We recently released dbt Wizard, a CLI for doing agentic data work on dbt. This is a harness specifically built for data work and as such I was excited to spend an afternoon running dbt Wizard across a personal project with about 100 models or so that I've been maintaining for a while. You can use this project to see firsthand some of the differentiators for Wizard, particularly how the validation loop works.

Data projects are never done and I had a few tasks I've been putting off:

• Migrating from MotherDuck to Iceberg + BigQuery
• Upgrading dbt Core to dbt Fusion
• Migrating from dbt Core to dbt platform
• Building out a semantic layer

These are a good mix of pretty standard data work (have you done your annual migration yet?) and some dbt specific tasks. A good informal eval for the fundamental question for any dev tool: Does it work?

I was pleasantly surprised with my experience in that I didn't have to babysit Wizard or build any additional workflows on top of it. Because it has a native understanding of what a dbt project is, and its ability to set up validation subagents that match the task made it easy for me to just pop in when needed and I didn't have to babysit the whole process and I accomplished all of my objectives.

When building a dbt project, you start with the simplest strategy and only add complexity when you need more speed or reduced cost. At a model-by-model level, this is the view → table → incremental materialization pattern. At a project-wide level, optimizations used to require a lot more complexity - rebuilding only the relevant parts of a project meant you spent a lot of mental energy chaining together selectors like state:modified, source_status:fresher, or config.materialized:view and ensuring fresh JSON artifacts were distributed across your organization.

dbt State provides more speed, reduced cost, and less complexity all in one. It works wherever you do: dbt Core or Fusion, locally or on any orchestrator you choose. Since it's backed by a new paid service (with a 30 day free trial), you don't need to adopt the whole dbt platform to get these savings. dbt State supersedes state-aware orchestration, which required a dbt platform subscription and moving to Fusion.

The most technically interesting part of this is the query normalization process1 which means running the new dbt lint command doesn't instantly sign you up for a full rebuild. But before getting into the depths of syntax trees, let's recap what dbt State actually does for an end user.

The basic dbt State workflow​

When dbt runs, it will check with the dbt State server whether a model:

• already exists in the target schema and can be reused
• already exists in another schema and can be reused, or
• needs to be built from scratch.

Today, we published the first alpha release of dbt Core version 2.0, raising the baseline for what dbt users can expect from their transformation tool. As always, dbt Core's code is completely open source under the Apache 2.0 license. What makes this release significant is that dbt Core v2.0 is now built on the same foundations as the dbt Fusion engine – we've open-sourced a lot of Fusion code for the first time.

The launch of dbt Core v2.0 comes with key feature developments:

• Significant parse time improvements, especially on the largest dbt projects
• A tightly-defined language spec which makes it impossible to accidentally configure a desciptin instead of a description, and gives anyone integrating with the dbt ecosystem (including dbt's own development team!) a stable interface to build against.
• New Parquet artifacts as a high-performance alternative to large JSON files that can be directly queried through DuckDB or your agent of choice
• A completely revamped local documentation experience, powered by those new artifacts and capable of scaling to projects of arbitrary size.
• A more streamlined way to build new adapters, powered by ADBC and the Arrow ecosystem
• A simplified installation process that removes the need to fight with Python's virtual environments

"By all means, move at a glacial pace." — Miranda Priestly, The Devil Wears Prada

There's another scene in The Devil Wears Prada that I think about more than that one. If you haven't seen it yet, I'll try not to spoil it for you. Miranda turns to Andy (Miranda's assistant) and explains, with such impatience, that the cerulean blue in Andy's "lumpy sweater" didn't come out of thin air — it's traced back through a chain of deliberate fashion decisions made years earlier, by people who thought carefully about every choice. The sweater Andy bought from the store was the end of a long chain of deliberate curation, carefully veiled to protect the illusion that the sweater effortlessly came into existence.

That's how I'd describe documentation, especially in the AI era. A user — could be a developer, analyst, data engineer, tech writer 😜 — asks an AI tool a question and gets an answer. They don't need to see every decision in the chain behind it: what to include, how to structure it, where the gaps are, what needs updating. But those decisions shape every answer they get. Somewhere upstream, a docs team is making careful, deliberate choices that ripple all the way down to the moment the answer lands in the user's editor.

