In this episode, Tristan Handy sits down with Chang She — a co-creator of Pandas and now CEO of LanceDB — to explore the convergence of analytics and AI engineering. The team at LanceDB is rebuilding the data lake from the ground up with AI as a first principle, starting with a new AI-native file format called Lance. Tristan traces Chang's journey as one of the original contributors to the pandas library to building a new infrastructure layer for AI-native data. Learn why vector databases alone aren't enough, why agents require new architecture, and how LanceDB is building a AI lakehouse for the future. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Tristan talks with Mikkel Dengsøe, co-founder at SYNQ, to break down what agentic coding looks like in analytics engineering. Mikkel walks through a hands-on project using Cursor, the dbt MCP server, Omni's AI assistant, and Snowflake. They cover where agents shine (staging, unit tests, lineage-aware checks), where they're risky (BI chat for non-experts), and how observability is shifting from dashboards to root-cause explanations. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

What does it mean to be agentic? Is there a spectrum of agency?  In this episode of The Analytics Engineering Podcast, Tristan Handy talks to Sean Falconer, senior director of AI strategy at Confluent, about AI agents. They discuss what truly makes software "agentic," where agents are successfully being deployed, and how to conceptualize and build agents within enterprise infrastructure.  Sean shares practical ideas about the changing trends in AI, the role of basic models, and why agents may be better for businesses than for consumers. This episode will give you a clear, practical idea of how AI agents can change businesses, instead of being a vague marketing buzzword. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

In this season of the Analytics Engineering podcast, Tristan is digging deep into the world of developer tools and databases. There are few more widely used developer tools than Docker. From its launch back in 2013, Docker has completely changed how developers ship applications.  In this episode, Tristan talks to Solomon Hykes, the founder and creator of Docker. They trace Docker's rise from startup obscurity to becoming foundational infrastructure in modern software development. Solomon explains the technical underpinnings of containerization, the pivotal shift from platform-as-a-service to open-source engine, and why Docker's developer experience was so revolutionary.  The conversation also dives into his next venture Dagger, and how it aims to solve the messy, overlooked workflows of software delivery. Bonus: Solomon shares how AI agents are reshaping how CI/CD gets done and why the next revolution in DevOps might already be here. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

In this decades-spanning episode, Tristan Handy sits down with Lonne Jaffe, Managing Director at Insight Partners and former CEO of Syncsort (now Precisely), to trace the history of the data ecosystem—from its mainframe origins to its AI-infused future. Lonne reflects on the evolution of ETL, the unexpected staying power of legacy tech, and why AI may finally erode the switching costs that have long protected incumbents. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

In this episode, Tristan talks to Zach Lloyd, founder of Warp—a terminal built for the modern era, including for AI agents. They explore the history of terminals, differences between terminals and shells, and what the future might look like. In a world driven by generative AI, the terminal could once again be the control center of computer usage. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Fivetran recently passed $300 million ARR and has over 7,000 customers globally. Taylor Brown, the cofounder and COO of Fivetran, joins the show to talk about Fivetran's moat, the impact of AI on the data ingestion space, and open table formats and catalogs.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Erik Bernhardsson, the CEO and co-founder of Modal Labs, joins Tristan to talk about Gen AI, the lack of GPUs, the future of cloud computing, and egress fees. They also discuss whether the job title of data engineer is something we should want more or less of in the future. Erik's not afraid of a spicy take, so this is a fun one.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Former co-host Julia Schottenstein returns to the show to go deep into the world of LLMs. Julia joined LangChain as an early employee, in Tristan's words, to "Basically solve all of the problems that aren't specifically in product and engineering." LangChain has become one of, if not the primary frameworks for developing applications using large language models. There are over a million developers using LangChain today, building everything from prototypes to production AI applications.

Nisha Paliwal, who leads enterprise data tech at Capital One, joins Tristan to discuss building a strong data culture for in the world of AI. She is the co-author of the book Secrets of AI Value Creation.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Yohei Nakajima is an investor by day and coder by night. In particular, one of his projects, an AI agent framework called BabyAGI that creates a plan-execute loop, got a ton of attention in the past year. The truth is that AI agents are an extremely experimental space, and depending on how strict you want to be with your definition, there aren't a lot of production use cases today.  Yohei discusses the current state of AI agents and where they might take us.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Misha Panko has worked in data for a long time, including on high performance data teams at Uber and Google. Today, Misha is the co-founder and CEO of Motif Analytics, a product focused on helping growth and ops teams understand their event data. In this episode, Tristan and Misha nerd out about the state of the art in computational neuroscience, where Misha got his PhD. They then go deep into event stream data and how it differs from classical fact and dimension data, and why it needs different analytical tools. Make sure to check out the back half of the episode, where they dive into AI and how Motif is applying breakthroughs in language modeling to train foundation models of event sequences—check out his team's blog post on their work. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Eric Avidon is a journalist at TechTarget who's interviewed Tristan a few times, and now Tristan gets to flip the script and interview Eric. Eric is a journalist veteran, covering everything from finance to the Boston Red Sox, but now he spends a lot of time with vendors in the data space and has a broad view of what's going on. Eric and Tristan discuss AI and analytics and how mature these features really are today, data quality and its importance, the AI strategies of Snowflake and Databricks, and a lot more. Plus, part way through you can hear Tristan reacting to a mild earthquake that hit the East Coast. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.

