Databricks integrations overview
The articles listed here provide information about how to connect to the large assortment of data sources, BI tools, and developer tools that you can use with Databricks. Many of these are available through our system of partners and our Partner Connect hub.
Partner Connect is a user interface that allows validated solutions to integrate more quickly and easily with your Databricks clusters and SQL warehouses.
For more information, see What is Databricks Partner Connect?.
Databricks can read data from and write data to a variety of data formats such as CSV, Delta Lake, JSON, Parquet, XML, and other formats, as well as data storage providers such as Amazon S3, Google BigQuery and Cloud Storage, Snowflake, and other providers.
For a comprehensive list, with connection instructions, see Data ingestion and Interact with external data on Databricks.
Databricks has validated integrations with your favorite BI tools, including Power BI, Tableau, and others, allowing you to work with data through Databricks clusters and SQL warehouses, in many cases with low-code and no-code experiences.
For a comprehensive list, with connection instructions, see BI and visualization.
Other ETL tools
In addition to access to all kinds of data sources, Databricks provides integrations with ETL/ELT tools like dbt, Prophecy, and Azure Data Factory, as well as data pipeline orchestration tools like Airflow and SQL database tools like DataGrip, DBeaver, and SQL Workbench/J.
For connection instructions, see:
ETL tools: Data preparation and transformation
SQL database tools: Use a SQL database tool.
IDEs and other developer tools
Databricks supports developer tools such as DataGrip, IntelliJ, PyCharm, Visual Studio Code, and others, that allow you to work with data through Databricks clusters and SQL warehouses by writing code.
For a comprehensive list, with connection instructions, see Developer tools and guidance.
Databricks Repos provides repository-level integration with your favorite Git providers, so you can develop code in a Databricks notebook and sync it with a remote Git repository. See Git integration with Databricks Repos.