dbt Labs

Brought to you by Vanta—Automate compliance. Simplify security | Superhuman—The fastest email experience ever made | AssemblyAI—Production-ready AI models to transcribe and understand speech — Julia Schottenstein is a product lead at dbt Labs, a data transformation company, and an active angel investor in data and infrastructure startups. She first got excited about dbt in 2019 when she was a VC at NEA and decided to make the leap from investor to operator by joining dbt Labs. She also co-hosts the dbt Labs Analytics Engineering Podcast, a show about data trends that impact analytics engineers’ work. In today’s episode, we discuss: • Advice for founders hoping to improve their M&A outcome • How to strategically think about competition • How to determine your paid features and have willingness-to-pay conversations • Why Julia lives by “worse is better” and “tech debt is a champagne problem” • Lessons from dbt Labs • What PMs can learn from investors — Find the full transcript at: https://www.lennysnewsletter.com/p/m-and-a-competition-pricing-and-investing — Where to find Julia Schottenstein: • Twitter: https://twitter.com/j_schottenstein • LinkedIn: https://www.linkedin.com/in/julia-schottenstein-25424318/ • Podcast: https://open.spotify.com/show/4BKMMeVXk4jJnAQSqGSJvE — Where to find Lenny: • Newsletter: https://www.lennysnewsletter.com • Twitter: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ — In this episode, we cover: (00:00) Julia’s background (04:15) How Julia went from VC to working in product at dbt Labs (08:24) Four things Julia uses to evaluate a company’s potential  (11:10) How to identify whether or not you have product-market fit (12:05) Distribution strategies (13:11) M&A strategies (15:54) Lessons from the Transform acquisition (18:01) Competitive values at dbt (20:25) Keys to dbt’s success (26:35) An offsite exercise Julia used to help her team internalize upcoming changes (29:32) Determining what features are included in open source (31:56) Pricing and willingness to pay (33:34) Lessons from dbt Labs’s first pricing change (36:33) Whether or not to be public about selling your startup (40:08) How to utilize connections during acquisitions (44:57) How to communicate selling your company (46:33) M&A market forecast (47:28) Values at dbt Labs  (50:14) Lessons from working with strongly opinionated users (52:02) The importance of shipping, learning, and iterating  (54:08) How VC skills translate into product (57:03) Lightning round — Referenced: • dbt Labs: https://www.getdbt.com/ • Tristan Handy on LinkedIn: https://www.linkedin.com/in/tristanhandy/ • dbt Labs acquires Transform to enhance Semantic Layer tool: https://www.techtarget.com/searchbusinessanalytics/news/365530993/DBT-Labs-acquires-Transform-to-enhance-Semantic-Layer-tool • Snowflake: https://www.snowflake.com/en/ • Gödel, Escher, Bach: An Eternal Golden Braid: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567 • Red strings training clip from Ted Lasso: https://www.youtube.com/watch?v=aVe3Iwy10MA • Monetizing Innovation: https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867 • Madhavan Ramanujam on Lenny’s Podcast: https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan#details • Pricing survey: https://www.qualtrics.com/marketplace/vanwesterndorp-pricing-sensitivity-study/ • Hunter Walk’s blog post about publicly selling your startup: https://hunterwalk.com/2023/05/13/the-acquihire-market-for-early-stage-startups-is-ice-cold-one-better-strategy-announce-youre-for-sale/ • Range: Why Generalists Triumph in a Specialized World: https://www.amazon.com/Range-Generalists-Triumph-Specialized-World/dp/0735214506/ • The Snowball: Warren Buffett and the Business of Life: https://www.amazon.com/Snowball-Warren-Buffett-Business-Life/dp/0553384619/r • Sam Walton: Made in America: https://www.amazon.com/Sam-Walton-Made-America/dp/0553562835 • Succession on HBO: https://www.hbo.com/succession • In Depth podcast: https://review.firstround.com/podcast • dbt community Slack: https://www.getdbt.com/community/join-the-community/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com. — Lenny may be an investor in the companies discussed. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