This blog discusses how docs teams are trying to bring those decisions closer to users — and why that architecture matters more than ever in the AI era.

It's the multitrillion-dollar question that's haunting everyone, from senior software developers and junior lawyers to Hollywood writers and, of course, data analysts and engineers: Can the robots do our jobs?

For us data people, in various forms, the signs are mixed. According to recent research from Anthropic, data scientists are already using AI to augment or automate 46 percent of their work. In two years, LLMs got good at writing code; they are now also very good at writing SQL.1

But, as every data person knows, writing queries is not our job; not really. For better or for worse (and perhaps better, if the great code-writing disruption is already here), our job is, as Caitlin Moorman put it over six years ago, glue work. We map this team's data to that team's goal; we connect questions over here to trends over there. We deal with ambiguity; we say "it depends." We tell people no.

There are two primary ways to get answers from your data using LLMs today: have the model write SQL directly, or have it query through a structured ontology like the dbt Semantic Layer. Both work. Companies are getting real value from each. But they fail in very different ways, and understanding those failure modes is what actually matters when you're deciding which to use.

In 2023, we ran a benchmark comparing the two approaches and the Semantic Layer won handily. But 2023 is roughly 10 million years ago in LLM time. Models have gotten dramatically better at writing SQL. So we reran the benchmark with the latest generation models to see whether the gap has closed.

The Catalog Linked Database Diaries: On Freshness and Writes​

Last November, at dbt Summit, Jeremy introduced dbt’s multi-platform Iceberg capabilities.

What intrigued us most was the promised interconnectivity of Databricks Unity Catalog and Snowflake catalog-linked databases.

AI’s all the rage, but another little revolution is taking shape: Teams are breaking their data storage out of vendor-specific platforms. For months, we have been chatting with users excited to adopt Iceberg as a core pillar of their data architecture. The Iceberg table format and Iceberg REST catalogs are the emerging standards powering that flexibility.

For dbt’s part, this shows up in two concrete use cases:

• dbt projects at scale: Teams share one logical database, with many schemas and hundreds to thousands of tables
• Cross-platform mesh: One project in Snowflake, one in Databricks, sharing data without juggling manual refreshes or metadata pointers

Community-driven creation and curation of best practices is perhaps the driving factor behind dbt and analytics engineering’s rise - transferrable workflows and processes enable everyone to create and disseminate organizational knowledge. In the early days, dbt Labs’ Fishtown Analytics’ dbt_style_guide.md contained foundational guidelines for anyone adopting the dbt viewpoint for the first time.

Today we released a collection of dbt agent skills so that AI agents (like Claude Code, OpenAI's Codex, Cursor, Factory or Kilo Code) can follow the same dbt best practices you would expect of any collaborator in your codebase. This matters because by extending their baseline capabilities, skills can transform generalist coding agents into highly capable data agents.

dbt agent skills allow you to transform generalist coding agents into highly capable data agents

These skills encapsulate a broad swathe of hard-won knowledge from the dbt Community and the dbt Labs Developer Experience team. Collectively, they represent dozens of hours of focused work by dbt experts, backed by years of using dbt.

With access to skills, agents like Claude take a systematic approach to tasks

New engine, who dis?​

It’s unlikely that anyone reading this blog has not heard about the new dbt Fusion engine — it’s been the talk of the data town since last January, culminating in Elias’s legendary live Coalesce 2025 demo of the incredible capabilities that native SQL comprehension in dbt can unlock. If you attended Coalesce, or have upgraded your project to Fusion already, you’ve likely also heard about the changes we’ve made to the authoring layer of dbt (the literal code you write in your project). As part of the major version upgrade, we took the opportunity to simplify + standardize the configuration language of dbt to be built to scale as we enter the next era of analytics engineering.

In particular, we wanted to reevaluate how metrics are defined in the dbt Semantic Layer. We’ve heard from numerous community members over the years that defining metrics was just plain hard. In conversation with internal + external users and our newest pals from SDF, we’ve come up with a redesigned YAML spec that is simpler, more integrated to the dbt configuration experience we’ve come to know and love, and built for the future.

In April, we released the local dbt MCP (Model Context Protocol) server as an open source project to connect AI agents and LLMs with direct, governed access to trusted dbt assets. The dbt MCP server provides a universal, open standard for bridging AI systems with your structured context that keeps your agents accurate, governed, and trustworthy. Learn more in About dbt Model Context Protocol.

