and more.

#data #analytics #businessintelligence #datascience

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Is data modeling on life support? I posed this question to LinkedIn earlier this week. It got a fair number of replies, some supportive and others saying I'm full of sh*t. In this 5 minute Friday nerdy rant, I unpack what I mean by data modeling being on life support, and where I think data modeling needs to go given newer practices like streaming and machine learning, which aren't currently discussed in data modeling circles.

LinkedIn post about data modeling on life support: https://www.linkedin.com/posts/josephreis_dataengineering-datamodeling-data-activity-7048722463010013185-OyIy

#dataengineering #datamodel #data

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Shane Gibson joins the show to discuss how to make data modeling more accessible, why the world's moved past traditional data modeling, enabling data mesh, and more.

Shane's LinkedIn: https://www.linkedin.com/in/shagility/

Shagility: https://shagility.nz/

Shane's podcasts: https://shagility.nz/podcasts/

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Dave Langer teaches data literacy with the world's most popular data tool - Excel. We chat about why Excel is awesome, ways to teach data to the masses, and much more.

Dave Langer LinkedIn: https://www.linkedin.com/in/davelanger/

Dave on Data YouTube: https://www.youtube.com/@davidlanger8217

Website: https://www.daveondata.com/

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Zach Wilson is one of my favorite people, and when we chat, it's total honesty and great vibes. In this episode, we discuss his transition from a staff data engineer at Airbnb to an entrepreneur (!), and we both talk about our experiences with ADHD, the data engineering field today, content creation, and a ton in between. 

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Chris Tabb (LEIT Data) and I hang out at my house and chat about data monetization and business value. 

What the heck are those things? Good question. Listen and find out.

LEIT Data: https://www.leit-data.com/

Chris Tabb: https://www.linkedin.com/in/chris-tabb-datatips/

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The tech and data industry needs more candor. The point of this podcast is for me (and my guests) to rant about their opinions on the tech and data industry in a very honest way.