Julia Schottenstein

Product Lead

dbt Labs

Tristan Handy is the Founder and CEO @ dbt, a data transformation tool that enables data analysts and engineers to transform, test and document data in the cloud data warehouse. To date, Tristan has raised over $400M from the likes of Sequoia, Altimeter, Coatue, ICONIQ and GV with the latest funding round valuing the company at $4.2BN. Prior to founding dbt, Tristan was the VP Marketing @ RJ Metrics and got his break in the world of startups through former 20VC guest, Anthony Casalena with a Director of Operations role at Squarespace. In Today's Episode with Tristan Handy: 1.) Entry into Startups: How did Tristan make his way into the world of startups with his first role at Squarespace? How did Tristan's time with Squarespace impact how he builds dbt today? What does Tristan know now that he wishes he had known when he founded dbt? 2.) Our Jobs Are Not That Hard: Why does Tristan believe that our jobs are not that hard? If going down this line, how does Tristan hire? What does he look for? How does he test for it? When does experience matter? When does it not matter? 3.) dbt: The Company Why does Tristan believe that remote work does not work? What financial packages have dbt put in place to allow their employees this physical interaction? What does Tristan believe is the hardest element of building a hybrid company? When does everything start to break? What are the biggest lessons Tristan and dbt have taken from Gitlab? 4.) Tristan: The Leader How does Tristan conduct and execute on the best performance reviews? How does Tristan create an environment of safety where people feel they can be honest and transparent? What are the elements that you cannot be transparent on? Where does transparency break down? 5.) Trading Freedom for Scale: dbt could have been a small and super profitable company, why did Tristan decide to trade off the freedom and raise big from VCs? How did Tristan raise over $414M without ever talking about an efficiency metric? Is Tristan concerned about living into the $4.2BN valuation in what is a very different time? With the benefit of hindsight, is Tristan pleased he went big and raised venture?

Tristan Handy

Founder

dbt Labs

Tristan Handy is CEO and founder of dbt Labs. dbt (data build tool) simplifies the data transformation workflow and helps organizations make better decisions. Lukas and Tristan dive into the history of the modern data stack and the subsequent challenges that dbt was created to address; communities of identity and product-led growth; and thoughts on why SQL has survived and thrived for so long. Tristan also shares his hopes for the future of BI tools and the data stack. Show notes (transcript and links): http://wandb.me/gd-tristan-handy --- ⏳ Timestamps: 0:00 Intro 0:40 How dbt makes data transformation easier 4:52 dbt and avoiding bad data habits 14:23 Agreeing on organizational ground truths 19:04 Staying current while running a company 22:15 The origin story of dbt 26:08 Why dbt is conceptually simple but hard to execute 34:47 The dbt community and the bottom-up mindset 41:50 The future of data and operations 47:41 dbt and machine learning 49:17 Why SQL is so ubiquitous 55:20 Bridging the gap between the ML and data worlds 1:00:22 Outro --- Connect with Tristan: 📍 Twitter: https://twitter.com/jthandy 📍 The Analytics Engineering Roundup: https://roundup.getdbt.com/ --- 💬 Host: Lukas Biewald 📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla --- Subscribe and listen to our podcast today! 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Google Podcasts: http://wandb.me/google-podcasts​ 👉 Spotify: http://wandb.me/spotify​

Tristan Handy

Founder

dbt Labs

Over a decade after the idea of “big data'' was first born, data has become the central nervous system for decision-making in organizations of all sizes. But the modern data stack is evolving and which infrastructure trends and technologies will ultimately win out remains to be decided. In this podcast, originally recorded as part of Fivetran's Modern Data Stack conference, five leaders in data infrastructure debate that question: a16z general partner and pioneer of software defined networking Martin Casado, former CEO of Snowflake Bob Muglia; Michelle Ufford, founder and CEO of Noteable; Tristan Handy, founder of Fishtown Analytics and leader of the open source project dbt; and Fivetran founder George Fraser. The conversation covers the future of data lakes, the new use cases for the modern data stack, data mesh and whether decentralization of teams and tools is the future, and how low we actually need to go with latency. And while the topic of debate is the modern data stack, the themes and differing perspectives strike at the heart of an even bigger: how does technology evolve in complex enterprise environments?  We're re-running this episode as part of a special report on Future.com, the Data50: the World's Top Data Startups, which covers the bellwether private companies across the most exciting categories in data, from AI/ML to observability and more.  Stay Updated: Find a16z on YouTube: YouTube Find a16z on X Find a16z on LinkedIn Listen to the a16z Show on Spotify Listen to the a16z Show on Apple Podcasts Follow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Bob Muglia

