Fivetran + dbt Labs makes dbt v2 and dbt State generally available, alongside the debut of Fivetran Context Layer, dbt Charts and a new open lakehouse vision for greater flexibility across storage and compute
LAS VEGAS--(BUSINESS WIRE)--Fivetran + dbt Labs today announced the general availability of dbt v2 and dbt State, delivering new levels of speed and cost optimization, and introduced Fivetran Context Layer, new dbt Wizard experiences, dbt Charts and an open lakehouse vision for greater flexibility across storage and compute.
As enterprises deploy AI agents into production, they need trusted data, context the business controls, and the flexibility to work across the platforms, models and tools already in use. To meet these demands, Fivetran + dbt Labs is advancing its vision for Open Data Infrastructure: a vendor-neutral, interoperable architecture that lets organizations independently choose and evolve their storage, compute, data movement, transformation and visualization technologies at every layer. This enables AI systems to work across platforms using data and context the enterprise owns, not a single vendor.
"Every model our customers have built, every test they've written, every metric they've defined already captures the context AI agents need to do meaningful work," said Anjan Kundavaram, Chief Product Officer, Fivetran + dbt Labs. "What we're delivering now is the open infrastructure to put that context to work across systems, while giving organizations the freedom to choose how their data is stored, moved, transformed and used as AI evolves."
A faster, more efficient engine with deeper SQL understanding
dbt v2 is a full Rust rewrite of the dbt engine built for the scale that teams run at today and for how agents write SQL. It parses a 10,000-model project up to 10x faster than v1 and gives teams and their agents accurate real-time feedback, surfacing errors, column checks, and lineage before anything runs. With this release, the two engine era of Core and Fusion ends. Now, dbt is one engine with two versions: dbt Core v1, the python implementation, is dbt v1. Fusion, the Rust implementation, has become dbt v2. Both versions remain Apache 2.0-licensed and security-supported.
dbt State determines what has changed by checking warehouse metadata and model SQL, then builds, skips, clones or defers each run accordingly. This simplifies orchestration and allows engineers to iterate faster without complex development rituals, while reducing unnecessary warehouse compute.
"dbt State has been a paradigm shift for how we work,” said Gordon Curzon, Head of Analytics Engineering, Virgin Media O2. “With freshness codified, simpler orchestration, and freed-up developer capacity, we focus more time on initiatives that add value to our business on top of the 25% savings on both job run time and BigQuery compute costs."
More flexibility across storage and compute
Open Data Infrastructure centers on a customer-owned data layer built on open formats, avoiding vendor lock-in for storage and compute so organizations can store data once and access it through different engines for different use cases.
Fivetran's Managed Data Lake Service, already generally available, organizes, structures and maintains data as managed Apache Iceberg™ tables in customers' own cloud storage. Lake Compute, now in Private Beta, is a single-node SQL engine built on DuckDB and runs dbt models directly against Apache Iceberg™ tables, built and priced specifically for transformation, not general-purpose compute. Together, the two give teams the flexibility to run each workload on whichever engine fits best, optimizing cost without re-platforming.
An open standard for agent context
Agents are only as trustworthy as the context they can access. Fivetran Context Layer (Private Beta) unifies the data and metadata needed to give LLMs and AI agents relevant context, building on dbt’s structured context and adding unstructured knowledge, like docs and Slack threads. This service uses Agents Schema, an open source standard, that centralizes context in a structured, extensible format directly in the data warehouse. The context is accessible to teams via preferred MCP or AI tools, including generally available integrations through AI marketplaces including Anthropic and a plugin in ChatGPT.
One agent, grounded in your dbt project – wherever you work
Coding agents can now write SQL as well as most engineers, but writing code isn't the same as understanding a governed dbt project, including its lineage, its tests, its contracts, and what breaks when something changes. dbt Wizard in the dbt platform (Public Preview) is built to close that gap. It's natively connected to your project, knows which tool to call, pulls the right context automatically, and proactively validates changes before they ship.
Wizard is also expanding beyond the dbt platform with Wizard CLI (Public Beta), bringing the project-grounded agent directly into the terminal, and Wizard Desktop (Private Beta), a dedicated local workspace for longer, more complex work.
Wizard Explore Mode (Public Preview) brings conversational analytics to business users, enabling them to ask questions in plain language and get answers grounded in the same dbt project the data team maintains. When an answer falls short, those questions can also surface what the data team should improve next.
A shared language for BI, built for humans and agents
dbt Charts (Public Beta) brings governed BI alongside the models it depends on. Instead of governance living in a separate, closed tool, they are defined as YAML and version-controlled alongside the dbt models they reference, creating a shared, declarative format that both humans and AI agents can read, write and review.
Customers building with Fivetran + dbt Labs
“Since rolling out dbt State, we’ve reduced warehouse costs by 59% on scheduled jobs in dbt platform,” said Chris Shepherd, Principal Data Engineer, RxBenefits. “That’s $8,173.23 in the first 60 days alone on top of a Snowflake adaptive warehouse. We’ve reused 716k models instead of rebuilding, which reduced query run time a total of 14 days, 11 hours, and 15 minutes over the same period.”
“We've been impressed by the flexibility Lake Compute gives us. Now we can choose where each dbt workload runs, and use whichever engine actually fits the job,” said Tyson Doberneck, Senior Data Engineer, Obie.
"dbt Wizard is changing how we work. Instead of hand-coding everything, we draft logic with an agent that already has full context on our jobs and our codebase. No more finding and uploading a manifest file just to explain myself, that step used to slow down every request. Now we're pointing it at sales and marketing data too, so analysts get answers themselves instead of waiting on my team," said Farin Fukunaga, Data Engineering Lead, Paylocity.
To learn more about the product innovations unveiled at dbt Summit 2026, read the recap or register to watch the full keynote: https://www.getdbt.com/dbt-summit/registration/online.
About Fivetran + dbt Labs
Fivetran + dbt Labs deliver the data infrastructure layer that makes agents trustworthy – from the moment data moves, through every transformation, to the context an agent reasons from.
The Fivetran platform moves, manages, and transforms data from every system a business runs on into a secure, reliable foundation engineered to evolve, with the flexibility to work across clouds, engines, and tools. With Fivetran, analytics, operations, and AI run on data you trust and control. Thousands of organizations worldwide, including OpenAI, LVMH, Pfizer, and Verizon, rely on Fivetran to turn data into a competitive advantage. Learn more at Fivetran.com, or follow Fivetran on LinkedIn.
Since 2016, dbt Labs has been on a mission to help data practitioners create and disseminate organizational knowledge. dbt is the standard for AI-ready structured data. Globally, more than 100,000 data teams use dbt, including those at Siemens, Roche and Condé Nast. Learn more at getdbt.com, and follow dbt Labs on LinkedIn, X, Instagram, and YouTube.
Contacts
Media Contact
Elaine Green
586-879-8774
elaine.green@dbtlabs.com