Since releasing the local dbt MCP server, the dbt community has been applying it in incredible ways including agentic conversational analytics, data catalog exploration, and dbt project refactoring. However, a key piece of feedback we received from AI engineers was that the local dbt MCP server isn’t easy to deploy or host for multi-tenanted workloads, making it difficult to build applications on top of the dbt MCP server.

This is why we are excited to announce a new way to integrate with dbt MCP: the remote dbt MCP server. The remote dbt MCP server doesn’t require installing dependencies or running the dbt MCP server in your infrastructure, making it easier than ever to build and run agents. It is available today in public beta for users with dbt Starter, Enterprise, or Enterprise+ plans, ready for you to start building AI-powered applications.

Introduction to dbt and BigFrames​

dbt: A framework for transforming data in modern data warehouses using modular SQL or Python. dbt enables data teams to develop analytics code collaboratively and efficiently by applying software engineering best practices such as version control, modularity, portability, CI/CD, testing, and documentation. For more information, refer to What is dbt?

BigQuery DataFrames (BigFrames): An open-source Python library offered by Google. BigFrames scales Python data processing by transpiling common Python data science APIs (pandas and scikit-learn) to BigQuery SQL.

You can read more in the official BigFrames guide and view the public BigFrames GitHub repository.

By combining dbt with BigFrames via the dbt-bigquery adapter (referred to as "dbt-BigFrames"), you gain:

• dbt’s modular SQL and Python modeling, dependency management with dbt.ref(), environment configurations, and data testing. With the cloud-based dbt platform, you also get job scheduling and monitoring.
• BigFrames’ ability to execute complex Python transformations (including machine learning) directly in BigQuery.

dbt-BigFrames utilizes the Colab Enterprise notebook executor service in a GCP project to run Python models. These notebooks execute BigFrames code, which is translated into BigQuery SQL.

Hello, community!

My name is Bruno, and you might have seen me posting dbt content on LinkedIn. If you haven't, let me introduce myself. I started working with dbt more than 3 years ago. At that time, I was very new to the tool, and to understand it a bit better, I started creating some resources to help me with dbt learning. One of them, a dbt cheatsheet, was the starting point for my community journey.

I went from this cheatsheet to creating all different kinds of content, contributing and engaging with the community, until I got the dbt community award two times, and I am very thankful and proud about that.

Since the acquisition of SDF Labs by dbt Labs, I have been waiting for the day that we would see what the result of the fusion of these two companies would be. Spoiler alert: It’s the dbt Fusion engine and it's better than I could have expected.

Today, we announced the dbt Fusion engine.

Fusion isn't just one thing — it's a set of interconnected components working together to power the next generation of analytics engineering.

This post maps out each piece of the Fusion architecture, explains how they fit together, and clarifies what's available to you whether you're compiling from source, using our pre-built binaries, or developing within a dbt Fusion powered product experience.

From the Rust engine to the VS Code extension, through to new Arrow-based adapters and Apache-licensed foundational technologies, we'll break down exactly what each component does, how each component is licensed (for why, see Tristan's accompanying post), and how you can start using it and get involved today.

Today, we announced that the dbt Fusion engine is available in beta.

• If Fusion works with your project today, great! You're in for a treat 😄
• If it's your first day using dbt, welcome! You should start on Fusion — you're in for a treat too.

Today is Launch Day — the first day of a new era: the Age of Fusion. We expect many teams with existing projects will encounter at least one issue that will prevent them from adopting the dbt Fusion engine in production environments. That's ok!

We're moving quickly to unblock more teams, and we are committing that by the time Fusion reaches General Availability:

• We will support Snowflake, Databricks, BigQuery, Redshift — and likely also Athena, Postgres, Spark, and Trino — with the new Fusion Adapter pattern.
• We will have coverage for (basically) all dbt Core functionality. Some things are impractical to replicate outside of Python, or so seldom-used that we'll be more reactive than proactive. On the other hand, many existing dbt Core behaviours will be improved by the unique capabilities of the dbt Fusion engine, such as speed and SQL comprehension. You'll see us talk about this in relevant GitHub issues, many of which we've linked below.
• The source-available dbt-fusion repository will contain more total functionality than what is available in dbt Core today. (Read more about this here.)
• The developer experience will be even speedier and more intuitive.

