dbt Labs

Your SQL query isn’t working. Solution 1: Debug the issue Solution 2: Post a meme about it in the dbt Community Slack Why not both? Join the dbt Community to:  • network with other data humans like you • get your questions answered • knowledge share • laugh at memes only data people get Become a member 👉 https://lnkd.in/dnMVYqdp

$100 million ARR calls for a virtual toast—and a whole lot of confetti 🎉 What started as an open-source project has grown into a billion-dollar business, driven by an incredible community, world-class customers, and a team that never stops innovating. Read more about our journey in Forbes: https://lnkd.in/g5jBUdaw

We have officially surpassed $100 million in annual recurring revenue (ARR) and now support over 5,000 customers worldwide 🚀 This milestone reflects the incredible trust and collaboration of the dbt Community, our partners, and our customers, including 85% year-over-year adoption growth among Fortune 500 companies. Together, we’re driving the future of data transformation and analytics engineering. Check out the full announcement here: https://lnkd.in/geXcgu45 Here’s to scaling new heights, together 🙌

We've been talking about tools "comprehending SQL" a lot recently—what does that actually mean? It isn’t a binary thing. There are three different levels of capability: 🌿 Parser – Recognizes keywords and checks if your syntax is valid. ⚙ Compiler – Understands data types, transformations, and dependencies to validate your logic. 🏃♀️ Executor – Actually runs the query and delivers results. Each level helps you be more effective as you write, debug, and optimize your SQL. Using tools that understand how SQL works behind the scenes makes it easier for you to write queries that are scalable, efficient, and trustworthy. Read the blog (link in comments) for a more in-depth look at each level.

🚀 dbt is about to get a whole lot faster. Last week’s Accelerating dbt with SDF webinar had the community buzzing. The live chat was on fire, and Jason Ganz summed it up perfectly: “There are some complaints that now people aren't going to be able to go and get a coffee or check the dbt Slack, because it's just going to be too fast.” If you missed it, you can still catch the on-demand webinar. Watch Lukas Schulte’s demo showcasing SDF capabilities, including: • Faster execution with local query validation • SQL comprehension • Lineage and metadata propagation Watch it here: https://bit.ly/4alC7fg

Your AI is only as good as your data. Without a semantic layer, AI systems are left guessing. That leads to: 🚨 Misinterpreted metrics 🚨 Conflicting business definitions 🚨 Security risks and compliance gaps To succeed, AI needs: 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 – One definition of “revenue,” not five. A semantic layer ensures AI always queries the right business logic. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 – Sensitive data stays protected, access is controlled, and changes don’t break everything downstream. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – AI needs more than raw data; it needs relationships, metadata, and logic to understand the "why" behind the numbers. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Precomputed, validated metrics make AI-powered insights faster and more reliable—so teams actually trust them. Check out the blog (link in comments) to learn more.

This week on The Analytics Engineering Podcast, Tristan Handy sits down with Daniel Avancini, Chief Data Officer and Co-founder of Indicium—a fast-growing data consultancy started in Brazil. What sets Indicium apart is their HR model. Instead of focusing on experienced hires, they’ve built a talent pipeline through courses and an internal onboarding program, training new employees from zero to 60 in just a few months. The result? Exceptional client outcomes and hundreds of new data professionals gaining the skills to succeed in a field that’s notoriously hard to break into. Companies investing in scalable hiring and training for analytical talent are shaping the future of data. Indicium is one of them. 🎧 Listen to their full conversation here https://lnkd.in/g5MX2k6b or wherever you get your podcasts.

We’ve been named one of the top 100 midsized companies to work for 🎉 Thank you to Built In for the recognition—and to our amazing team at dbt Labs for building a culture that’s as impactful as the work we do. And we’re just getting started: our team is growing globally. If you’re ready to shape the future of data, check out our latest openings https://lnkd.in/gt2vdiB7

What’s new in dbt Cloud? (spoiler alert: A LOT) 🚀 We’re only a few weeks into 2025, and we’ve added a bunch of new dbt Cloud features to help you ship trusted data faster, optimize costs, and collaborate across teams. 👉 See what’s new and read the blog (link in comments) for the full breakdown.

🧪 Test smarter, not harder. We’re wrapping up our series on the four layers of data testing with the marts layer. At this layer, focus on net-new columns and high-priority business rules, following the same hygiene-or-anomaly approach as staging and intermediate layers. What to test in the marts layer: 𝗨𝗻𝗶𝘁 𝘁𝗲𝘀𝘁𝘀  Validate complex transformation logic, such as: 📅 Calculating dates for forecasting models. 🎯 Customer segmentation logic with multiple CASE-WHEN statements. 𝗣𝗿𝗶𝗺𝗮𝗿𝘆 𝗸𝗲𝘆 𝘁𝗲𝘀𝘁𝘀 🔑 Apply to models where your mart’s granularity has changed. 🧩 For enriched models (even without a grain change), primary key tests communicate your intent to future developers. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗮𝗻𝗼𝗺𝗮𝗹𝘆 𝘁𝗲𝘀𝘁𝘀 Focus on critical fields and specific business problems: 🧠 Detect fuzzy matches, such as a user with multiple emails extending free trials. 📊 Ensure calculated fields don’t vary beyond a set percentage in a week. 💲 Verify ledger tables follow rules, like today’s total always exceeding yesterday’s. Testing at the marts layer keeps your data aligned with business rules and ensures high integrity, even for complex scenarios.