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Supported data platforms

dbt connects to and runs SQL against your database, warehouse, lake, or query engine. We group all of these SQL-speaking things into one bucket called data platforms. dbt can be extended to any data platform using a dedicated adapter plugin. These plugins are built as Python modules that dbt Core discovers if they are installed on your system. All the adapters listed below are open source and free to use, just like dbt Core.

To learn more about adapters, check out What Are Adapters.

Supported Data Platforms​

Verified Adapters​

Data Platform (click to view setup guide)latest verified version
AlloyDB(same as dbt-postgres)
Azure Synapse1.3.0 🚧
BigQuery1.2.0
Databricks1.2.0 🚧
Dremio1.3.0 🚧
Postgres1.2.0
Redshift1.2.0
Snowflake1.2.0
Spark1.2.0
Starburst & Trino1.2.0 🚧

🚧: Verification in progress

Community Adapters​

Data Platforms (click to view setup guide)
AthenaHiveSingleStore
ClickhouseImpalaSQLite
IBM DB2iometeSQL Server & Azure SQ
DuckDBLayerAzureSynapse
DremioMaterializeTeradata
Exasol AnalyticsMindsDBTiDB
FireboltMySQLVertica
AWS GlueOracle
GreenplumRockset

Adapter Installation​

With a few exceptions 1, all adapters listed below can be installed from PyPI using pip install <ADAPTER-NAME>. The installation will include dbt-core and any other required dependencies, which may include both other dependencies and even other adapter plugins. Read more about installing dbt.

Adapter Taxonomy​

Verified by dbt Labs​

In order to provide a more consistent and reliable experience, dbt Labs has a rigorous process by which we verify adapter plugins. The process covers aspects of development, documentation, user experience, and maintenance. These adapters earn a Verified designation so that users can have a certain level of trust and expectation when they use them. To learn more, see Verifying a new adapter.

We also welcome and encourage adapter plugins from the dbt community (see the below Contributing to a pre-existing adapter). Please be mindful that these community maintainers are intrepid volunteers who donate their time and effort β€” so be kind, understanding, and help out where you can!

Maintainers​

Who made and maintains an adapter is certainly relevant, but we recommend using an adapter's verification status to determine the quality and health of an adapter. So far there are three categories of maintainers:

Supported byMaintained By
dbt Labsdbt Labs maintains a set of adapter plugins for some of the most common databases, warehouses, and platforms. As for why particular data platforms were chosen, see "Why Verify an Adapter"
PartnerThese adapter plugins are built and maintained by the same people who build and maintain the complementary data technology.
CommunityThese adapter plugins are contributed and maintained by members of the community. 🌱

Contributing to dbt-core adapters​

Contributing to a pre-existing adapter​

Community-supported plugins are works in progress, and anyone is welcome to contribute by testing and writing code. If you're interested in contributing:

  • Join both the dedicated channel, #adapter-ecosystem, in dbt Slack and the channel for your adapter's data store (see Slack Channel column of above tables)
  • Check out the open issues in the plugin's source repository (follow relevant link in Adapter Repository column of above tables)

Creating a new adapter​

If you see something missing from the lists above, and you're interested in developing an integration, read more about adapters and how they're developed in the Adapter Development section.

If you have a new adapter, please add it to this list using a pull request! See Documenting your adapter for more information.


  1. Here are the two different adapters. Use the PyPI package name when installing with pip

    Adapter repo namePyPI package name
    dbt-athenadbt-athena-adapter
    dbt-layerdbt-layer-bigquery

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