Adapters are an essential component of dbt. At their most basic level, they are how dbt connects with the various supported data platforms. At a higher-level, adapters strive to give analytics engineers more transferrable skills as well as standardize how analytics projects are structured. Gone are the days where you have to learn a new language or flavor of SQL when you move to a new job that has a different data platform. That is the power of adapters in dbt — for more detail, read the What are adapters guide.
This section provides more details on different ways you can connect dbt to an adapter, and explains what a maintainer is.
Set up in dbt Cloud
Explore the fastest and most reliable way to deploy dbt using dbt Cloud, a hosted architecture that runs dbt Core across your organization. dbt Cloud lets you seamlessly connect with a variety of verified data platform providers directly in the dbt Cloud UI.
Install using the CLI
Install dbt Core, which is an open-source tool, locally using the CLI. dbt communicates with a number of different data platforms by using a dedicated adapter plugin for each. When you install dbt Core, you'll also need to install the specific adapter for your database, connect to dbt Core, and set up a
With a few exceptions 1, you can install all Verified adapters from PyPI using
pip install adapter-name. For example to install Snowflake, use the command
pip install dbt-snowflake. 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.
Here are the two different adapters. Use the PyPI package name when installing with
Adapter repo name PyPI package name