Data build tool (dbt) is an open-source command line tool that helps analysts and engineers transform data in their warehouse more effectively. It enables analytics engineers to write SQL select statements that are compiled into tables and views, focusing on the transformation step in extract, load, transform (ELT) processes. dbt does not extract or load data; it is designed to be performant at transforming data already inside a warehouse, allowing analysts to work more like software engineers.
The tool originated at RJMetrics in 2016 as a solution to add basic transformation capabilities to Stitch, which was acquired by Talend in 2018. From its earliest versions, dbt allowed analysts to contribute to data transformation following software engineering best practices, such as version control, testing, and documentation. dbt has been open source from the beginning, and in 2018, the dbt Labs team, then called Fishtown Analytics, released a commercial product on top of dbt Core.
History and Development
In 2016, RJMetrics, a Philadelphia-based analytics company, developed dbt to address the need for a transformation layer in its Stitch data pipeline. The initial versions focused on enabling analysts to write SQL-based transformations that could be versioned and tested. After Stitch was acquired by Talend in 2018, the dbt team spun off as Fishtown Analytics, later renamed dbt Labs. In 2018, they launched dbt Cloud, a commercial platform that provides a web-based IDE, scheduling, and monitoring on top of the open-source dbt Core.
The project gained significant traction in the analytics engineering community, leading to a series of funding rounds. In April 2020, dbt Labs announced its Series A led by Andreessen Horowitz. In November of the same year, it announced a Series B led by Andreessen Horowitz and Sequoia. In June 2021, the company raised a Series C led by Altimeter, Sequoia, and Andreessen Horowitz. In February 2022, dbt Labs raised $222 million in a Series D at a $4.2 billion valuation.
In January 2025, dbt Labs acquired SDF Labs, a Seattle-based startup founded in 2022 by Lukas Schulte, Wolfram Schulte, Elias DeFaria, and Michael Levin. SDF Labs developed SQL compilation and static analysis technology for analytics engineering, which was integrated into dbt's tooling to improve performance and reliability.
Funding and Corporate Evolution
The funding trajectory of dbt Labs reflects the growing demand for analytics engineering tools. The Series A in 2020, led by Andreessen Horowitz, marked the beginning of significant venture capital interest. The Series B later that year added Sequoia as a co-lead, and the Series C in 2021 included Altimeter, Sequoia, and Andreessen Horowitz. The Series D in February 2022, at a $4.2 billion valuation, provided substantial capital for product development and market expansion.
In October 2025, dbt Labs announced a definitive agreement to merge with Fivetran, a data integration company, in an all-stock deal. Under the terms, Fivetran CEO George Fraser would lead the combined company, while dbt Labs founder Tristan Handy would serve as president. The merger was pending regulatory approval as of 2026, with the combined entity projected to approach $600 million in annual recurring revenue. This merger aimed to create a comprehensive data stack covering both data movement and transformation.
Core Features and Workflow
Dbt operates by allowing users to define data transformations as SQL select statements, which are then compiled into tables or views in the warehouse. The tool supports modular development, where transformations can be broken into reusable models, and dependencies between models are managed automatically. This approach enables analytics engineers to build complex pipelines with clear lineage and documentation.
Dbt uses YAML files to declare properties such as model configurations, tests, and documentation. A notable feature is the seed type, which is used for static or infrequently changed data, such as country codes or lookup tables. Seeds are CSV-based and typically stored in a seeds folder, allowing them to be versioned and loaded into the warehouse as tables.
The tool integrates with major data warehouses, including Amazon Redshift, Google BigQuery, Azure Synapse, and others, making it a versatile choice for organizations with diverse data infrastructure. Dbt also supports testing and documentation generation, promoting data quality and collaboration across teams.
Impact on Analytics Engineering
Dbt has played a pivotal role in establishing the field of analytics engineering, bridging the gap between data engineering and business intelligence. By applying software engineering practices like version control, continuous integration, and code review to data transformations, dbt has helped organizations move away from ad-hoc SQL scripts toward maintainable, scalable data pipelines.
The tool's open-source nature has fostered a large community of practitioners who contribute plugins, packages, and best practices. This ecosystem has expanded dbt's capabilities, including integrations with orchestration tools, data quality platforms, and Machine learning workflows. As of 2026, dbt remains a leading solution for data transformation, with ongoing developments in areas such as SQL compilation and static analysis following the SDF Labs acquisition.