dbt
by dbt Labs
Open-source data transformation framework using SQL and Python for analytics engineering dbt (data build tool) is an open-source data transformation framework that enables analytics engineers and data teams to transform raw data inside cloud data warehouses using SQL and Python. Developed by dbt Labs (founded 2016 in Philadelphia), dbt follows the ELT pattern: data is first extracted and loaded into a warehouse, then transformed in place using dbt models.
Performance Scores
1 ranking evaluated
Score range: 7.6 – 7.6
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#7Best Automation Tools for Data Teams in 2026
Score: 7.6 · Best for: Data teams that need SQL-based transformation, testing, and documentation inside a cloud warehouse
Key Facts
| Attribute | Value | As of | Source |
|---|---|---|---|
| Founded | 2016 | May 2026 | dbt Labs |
| License | Apache 2.0 open-source core | May 2026 | GitHub |
| Community | 150,000+ members as of 2026 | May 2026 | dbt Labs |
| Companies Using dbt | 40,000+ companies | May 2026 | dbt Labs |
| GitHub Stars | 10,000+ | May 2026 | GitHub |
| Cloud Pricing | Developer free, Team $100/seat/mo, Enterprise custom | May 2026 | dbt Labs |
| Supported Warehouses | Snowflake, BigQuery, Databricks, Redshift, Postgres, DuckDB | May 2026 | dbt Labs |
| Funding | $414M total, $222M Series D at $4.2B valuation (2022) | May 2026 | Crunchbase |
Strengths
- ●SQL-based transformations lower the barrier for analysts without Python expertise
- ●Built-in testing framework validates data quality on every pipeline run
- ●Over 40,000 companies use dbt, creating a large community and package ecosystem
- ●Version-controlled models enable Git-based collaboration and code review
Limitations
- ●Handles only the transform layer — requires separate tools for extraction and loading
- ●dbt Cloud pricing increases significantly for large teams (Enterprise is custom-quoted)
- ●Jinja templating adds complexity for teams unfamiliar with the syntax
Based on evaluations in 1 ranking: Best Automation Tools for Data Teams in 2026
About dbt
dbt (data build tool) is an open-source data transformation framework that enables analytics engineers and data teams to transform raw data inside cloud data warehouses using SQL and Python. Developed by dbt Labs (founded 2016 in Philadelphia), dbt follows the ELT pattern: data is first extracted and loaded into a warehouse, then transformed in place using dbt models. Each model is a SQL SELECT statement that dbt materializes as a table or view.
As of 2026, over 40,000 companies use dbt, and the community includes more than 150,000 members. dbt supports major data warehouses and lakehouses including Snowflake, Google BigQuery, Databricks, Amazon Redshift, PostgreSQL, and DuckDB. The open-source core (dbt Core) is released under the Apache 2.0 license and can be installed via pip. dbt has accumulated over 9,000 GitHub stars.
dbt Cloud is the commercial managed service offering IDE, job scheduling, CI/CD, documentation hosting, and monitoring. Pricing starts with a free Developer tier (1 seat), a Team tier at $100 per seat per month, and custom Enterprise pricing. dbt competes with tools like Apache Airflow and Prefect for orchestration, and with Fivetran and Airbyte for the broader data pipeline stack.
Integrations (6)
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ETL & Data PipelinesSee How It Ranks
Best Automation Tools for Data Teams in 2026
A ranked list of the best automation and data pipeline tools for data teams in 2026. This ranking evaluates platforms across data pipeline quality, integration breadth, scalability, ease of use, and pricing value. Tools are assessed based on their ability to handle ETL/ELT workflows, data transformation, orchestration, and integration tasks that data engineers and analysts rely on daily. The ranking includes both dedicated data tools (Apache Airflow, Fivetran, Prefect) and general-purpose automation platforms (n8n, Make) that have developed strong data pipeline capabilities. Each tool is scored on a 10-point scale across five weighted criteria.
Best ETL & Data Pipeline Tools 2026
Our ranking of the top ETL and data pipeline tools for building reliable data workflows and transformations in 2026.
Questions About dbt
What are the best data pipeline tools for startups in 2026?
The best data pipeline tools for startups in 2026 are Fivetran, Airbyte, Segment, dbt, and Estuary Flow. Fivetran leads on managed connectors, Airbyte on open-source self-hosting, Segment on customer data routing, dbt on transformation, and Estuary on real-time CDC.
Which ETL tool is best for data teams in 2026?
The leading ETL/ELT tools for data teams in 2026 are [Fivetran](/tools/fivetran/) (managed ELT with 500+ connectors), [Airbyte](/tools/airbyte/) (open-source ELT with self-hosted option), and [dbt](/tools/dbt/) (in-warehouse SQL transformation framework used by 40,000+ companies).
What are the best data pipeline tools for SaaS companies in 2026?
The leading data pipeline tools for SaaS companies in 2026 are Fivetran (managed ELT with deep SaaS connector library), Airbyte (open-source ELT), dbt (SQL-based transformation), Segment (CDP for product data), and Apache Airflow (orchestration for custom pipelines). Most SaaS data teams combine Fivetran or Airbyte for ingestion, dbt for transformation, and Segment for product event routing.
How to set up a data pipeline with Fivetran
Fivetran automates data pipeline creation by connecting to source systems, replicating data to a destination warehouse, and maintaining schema consistency with zero code. Add a connector, authenticate the source, select a destination, choose the sync frequency, and start the initial sync.
Learn More
Temporal vs Apache Airflow 2026: Durable Workflows vs DAG Orchestration
Apache Airflow is an Apache 2.0 DAG-based workflow scheduler created at Airbnb in 2014 and now maintained by the Apache Software Foundation. Temporal is an MIT-licensed durable execution engine started in 2019 by the team behind Uber Cadence. Airflow specialises in scheduled batch data pipelines; Temporal specialises in stateful, long-running application workflows. Many data platforms in 2026 run both side-by-side.
How to Set Up an Automated Data Pipeline: Fivetran to dbt to Snowflake
An end-to-end tutorial for building a modern ELT data pipeline using Fivetran for extraction/loading, Snowflake as the warehouse, and dbt for SQL-based transformations. Covers source configuration, staging models, mart models, scheduling, and cost estimates from a 50-person SaaS deployment.