Fivetran + dbt Labs announces new capabilities to make enterprise data agent-ready at dbt summit 2026. Last edited on Sep 16, 2026 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 - September 16, 2026 - 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(TM) 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(TM) 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. 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.

Celebrating the 2026 dbt partner of the year winners. Last edited on Sep 15, 2026 Its partners help data teams level up their dbt implementations: faster rollouts, cleaner data foundations, and AI initiatives that ship. At Partner Day during dbt Summit 2026, dbt Labs, Inc. recognized five partners who did that work at the highest level this year. Here's who won, and why. Partner of the year: phData. phData earned partner of the year for the fourth consecutive year for what its customers walk away with: dbt implementations and data foundations to build trusted agents on, and a certified bench ready to take on the hardest problems. As a Visionary-tier partner, phData is a team that shared customers trust with their most critical data programs. "Winning dbt Labs Partner of the Year for the fourth year in a row says more about the depth of this partnership than any single project could. dbt has become the backbone of how we help clients turn raw data into something the business can actually trust and reason with. We're proud of the work we've done together, and even more excited about where dbt is headed as it becomes central to how our clients build agentic and AI use cases on top of their data." - Dustin Dorsey, Senior Director, Data Engineering, phData Technology partner of the year: Snowflake. Snowflake earned technology partner of the year for the joint value that customers get out of the partnership. In every region, teams running dbt on Snowflake are standing up trusted, production-grade pipelines faster, with dbt Labs and Snowflake engineers working the same problems together rather than handing customers off to each other. It's one of the most widely adopted foundations in the ecosystem. "Snowflake and dbt Labs share a vision of empowering data teams to move faster with confidence, and this partnership continues to raise the bar for what our joint customers can achieve. Together, we've made it simpler than ever for organizations to build trusted, production-grade data pipelines on Snowflake. We're proud to be named dbt Labs Partner of the Year and excited to deepen our collaboration to help even more customers turn their data into real business impact." - Rodrigo Rocha, VP, Global ISV and Technology Partnerships, Snowflake EMEA partner of the year: Cívica. Cívica earned EMEA partner of the year for the quality of what customers get, not just the volume. More customers in the region do their dbt work with Cívica than with any other partner, and every engagement is backed by a certified delivery team. It's a Visionary-tier partner customers across EMEA rely on to get it right the first time. "Being recognized as dbt Labs Partner of the Year in EMEA is a tremendous honor and a true testament to the hard work and dedication of our entire team. What excites us most about collaborating with dbt is the strength of its community: a vibrant ecosystem where open collaboration, innovation, and shared learning help drive the entire industry forward. We're more motivated than ever to continue accelerating the future of data alongside this incredible network." - Josep Roig, CEO, Cívica APJ partner of the year: Datum Studio. Datum Studio earned APJ partner of the year for the depth of what it delivered for customers in Japan this year: more joint dbt work than any other partner in the region, as many customer wins as any partner globally, and one of the strongest certified delivery teams in APJ. In a market where technical depth is everything, Datum Studio was the team customers turned to this year. "dbt has become the de facto standard for analytics engineering, and our role is to make that standard take root in the Japanese market, not just as a tool, but as a way of working. What excites me most about the partnership is where it's heading: as AI agents start writing and reviewing transformations, the foundation dbt provides, lineage, testing, documentation, governance, and shared context, is what makes that work trustworthy at scale." - Sohei Takechi, President and CEO, Datum Studio Emerging partner of the year: 66degrees. 66degrees earned emerging partner of the year for how fast it got to real customer work. In under a year, 66degrees went from a standing start to delivering dbt projects for customers, bringing Google Cloud depth to shared accounts, and showing up alongside dbt Labs at field and virtual events. This is what a partnership looks like when a team decides to invest. "66degrees is honored by this recognition as dbt Labs' Emerging Partner of the Year, which celebrates our long-term Fivetran partnership and our years spent building dbt directly into our core accelerators, including Hub66 and Paradigm Data, our agentic data product delivery agents. Fivetran and dbt together define a transformative direction for data architecture, establishing the seamless pipeline required for modern analytics and AI. As both platforms innovate, we look forward to evolving our accelerators and driving the next era of intelligent data delivery right alongside them, giving our customers a best-in-class data platform." - Daniel Zagales, SVP, Data and Analytics, 66degrees What this means for the dbt community. These five partners represent five different ways of showing up for customers: depth of delivery, regional expertise, and the speed to build something new. Congratulations to phData, Snowflake, Cívica, Datum Studio, and 66degrees on the well-earned recognition.

