Brand Intelligence Graph
Company Overview
About dbt Labs
dbt Labs is a data transformation and analytics engineering company founded in 2016 and headquartered in Philadelphia, Pennsylvania, that created dbt (data build tool) — the open-source framework that established analytics engineering as a discipline and became the de facto standard for transforming raw data in the modern data warehouse. The company was founded by Tristan Handy, Drew Banin, and Connor McArthur with the conviction that data analysts should have the same software engineering workflows — version control, testing, documentation, modularity — that application engineers take for granted. dbt brought those practices to SQL-based data transformation, enabling data teams to build reliable, maintainable data pipelines.
Business Model & Competitive Advantage
The dbt product ecosystem includes dbt Core (the open-source transformation framework), dbt Cloud (the managed development and deployment platform), dbt Explorer (data lineage and documentation), and a growing set of features for data governance and collaboration. In January 2025, dbt Labs acquired SDF Labs, a high-performance SQL compilation and semantic layer technology, deepening its capabilities in query planning and column-level lineage. dbt integrates natively with major cloud data warehouses including Snowflake, Databricks, BigQuery, and Redshift, and sits at the center of the modern data stack alongside ingestion tools like Fivetran and orchestration platforms like Airflow.
Competitive Landscape 2025–2026
In October 2025, dbt Labs announced an all-stock merger with Fivetran, a combination that would unite the leading data ingestion and transformation layers of the modern data stack under one company. dbt Core's open-source community spans hundreds of thousands of data practitioners globally, and dbt Cloud serves thousands of paying enterprise customers. The merger, if completed, would create a dominant end-to-end data pipeline company and redefine the competitive landscape in the modern data stack market.
Recent Activity
View all →Material Event filed 2026-09-30
## v2.5.0 - 2026-09-28 ### Enhancement or New Feature * Classify warehouse authentication and permission errors from semantic layer query failures, stripping the internal classification marker and appending an actionable hint pointing the caller toward the likely fix ### Bug Fix * Fix semantic layer query error formatting to use QueryFailedError's clean underlying message instead of its str() representation, which was leaking a message="...", status= wrapper artifact into the error shown to callers * Bound get_lineage results with a configurable node limit and report the number of omitted nodes. * Allow multi-project OAuth contexts to reuse cached tokens by accepting selected_project_ids in place of prod_environment in context validation ### Security * Update UI build dependencies to patched releases. * Update example dependencies to patched releases. * Upgrade MCP, FastAPI, and Starlette to patched releases. * Upgrade the UI build to Vite 8 and remove vulnerable esbuild from the lockf
## What's Changed * fix: cast numeric literals in graph inserts to the destination column type by @TonyStack2026 in https://github.com/dbt-labs/dbt-project-evaluator/pull/602 * fix: give each base graph model a unique CTE name by @b-per in https://github.com/dbt-labs/dbt-project-evaluator/pull/604 * ci: run Databricks integration tests in a dedicated catalog by @b-per in https://github.com/dbt-labs/dbt-project-evaluator/pull/605 * fix: support dbt-sqlserver >= 1.10 (adapter type sqlserver) by @b-per in https://github.com/dbt-labs/dbt-project-evaluator/pull/603 ## New Contributors * @TonyStack2026 made their first contribution in https://github.com/dbt-labs/dbt-project-evaluator/pull/602 **Full Changelog**: https://github.com/dbt-labs/dbt-project-evaluator/compare/v1.3.5...v1.4.0
Analytics teams have always modeled data for BI. Context engineering models data for agents, starting with semantic search.
# [v1.12.9](https://github.com/dbt-labs/terraform-provider-dbtcloud/compare/v1.12.8...v1.12.9) ### Changes * add scopes to databricks nested schema in global_connection resource * add the dbtcloud_account_add_on and dbtcloud_account_add_ons data sources, to read the state of the dbt Wizard and dbt State add-ons ### Fixes * stop the inconsistent result error when cost_optimization_features is added to or removed from an existing job
## v2.4.0 - 2026-09-22 ### Enhancement or New Feature * Add an optional project_id filter to list_jobs to retrieve jobs across all environments in a project, while preserving the configured environment default when omitted.
## 2.0.5 Released September 18, 2026 ### Fixes - Both Legacy/Foundry BigQuery drivers now continue on ADBC.GetObjects 404/403. This fixes a possible usage of "REPLACE TABLE" instead of "MERGE" for incremental tables ([#16331](https://github.com/dbt-labs/dbt-core/issues/16331)) ### Contributors - [@serramatutu](https://github.com/serramatutu) ([#16331](https://github.com/dbt-labs/dbt-core/issues/16331))
# [v1.12.8](https://github.com/dbt-labs/terraform-provider-dbtcloud/compare/v1.12.7...v1.12.8) ### Fixes * Fix bigquery_credential Update() to actually send auth_type, workload_pool_provider_path, and service_account_impersonation_url changes to the API instead of only writing them into state * fix dataset and threads updates on bigquery credentials using the latest adapter * add api_endpoint to bigquery global connections, needed for private service connect * stop the perpetual diff on environments that set deployment_type to an empty string
dbt v2, dbt State are GA and Fivetran + dbt Labs debuts Fivetran Context Layer, dbt Charts and a new open lakehouse vision
Every product announced at dbt Summit, from dbt v2 and dbt State to dbt Wizard and dbt Charts, and why each one matters.
dbt State is now generally available everywhere you run dbt. See how it cuts compute costs and speeds up development.
## 2.0.4 Released September 16, 2026
Key Differentiators
Strong Challenger
dbt Labs is an established challenger with significant market presence and competitive offerings in Data & Analytics.
Top 10 Ranked
Ranked #8 in the Data & Analytics category, among the industry's best.
Frequently Asked Questions
Estimated Visibility Trend (Beta)
Simulated 8-week rolling score
Based on estimated brand signals. Historical tracking coming soon.
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