dbt Labs offers an open‑standard, SQL‑first framework for building modular, testable, and version‑controlled data transformation pipelines. It compiles SQL across major warehouses, provides local validation, automated testing, documentation, and column‑level lineage, and includes a state‑aware orchestration engine and semantic layer for reliable analytics and AI workloads.
Funding
$222M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
ACFounders
Product
Problem
Data teams struggle with fragmented, error‑prone pipelines that lack version control, testing, and clear lineage, leading to low data quality, high operational costs, and slow delivery of analytics and AI products.
Solution
dbt provides an open‑standard, SQL‑based transformation framework that lets analysts and engineers build modular, testable, and version‑controlled data models. It compiles SQL across multiple data warehouses, validates code locally, and generates rich metadata, documentation, and column‑level lineage. The dbt Catalog visualizes this metadata, enabling rapid discovery, reuse, and troubleshooting of data assets. The Fusion engine, written in Rust, adds state‑aware orchestration and incremental processing to reduce compute waste and accelerate development. Integrated with the dbt MCP server, the platform exposes a semantic layer that AI tools can query directly, ensuring consistent, governed data for machine‑learning pipelines.
Target Audience
Primary users are analytics engineers, data analysts, and data scientists in mid‑size to large enterprises who need reliable, governed data pipelines for reporting and AI initiatives.
Features
- SQL‑first development with native compilation for Snowflake, Databricks, Azure, and other warehouses
- Automated testing, documentation, and version control for every model
- Interactive DAG and column‑level lineage view via dbt Catalog/Explorer
- Fusion engine with state‑aware orchestration and incremental model execution to cut compute costs
- dbt MCP server providing a semantic layer for AI agents and copilots to query trusted data
- Seamless integrations with Fivetran, Tableau, OpenAI, and other ecosystem tools via open standards
- Community‑driven package ecosystem and open‑source adapters for extensibility