dbt Labs

Two years ago, I founded SDF Labs with my father Wolfram Schulte and our two phenomenal co-founders Michael Y. Levin and Elias DeFaria. We had a bold vision; to bring deeper SQL understanding, formal reasoning, and static analysis to data development. In other areas of software engineering precise error reporting, dependency tracking, and optimized code generation are powered by advanced tooling grounded in formal semantics. SQL tooling has long been left behind. Our mission has been to bridge that gap and redefine what's possible for SQL developers. I’m beyond excited to share that dbt Labs has acquired SDF Labs! While dbt’s capabilities are so comprehensive as to drive an entire ecosystem, it has historically been limited in one fundamental way: dbt only understands SQL statements only as a series of strings. Today, that changes. We anticipate bringing the full suite of SDF’s static analysis capabilities, speed, and scalability into the dbt ecosystem. 🚀 To read more on that, check out Tristan Handy's post here https://lnkd.in/gbYUxj-E I have so many people and institutions to thank for making this possible. Most importantly to the SDF team; thank you for your drive, dedication, and endless creativity. One chapter is closing, but we’re turning the page onto something even brighter, bolder, and more exciting.

We’re very excited about this acquisition and what it will mean for dbt users everywhere 🚀 Read the press release: https://lnkd.in/gteiZ4ST

What happens when a new CEO with deep AI experience takes the helm of a major data platform? Transformation. 🔄 In this episode of The Analytics Engineering Podcast, Rahul Jain 🇮🇳 breaks down Snowflake's evolution pre- and post- Sridhar Ramaswamy’s arrival: ➡️ The rise of Cortex: simplifying LLM and machine learning functions, making AI in SQL as intuitive as it is powerful. ➡️ The philosophy shift: From building advanced tech to empowering users to "use AI in seconds." ➡️ What this means for you: Democratized, governed access to cutting-edge AI, right in your SQL workflow. 🎧 Catch the full conversation: https://lnkd.in/grunQkbM

Unlock new ways to collaborate and trust your data 🔓 We’re bringing you the third edition of One dbt, our virtual event series designed to help you explore, improve, and trust your data assets with dbt Cloud. What’s on the agenda: 💡 Eliminate silos and build trust: Gain a full view of your data estate with insights into dependencies, lineage, and quality. ⛅ Understand downstream impact: Auto-context for data usage helps you refine and enhance your products. ✨ Track model usage: Prioritize development with query history insights. 📊 Share data quality signals: Build stakeholder confidence with embedded health tiles in downstream apps. 🛠 Validate with Advanced CI: Catch issues before they impact your workflows. Why join live? • Ask the experts: Live Q&A with Roxanna Dahlke and Alexis Jones • Get a sneak peek: See what’s next on dbt’s product roadmap 👉 Save your seat: https://bit.ly/4abjNW2

If you’re a data person who loves a good laugh (and maybe needs a little help from time to time), the dbt Slack Community is where you belong. Join the dbt Community to: • Connect with data humans who just get it 🤝 • Get your toughest questions answered 🧠 • Share knowledge and discover new ideas 💡 • Laugh at memes only data people understand 😂 👉 Become a member today: https://lnkd.in/dnMVYqdp

“All we needed to leverage for our customer-facing visualizations in our B2B portal was the data. Once we centralized data transformations with dbt and their Semantic Layer, we could easily create the visualizations to our front end. It became super simple." By migrating off BI tool embeds, Bilt Rewards achieved 80% cost savings while empowering their teams with faster, more reliable insights. See how they transformed their data workflows: https://lnkd.in/gWY_F3BH

🧪 Test smarter, not harder. We’re breaking down the 4 layers of data testing in your pipeline. First up: the sources layer. At this layer, tests should surface issues that can be fixed at the source system. If a test flags something that isn’t fixable at the source, remove it and address the problem in your staging layer instead. What to test at the sources layer: Freshness • 🚨High-priority sources: Use dbt source freshness, set severity to error, and fail jobs if freshness fails. • ⚠️Lower-priority sources: Set severity to warn—track freshness without breaking pipelines. Data hygiene Focus on identifying issues that are fixable in the source system, such as: • 🗂️ Duplicate customer records that can be removed at the source. • ❌ Nulls (e.g., missing names or emails) that should be filled in at the source. • 🔑 Duplicate primary keys that can be resolved upstream. Testing smarter at the sources layer builds trust in your data and prevents unnecessary complexity downstream.

DuckDB redefines how analytical compute happens—handling massive CSVs and Parquet files effortlessly, right on your local machine. It’s optimized for low-latency, exploratory workflows, bridging back-end data processing with front-end UIs. 🎙️ In the latest episode of the Analytics Engineering Podcast, Hamilton Ulmer from MotherDuck shares how DuckDB is becoming essential for modern data visualization and analysis. Catch the full episode to explore how DuckDB is reshaping analytics workflows (link in comments).

👉 Swipe through to see how you can make the most of dbt Explorer, and read the blog to dive deeper into these tips https://lnkd.in/gk3Sd64x Which of these tips are you using today? What are you going to try out now that you know about it?

Scaling analytics doesn’t have to feel overwhelming. 10 data leaders share practical advice for building scalable, reliable data strategies. Here are some key takeaways: “Focus on the key problems that drive the most value for the business, and then treat those problems like modular building blocks that you use to build all the analytics out of.” — Scott G. Parent, Eleanor Health “Once you’re transparent and once you get more people involved into your data project, then you will see data analysts and analytics engineers be born in other teams.” — Valentinas Mitalauskas, Hostinger "Make it really easy for everyone at your company to resolve at least 80% of the questions that come in.” — Kyle Salomon, LiveRamp Read all 10 tips here: https://lnkd.in/eH4vwFdw