dbt Labs

Meet the dbt Developer Agent 👋 It lives right in dbt Studio, so you see every change in context directly in the IDE, right alongside the code you're changing. Describe what you want to make, rename a model, update a column, add a metric, and the agent analyzes your full dbt graph. Not just file dependencies, but lineage, contracts, semantic definitions, tests, and governance. Other agents know your repo. The dbt Developer Agent is grounded in your dbt project. No configuration. No MCP to maintain. Just the right answer, with governance and guardrails built in by default. Full details in the blog (link in comments).

𝗥𝗲𝗰𝗮𝗽: 𝗱𝗯𝘁 𝘅 𝗕𝗨 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 🚀 We’re grateful to our partners at dbt Labs — especially Akash Trivedi, DPhil for creating this terrific opportunity for our students and Shania Thomas for ✈️ Austin-to-Boston travel to facilitate the day-long event! MSBA students, alongside others from BU, participated in a guided dbt build followed by a 𝗝𝗮𝗳𝗳𝗹𝗲 𝗦𝗵𝗼𝗽 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻, applying analytics engineering skills in a hands-on environment. And yes a jaffle is a real food ... if you've had one before, let us know! 𝗔 𝗯𝗶𝗴 𝘁𝗵𝗮𝗻𝗸 𝘆𝗼𝘂 𝘁𝗼 𝗼𝘂𝗿 𝗷𝘂𝗱𝗴𝗲𝘀 for generously sharing their time and experience: - Joel Labes logging in from dbt Labs - Andrew Masnyj, BU alum and Data Viz/Analytics Practice Team Lead at Cleartelligence - Mustafa Kutay Ebiclioglu, BU alum and Data Engineer at CarGurus - William Tsu, dbt community enthusiast and Analytics Engineer WHOOP 𝗖𝗼𝗻𝗴𝗿𝗮𝘁𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗼𝘂𝗿 𝘄𝗶𝗻𝗻𝗲𝗿𝘀: 🥇 Stephanie C. (1st) 🥈 Sarah Julia Dsouza (2nd) 🥉 Erin Z. (3rd) We hope all participants learned something new — and we’re excited for continued collaboration with dbt Labs! #QuestromMSBA #dbt #AnalyticsEngineering #Hackathon #DataAnalytics

New dbt updates just dropped. Fusion is self-serve, your AI tools now connect to dbt with OAuth, dbt Core 1.12 is in Beta, the Studio IDE finally has search and replace, and more. See everything that shipped since January ➡️

SafetyCulture's data team was spending over 14 hours processing a single day of data, with no visibility into what changed or when. Trusted decisions were nearly impossible to make. Thiago Baldim and Yuna(Yunnan) Tang share how they rebuilt their data platform on dbt, turning fragmented pipelines into a fully documented, fully tested foundation for AI. The same data that once took 14 hours to process now runs in 90 minutes. New team members onboard and contribute without the guesswork. And AI product adoption is growing every single day. "Without dbt, we would never have been AI-ready.”

AI agents can now pull dbt docs directly from the source. The dbt product docs team added docs tooling to the dbt MCP server, so agents fetch from docs.getdbt.com as a live source of truth, right inside your existing workflow. Since launching in March: - 1,000+ unique accounts using the tools - 6,000+ docs-tool calls in May alone Mirna Wong shares how the team got here, what the data showed, and what's coming next https://lnkd.in/gvjTUkYE

Jason Ganz spent last week at AI Council. In this week's Analytics Engineering Roundup, he breaks down what current benchmarks get right, where they fall short, and why testing agents in sandboxes may be the field's biggest blind spot. Read it here: https://lnkd.in/g35CirDQ

If you know, you know 😭 The dbt Community Slack is full of people who know. Inside the dbt Community, you'll find: • 70,000+ data practitioners building with the modern data stack • Spaces to ask questions, share wins, and swap ideas • Real-time support from experts and peers • Channels for events, meetups, job opps—and yes, memes Join the conversation: https://lnkd.in/dnMVYqdp

90% fewer workflows. 60% faster pipelines. Data delivered more than an hour earlier, every day. That's what Kaizen Gaming's analytics engineering team achieved after standardizing on dbt across their data organization. The team had grown fast (more than doubling in under six months), and their analytics environment needed to keep pace. Notebook-driven workflows, duplicated logic, and inconsistent practices across domains made it hard to scale with confidence. With dbt, they built a layered modeling structure, automated testing on every pull request, and Slim CI for data quality, all while cutting daily pipeline costs by approximately 60%. Read the full story https://lnkd.in/gmsXvuzd

We're heading to #SnowflakeSummit ❄️ Hear from customers like CarGurus and Fanatics on how they're building with dbt, plus sessions on AI-ready data, natural language analytics, and what's new in the dbt Fusion engine on Snowflake. Swipe to explore the sessions, and save this for when you're planning your schedule. 📍 Find us at booth 2112 all week. Book time with our team here: https://lnkd.in/gbPBaTRw