How Hex thinks shared context will unlock enterprise AI ROI. As enterprises move beyond AI pilots, shared context and open infrastructure will determine whether intelligent agents deliver measurable business value at scale. Smruthi Nadig · AUGUST 21, 2026, 10:42 AM · Updated AUGUST 21, 2026, 12:32 PM Key Takeaways What actually matters. Organisations are shifting focus from AI model selection to demonstrating measurable business outcomes. A shared context and open infrastructure are crucial for scaling enterprise AI effectively. The enterprise AI conversation has changed. Over the past two years, organisations have invested heavily in large language models, copilots and generative AI applications, driven by the promise of greater productivity and faster decision-making. Today, however, boardrooms are asking a different question. Rather than debating which model to adopt, leaders want evidence that AI is delivering measurable business outcomes. That shift took centre stage at MachineCon USA on July 24, where Carlos Aguilar, Head of Product at Hex, joined leaders from Fivetran, dbt Labs and other enterprise data teams for a panel titled 'The AI-Native Mandate: Building the Foundation for Measurable AI ROI'. The discussion reflects a growing consensus across the industry: organisations can no longer think of AI as a standalone capability. They need a foundation that enables people and AI systems to reason, collaborate and make decisions from the same trusted understanding of the business. Hex believes the biggest obstacle to scaling enterprise AI is no longer the model itself, but the context surrounding it. As Aguilar puts it, "Models are commoditising fast. Context isn't. Every agent, workflow, and application pulling from a different, ungoverned understanding of the business doesn't produce intelligence; it produces inconsistency at scale. Shared context is becoming as fundamental to the AI stack as the database was to the application stack." The same shift is visible across the broader AI ecosystem. As Remy Thellier, Head of AI/ML Partners at Snowflake, notes, "C-suites aren't asking for another AI pilot. They want to see it working in production, with numbers behind it. Our joint customers get there by keeping AI analysis grounded in governed data on Snowflake, and now Snowflake's AI functions run right inside the Hex workflow. That's the difference between a demo and something that can actually be leveraged every day to drive significant value for the organisation." AI doesn't have a model problem. Many enterprises believe they are AI-ready because they have invested in cloud platforms, data warehouses and business intelligence tools. Yet, these architectures were built for analytics and reporting, not for autonomous AI agents capable of continuously reasoning across workflows. Different departments often calculate the same business metric differently. AI assistants generate conflicting recommendations because they interpret business logic independently. Employees spend more time validating AI-generated outputs than acting on them. According to Hex, these are not failures of AI models but of fragmented context. Hex argues that many organisations delay AI adoption while trying to perfect their data estate. Instead, those seeing measurable returns begin with a recurring business process, assemble enough trusted context to support that workflow, and allow their data foundation to mature as AI delivers value. Rather than treating data quality as a prerequisite, they improve it by solving real business problems. Shared context as AI's competitive advantage. For Hex, the defining challenge of enterprise AI is not connecting more data. It is ensuring every analyst, application and AI agent interprets data in exactly the same way. Business metrics such as revenue, customer lifetime value or churn often have different definitions across departments. Human analysts understand these nuances through experience, but AI agents cannot. Two systems can access the same dataset yet arrive at different conclusions if they rely on different business definitions. Aguilar believes the semantic layer is becoming a critical component of the AI stack. Rather than embedding business logic into individual dashboards or applications, organisations should create reusable semantics that every analyst and AI agent can build upon. Shared business definitions, governed metrics and common analytical logic become organisational assets rather than project-specific configurations. Their customer experiences reinforce this philosophy. At ClickUp, combining product usage, customer engagement and billing data enabled AI to identify high-value churn risks, helping customer success and marketing teams personalise interventions and save more than $1 million in revenue churn. At Huckberry, bringing together historical sales data, promotional calendars and finance expertise produced more accurate inventory forecasts and savings exceeding $1 million annually. In both cases, AI generated value because it had access to shared business context rather than isolated datasets. For the company, AI should complement analysts rather than replace them. Analysts contribute business judgement and institutional knowledge, while AI contributes speed, exploration and automation. Better decisions emerge only when both operate from the same trusted context. Open infrastructure is not just about moving data. As organisations prepare for an increasingly agentic future, open data infrastructure is becoming a strategic necessity. Open table formats such as Apache Iceberg, interoperable APIs and portable architectures allow enterprises to move data across technologies without repeatedly rebuilding pipelines. But Hex argues that openness cannot stop with storage. As organisations adopt multiple foundation models and specialised AI agents, the challenge is no longer moving data between systems, it is preserving business meaning wherever that data goes. An Open Data Infrastructure therefore extends beyond open formats to include an open semantic layer, ensuring business definitions, governance policies and analytical context remain portable across tools. Without context portability, data portability alone simply recreates fragmentation. Rather than waiting for every data quality issue to be resolved, Hex advocates starting with a single, measurable business process, building enough trusted context to make AI useful, and allowing the foundation to mature through production. Every successful workflow expands the organisation's context layer, making future AI initiatives easier to deploy. Building for Measurable AI ROI. Hex's CIO playbook encourages organisations to begin with business outcomes rather than platforms, identify where trusted context already exists, design workflows that business teams can own, measure success through operational outcomes rather than AI metrics, and allow the data foundation to evolve alongside real work. This philosophy is evident across Hex's customer stories. At EliseAI, AI reduced quarterly business review preparation from days to minutes while keeping analysts in control of the underlying reasoning. At Supabase, AI consolidated information spread across eight systems into a single workflow, enabling support engineers to resolve customer requests in seconds while maintaining human oversight before actions were executed. In both cases, AI succeeded because it became part of an existing business process rather than another standalone application. Boardrooms will increasingly judge AI investments by business outcomes rather than technical achievements. Organisations that start with real business processes, build just enough trusted context to solve them, and allow that foundation to mature through production will be best positioned to realise measurable AI ROI. Your reaction Discussion. No comments yet - be the first to share your view. Scoops & predictions tracker