Barry McCardel is the co-founder and CEO of Hex. Hex is an analytics tool that's structured around a notebook experience, but as you'll hear in the episode, goes well beyond the traditional notebook. We're big fans of Hex at dbt Labs, and use it for a bunch of our internal data work. In this episode, Barry and Tristan discuss notebooks and data analysis, before zooming out to discuss the hype cycle of data science, how AI is different, the experience of building AI products, and how AI will impact data practitioners. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Matt Turck has been publishing his ecosystem map since 2012. It was first called the Big Data Landscape. Now it's the Machine Learning, AI & Data (MAD) Landscape.  The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, "Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI,  ML/AI, and applications." Matt and Tristan discuss themes in Matt's post: generative AI's impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric). For full show notes and to read 7+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Matthew Lynley is a bit of a hybrid. He's been a long-time journalist covering enterprise tech, currently in his fantastic AI and data newsletter Supervised, and he's also been a hands-on data practitioner.  Matthew has covered the analytics tech stack, but this time Tristan turns the tables to get Matthew's perspective on the rise of Gen AI as a topic in the popular press, what's going on in the space today, and where AI is headed. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Juan Sequeda is a principal data scientist and head of the AI Lab at data.world, and is also the co-host of the fantastic data podcast Catalog and Cocktails.  This episode tackles semantics, semantic web, Juan's research in how raw text-to-SQL performs versus text-to-semantic layer,  and where we both believe AI will make an impact in the world of structured data analytics. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Jonathan Frankle is the Chief Scientist at MosaicML, which was recently bought by Databricks for $1.3 billion.  MosaicML helps customers train generative AI models on their data. Lots of companies are excited about gen AI, and the hope is that their company data and information will be what sets them apart from the competition.  In this conversation with Tristan and Julia, Jonathan discusses a potential future where you can train specialized, purpose-built models, the future of MosaicML inside of Databricks, and the importance of responsible AI practices. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Andy Pavlo is a professor of databaseology (he says it's a made-up word) at Carnegie Mellon and currently on leave to build his own company—OtterTune, which uses AI to figure out the settings to get the best performance out of databases. He is one of the preeminent minds on databases and a die-hard relational database maximalist. We talk about the state of databases today, why there are so many specialized databases (and if we need so many), why tuning databases is so hard but important, and how the database landscape will evolve.

Advances in ML have transformed data privacy from a regulatory necessity into an opportunity to improve the work of data people. Synthetic data for modeling + testing is one example of a hard thing that's now easy - and in this conversation with Tristan and Julia, Ian + Abhishek cover many other ways that privacy can actually be a skill that propels your work forward, rather than a mere legal best practice. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Julia just got back from Data Council in Austin, a conference organized by Pete Sonderling, where lots of startups share what they're building, data practitioners go to learn in hands-on workshops, and of course investors go to spot the next big trend. In this episode, Taylor Murphy (Head of Product & Data at Meltano) + Pedram Navid (Founder, West Marin Data) join Julia to recap the conference and have a bit of fun. They talked streaming, how the MDS is growing up, new SQL variants, and, of course, AI. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.

Sarah and Chris are both at the forefront of bringing the promise of gen AI to our actual work as data people—which is a unique challenge!  Precise truth is critical for business questions in a way that it's not for a consumer search query. Sarah Nagy is the CEO of Seek AI, a startup that aims to use natural language processing to change how professionals work with data. Chris Aberger currently leads Numbers Station AI, a startup focused on data-intensive workflow automation. In this conversation with Tristan and Julia, they dive into what this future might actually look like, and tangibly what we can expect from gen AI in the short/medium term. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

WARNING: This episode contains detailed discussion of data contracts. The modern data stack introduces challenges in terms of collaboration between data producers and consumers. How might we solve them to ultimately build trust in data quality? Chad Sanderson leads the data platform team at Convoy, a late-stage series-E freight technology startup. He manages everything from instrumentation and data ingestion to ETL, in addition to the metrics layer, experimentation software and ML.  Prukalpa Sankar is a co-founder of Atlan, where she develops products that enable improved collaboration between diverse users like businesses, analysts, and engineers, creating higher efficiency and agility in data projects.  For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Amit Prakash is Co-founder and CTO at ThoughtSpot. He has a deep background in search, having previously led the AdSense engineering team at Google and served on the early Bing team at Microsoft. In this conversation with Tristan and Julia, Amit gets real about the promise of AI in data: which applications are being widely used today, and which are still a few years out? For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

Your company has one definition for revenue across the organization, one definition of the customer, and one definition of sign-up. For people whose jobs are so defined by ensuring we're aligned, we can't seem to standardize on one definition for the Data Scientist. In this talk, Emilie Schario (Data Strategist-in-Residence at Amplify Partners and longtime dbt community member) proposes we lobby against the title Data Scientist, instead choosing some variation of the Core Four Data Roles: Data Analyst, Analytics Engineer, Data Engineer, and Machine Learning Engineer. Register to catch the rest of Coalesce, the Analytics Engineering Conference, at https://coalesce.getdbt.com. The Analytics Engineering Podcast is brought to you by dbt Labs.