Former CEO

Snowflake

Michelle Ufford

Founder and CEO

Noteable

Tristan Handy

Founder

dbt Labs

George Fraser

Founder

Fivetran

Lakes v. warehouses, analytics v. AI/ML, SQL v. everything else... As the technical capabilities of data lakes and data warehouses converge, are the separate tools and teams that run AI/ML and analytics converging as well? In this podcast, originally recorded as part of Fivetran's Modern Data Stack conference, five leaders in data infrastructure debate that question: a16z general partner and pioneer of software defined networking Martin Casado, former CEO of Snowflake Bob Muglia; Michelle Ufford, founder and CEO of Noteable; Tristan Handy, founder of Fishtown Analytics and leader of the open source project dbt; and Fivetran founder George Fraser. The conversation covers the future of data lakes, the new use cases for the modern data stack, data mesh and whether decentralization of teams and tools is the future, and how low we actually need to go with latency. And while the topic of debate is the modern data stack, the themes and differing perspectives strike at the heart of an even bigger: how does technology evolve in complex enterprise environments?  Stay Updated: Find a16z on YouTube: YouTube Find a16z on X Find a16z on LinkedIn Listen to the a16z Show on Spotify Listen to the a16z Show on Apple Podcasts Follow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Bob Muglia

Former CEO

Snowflake

Michelle Ufford

Founder and CEO

Noteable

Tristan Handy

Founder

dbt Labs

George Fraser

Founder

Fivetran

Summary In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can start using it today to create reliable and well-tested reports in your favorite data warehouse. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Drew Banin about DBT, the Data Build Tool, a toolkit for building analytics the way that developers build applications Interview Introduction How did you get involved in the area of data management? Can you start by explaining what DBT is and your motivation for creating it? Where does it fit in the overall landscape of data tools and the lifecycle of data in an analytics pipeline? Can you talk through the workflow for someone using DBT? One of the useful features of DBT for stability of analytics is the ability to write and execute tests. Can you explain how those are implemented? The packaging capabilities are beneficial for enabling collaboration. Can you talk through how the packaging system is implemented? Are these packages driven by Fishtown Analytics or the dbt community? What are the limitations of modeling everything as a SELECT statement? Making SQL code reusable is notoriously difficult. How does the Jinja templating of DBT address this issue and what are the shortcomings? What are your thoughts on higher level approaches to SQL that compile down to the specific statements? Can you explain how DBT is implemented and how the design has evolved since you first began working on it? What are some of the features of DBT that are often overlooked which you find particularly useful? What are some of the most interesting/unexpected/innovative ways that you have seen DBT used? What are the additional features that the commercial version of DBT provides? What are some of the most useful or challenging lessons that you have learned in the process of building and maintaining DBT? When is it the wrong choice? What do you have planned for the future of DBT? Contact Info Email @drebanin on Twitter drebanin on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links DBT Fishtown Analytics 8Tracks Internet Radio Redshift Magento Stitch Data Fivetran Airflow Business Intelligence Jinja template language BigQuery Snowflake Version Control Git Continuous Integration Test Driven Development Snowplow Analytics Podcast Episode dbt-utils We Can Do Better Than SQL blog post from EdgeDB EdgeDB Looker LookML Podcast Interview Presto DB Podcast Interview Spark SQL Hive Azure SQL Data Warehouse Data Warehouse Data Lake Data Council Conference Slowly Changing Dimensions dbt Archival Mode Analytics Periscope BI dbt docs dbt repository The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Drew Banin

Creator

dbt Labs