dbt Labs

The next generation of analytics engineers is out there, and we're going to them. Last month, Connor McArthur, Erin Vaughan, and Anna Lee headed to Temple University for dbt Labs' first in-person University Partners event. They covered: 🔸What dbt is and how it fits into the AI ecosystem 🔸How to get involved with the dbt community 🔸The path from university to the data workplace For students who couldn't make it to campus, "Analytics Engineering for Students" brings the same foundation online. It's a free, beginner-friendly virtual training that takes you from raw data to insights using dbt, covering the full analytics development lifecycle from the ground up https://lnkd.in/g6vRR3fJ

Busta Rhymes told us to put our hands where his eyes could see. We're doing one better, putting him on stage 🎤 Data and AI are changing everything. On June 3rd, they're changing your plans. dbt MAINSTAGE is a one-night-only concert experience for the people shaping the future of data and AI. Invite only. Open bar. No #SnowflakeSummit badge required. Request your spot https://lnkd.in/g7Cw6jk3

If you’re already on Databricks, you may be asking: Do we really need dbt Labs too? According to Keith Ludeman (and what we see in real client environments) the answer is almost always yes. Yes, the tools do overlap in some areas. But the environments that scale best give each technology a clear role: where transformation happens, where governance lives, and how business logic is managed. We’ve delivered across both ecosystems for years, and the pattern is consistent: teams that get this right spend less time maintaining pipelines and more time creating new capabilities. A few practical lessons from real implementations: → Use each tool for what it does best. Databricks is excellent for landing, storing, and scaling raw data. dbt Labs brings structure, testing, documentation, and governed transformation layers that make data usable across the business. → Keep transformation logic in one place. When logic gets split across notebooks, scripts, and workflows, lineage breaks and debugging becomes a research project. → Architecture decisions matter early. How and when you introduce dbt Labs into your Databricks stack has a major impact on speed, governance, and long-term maintainability. 🔖Keith breaks down the full decision framework here: https://lnkd.in/g3r-CJvF

dbt Labs has been named a 2026 Google Cloud Partner of the Year for Data and Analytics: Data Pipelines and Governance 🏆 Thousands of organizations run dbt on Google BigQuery globally, and this recognition reflects what's possible when two teams are aligned on helping customers build trusted, AI-ready data pipelines. One year since launching on Google Cloud Marketplace, two Google Partner All Star awards, and a growing community of practitioners building AI-ready data pipelines on Google Cloud. Congrats to everyone on both teams who made this happen 👏 #GoogleCloudPartnerAwards

Nobody sees the real-world impact of an engine rewrite like the people fielding support tickets every day. Anders Swanson sat down with Jeremy Yeo and Shelli W. from the Customer Solutions Engineering team to get their take on what's different about the dbt Fusion engine. They get into: • What made dbt Core simple and what Fusion's added complexity buys you • How Fusion's built-in debugging tools change the support experience • Why state modified explainability is a meaningful quality-of-life improvement • How the new system report feature cuts down on back-and-forth • What detailed Fusion logging unlocks compared to dbt Core's SQL-only output • Why switching between Fusion versions is a one-liner instead of a Python virtual environment headache

350+ packages. 4,200+ versions. One engine to support them all. Getting users onto the dbt Fusion engine is one thing. Getting the entire package ecosystem ready for them is a whole different challenge. Chaya Carey has been six months deep in that work. Anders Swanson sat down with Chaya to talk through what that looks like. They cover: • Why Jinja flexibility is both a superpower and a migration challenge • How Package Hub evolved to surface Fusion compatibility info • What dbt autofix handles automatically so you don't have to • How the team went from manual package reviews to automated Fusion parsing at scale

Agents will be the primary consumers of analytic data within 12 months. I realize that's a big claim. Here's why I believe it. Over the past decade, we invested massively in data infrastructure. It worked — the bottleneck moved. But we just revealed a new one. We never actually fixed *analysis*. Analysis, mostly, is just *thinking*, and we hadn't solved thinking yet. Well, that bottleneck just vanished. Meta went from weekend prototype to a company-wide analytics agent used by thousands — in six months. OpenAI built their own. Ramp built Ramp Research. The dbt MCP server has grown 50% month-over-month since launch. These aren't experiments. They're in production. The analyst role isn't disappearing. But it is fundamentally changing. The analysts who thrive will be building and operating agentic systems, not shipping dashboards. How quickly do you think your org will cross that threshold? I write about this every week in the Analytics Engineering Roundup — link in my profile if you want to subscribe. #AnalyticsEngineering #DataAnalytics #AIAgents #dbt

Who wants to learn how to optimize their compile times in dbt? How many of you have waited 10+ minutes for a CI job just to start running a small state:modified` subset? Well I have 2 recs: 1. Adopt Fusion if you haven't already. Parse times are way faster, same with compile times. 2. Listen to Anders Swanson interview my partner and engineering lead for Fusion, Alexander Bogdanowicz. He'll tell you about how certain patterns in Jinja can ruin performance of compile and cause all commands that depend on it (run, build, etc.) to take unnecessarily long. https://lnkd.in/gXwPkngm

The analytics world is shifting faster than most people realize. This week's issue of The Analytics Engineering Roundup features Tristan Handy's take on where analytics is heading and what to do about it https://lnkd.in/gwMhnXAg