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Brief
dbt Labs

dbt Labs

Data Infrastructuregetdbt.com

dbt Labs enables data teams to transform raw warehouse data into reliable, tested, and documented analytics-ready models using software engineering best practices — dramatically improving data quality and team productivity.

Last updated Jun 23, 2026 by ATDb automated enrichment

Founded
2016
HQ
Philadelphia, Pennsylvania, United States
Connections
29

At a glance

Employees
501-1000
Funding
$414M
Revenue
$50M-$150M
24integrations1corporate family1acquisitions

About

Market leader and de facto standard for SQL-based data transformation in the modern data stack

dbt Labs, formerly known as Fishtown Analytics, is the company behind dbt (data build tool), an open-source transformation framework that has become the de facto standard for analytics engineering. dbt enables data analysts and engineers to write modular SQL-based transformations, run automated tests, and generate documentation — all within a version-controlled, software-engineering-inspired workflow. The company was founded in 2016 by Tristan Handy and Drew Bandy and has grown into one of the most influential players in the modern data stack ecosystem. dbt Labs offers both the open-source dbt Core (free, CLI-based) and dbt Cloud, a managed SaaS platform that adds scheduling, a web IDE, CI/CD integrations, observability, and collaboration features. The company has cultivated a massive community of data practitioners — the dbt Community — which includes tens of thousands of active members and has helped establish 'analytics engineering' as a recognized profession. This community-led growth strategy has made dbt a central hub in the modern data stack alongside cloud warehouses like Snowflake, BigQuery, and Databricks. While dbt Labs is primarily a data infrastructure and analytics engineering company rather than a pure AdTech player, it is widely used by AdTech and marketing data teams to model advertising performance data, attribution pipelines, and audience segmentation logic. Its integrations with major cloud data warehouses and its role in enabling clean, reliable data pipelines make it a critical enabler for data-driven advertising operations. The company has raised over $414 million in venture funding and is considered a key pillar of the modern data stack.

Business model

Open-source + SaaS

Target market

Enterprise, Mid-Market

What they offer

  • dbt Core

    Free, open-source CLI framework for defining, running, testing, and documenting SQL-based data transformations in a version-controlled environment.

  • dbt Cloud

    Managed SaaS platform built on top of dbt Core, adding a web-based IDE, job scheduling, CI/CD pipelines, observability, team collaboration, and enterprise governance features.

  • dbt Semantic Layer

    A centralized layer for defining business metrics and KPIs consistently across all downstream tools, enabling governed metric access via integrations with BI and analytics platforms.

  • dbt Explorer

    A data catalog and lineage visualization tool within dbt Cloud that helps teams understand data assets, dependencies, and documentation across their entire project.

  • dbt Mesh

    A framework for enabling cross-project dependencies and federated data ownership across large organizations with multiple dbt projects.

Key features

SQL-first data transformation with Jinja templatingAutomated data testing and validationAuto-generated data documentation and lineage graphsVersion control and CI/CD integration (Git-based workflow)Modular, reusable data modelsCentralized semantic layer for metric definitionsMulti-warehouse support (Snowflake, BigQuery, Redshift, Databricks, etc.)Package ecosystem (dbt Hub) for reusable community packagesJob scheduling and orchestration in dbt CloudRole-based access control and enterprise governance

Use cases

Transforming raw advertising and marketing data into clean, analytics-ready modelsBuilding attribution and media mix modeling pipelinesAudience segmentation and customer data modelingStandardizing business metrics and KPIs across BI toolsData quality testing and validation for reporting pipelinesCreating data lineage documentation for compliance and governanceEnabling self-service analytics for business usersOperationalizing data from CRM, ad platforms, and event tracking systems

Customer segments

Data engineering and analytics engineering teamsAdTech and marketing analytics teamsE-commerce and retail analyticsFinancial services data teamsSaaS companies with product analytics needsEnterprise data platform teamsData consultancies and analytics agencies

Tech & specs

Technology stack

PythonSQL / Jinja templatingYAML (configuration and testing)Git / GitHub / GitLabSnowflakeGoogle BigQueryAmazon RedshiftDatabricksApache SparkREST APIGraphQL (Semantic Layer)

Security & compliance

SOC 2 Type IIGDPRCCPASSO / SAML 2.0Role-based access control (RBAC)IP allowlisting

Deployment

CloudOn-premise

API

Yes

Corporate history
  1. 2016 · Founded
How it came together
  • 2021·Rebrandeddbt Labs
  • 2023·Acquireddbt Labs
See the full lineage →
See integrations with dbt Labs (24) See acquisitions by dbt Labs (1)

Explore further

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