These statements aren't true yet — but you can see where we're headed. That's what betas are for, that's the journey we're going on together, and that's why we want to have you all involved.

TL;DR: What You Need to Know​

• dbt’s familiar authoring layer remains unchanged, but the execution engine beneath it is completely new.
• The new engine is called the dbt Fusion engine — rewritten from the ground up in Rust based on technology from SDF. The dbt Fusion engine is substantially faster than dbt Core and has built in SQL comprehension technology to power the next generation of analytics engineering workflows.
• The dbt Fusion engine is currently in beta. You can try it today if you use Snowflake — with additional adapters coming starting in early June. Review our path to general availability (GA) and try the quickstart.
• You do not need to be a dbt Labs customer to use Fusion - dbt Core users can adopt the dbt Fusion engine today for free in your local environment.
• You can use Fusion with the new dbt VS Code extension, directly with the CLI, or with dbt Studio.
• This is the beginning of a new era for analytics engineering. For a glimpse into what the Fusion engine is going to enable over the next 1 to 2 years, read this post.

The AI revolution is here—but are we ready?
Across the world, the excitement around AI is undeniable. Discussions on large language models, agentic workflows, and how AI is set to transform every industry abound, yet real-world use cases of AI in production remain few and far between.

A common issue blocking people from moving AI use cases to production is an ability to evaluate the validity of AI responses in a systematic and well governed way. Moving AI workflows from prototype to production requires rigorous evaluation, and most organizations do not have a framework to ensure AI outputs remain high-quality, trustworthy, and actionable.

Introduction​

Building scalable data pipelines in a fast-growing fintech can feel like fixing a bike while riding it. You must keep insights flowing even as data volumes explode. At Kuda (a Nigerian neo-bank), we faced this problem as our user base surged. Traditional batch ETL (rebuilding entire tables each run) started to buckle; pipelines took hours, and costs ballooned. We needed to keep data fresh without reprocessing everything. Our solution was to leverage dbt’s incremental models, which process only new or changed records. This dramatically cut run times and curbed our BigQuery costs, letting us scale efficiently.

dbt is the standard for creating governed, trustworthy datasets on top of your structured data. MCP is showing increasing promise as the standard for providing context to LLMs to allow them to function at a high level in real world, operational scenarios.

Today, we are open sourcing an experimental version of the dbt MCP server. We expect that over the coming years, structured data is going to become heavily integrated into AI workflows and that dbt will play a key role in building and provisioning this data.

As a dbt Cloud admin, you’ve just upgraded to dbt Cloud on the Enterprise plan - congrats! dbt Cloud has a lot to offer such as CI/CD, Orchestration, dbt Explorer, dbt Semantic Layer, dbt Mesh, Visual Editor, dbt Copilot, and so much more. But where should you begin?

We strongly recommend as you start adopting dbt Cloud functionality to make it a priority to set up Single-Sign On (SSO) and Role-Based Access Control (RBAC). This foundational step enables your organization to keep your data pipelines secure, onboard users into dbt Cloud with ease, and optimize cost savings for the long term.

Hi! We’re Christine and Carol, Resident Architects at dbt Labs. Our day-to-day work is all about helping teams reach their technical and business-driven goals. Collaborating with a broad spectrum of customers ranging from scrappy startups to massive enterprises, we’ve gained valuable experience guiding teams to implement architecture which addresses their major pain points.

The information we’re about to share isn't just from our experiences - we frequently collaborate with other experts like Taylor Dunlap and Steve Dowling who have greatly contributed to the amalgamation of this guidance. Their work lies in being the critical bridge for teams between implementation and business outcomes, ultimately leading teams to align on a comprehensive technical vision through identification of problems and solutions.

Why are we here?
We help teams with dbt architecture, which encompasses the tools, processes and configurations used to start developing and deploying with dbt. There’s a lot of decision making that happens behind the scenes to standardize on these pieces - much of which is informed by understanding what we want the development workflow to look like. The focus on having the perfect workflow often gets teams stuck in heaps of planning and endless conversations, which slows down or even stops momentum on development. If you feel this, we’re hoping our guidance will give you a great sense of comfort in taking steps to unblock development - even when you don’t have everything figured out yet!