Developer tools and guidance
Learn about tools and guidance you can use to work with Databricks assets and data and to develop Databricks applications.
Use an IDE
You can connect many popular third-party IDEs to a Databricks cluster. This allows you to write code on your local development machine by using the Spark APIs and then run that code as jobs remotely on a Databricks cluster.
These third-party IDEs include:
- Eclipse
- IntelliJ
- Jupyter
- PyCharm
- SBT
- sparklyr and RStudio Desktop
- SparkR and RStudio Desktop
- Visual Studio Code
To connect other IDEs through JDBC or ODBC, see the JDBC and ODBC configuration guidance.
If you code in Python, you can also use pyodbc.
Use the command line or a notebook
Databricks provides additional developer tools.
| Name | Use this tool when you want to… |
|---|---|
| Databricks CLI | Use the command line to work with Data Science & Engineering workspace assets such as cluster policies, clusters, file systems, groups, pools, jobs, libraries, runs, secrets, and tokens. |
| Databricks Utilities | Run Python, R, or Scala code in a notebook to work with credentials, file systems, libraries, and secrets from a Databricks cluster. |
Call REST APIs
You can use popular third-party utilities such as curl and tools such as Postman to work with Databricks resources directly through the Databricks REST APIs.
| Category | Use this API to work with… |
|---|---|
| REST API 2.0 | Data Science & Engineering workspace assets such as clusters, global init scripts, groups, pools, jobs, libraries, permissions, secrets, and tokens. |
| REST API 1.2 | Command executions and execution contexts. |
Provision infrastructure
You can use an infrastructure-as-code (IaC) approach to programmatically provision Databricks infrastructure and assets such as workspaces, clusters, cluster policies, pools, jobs, groups, permissions, secrets, tokens, and users. For details, see Databricks Terraform provider.
Follow patterns and practices
To manage the lifecycle of Databricks assets and data, you can use continuous integration and delivery (CI/CD), data pipeline, and data engineering tools.
| Area | Use these patterns and best practices when you want to… |
|---|---|
| Continuous integration and delivery on Databricks using Jenkins | Develop a CI/CD pipeline for Databricks that uses Jenkins. |
| Managing dependencies in data pipelines | Manage and schedule a data pipeline that uses Apache Airflow. |
| dbt Core integration with Databricks | Transform data in Databricks by simply writing select statements on your local development machine. dbt turns these select statements into tables and views. |
| dbt Cloud integration with Databricks | Transform data in Databricks by simply writing select statements in your web browser. dbt turns these select statements into tables and views. |
Use a SQL database tool
You can use these third-party tools to run SQL commands and scripts and to browse database objects in Databricks.
| IDE | Use this when you want to: |
|---|---|
| DataGrip integration with Databricks | Use a query console, schema navigation, smart code completion, and other features to run SQL commands and scripts and to browse database objects in Databricks. |
| DBeaver integration with Databricks | Run SQL commands and browse database objects in Databricks by using this client software application and database administration tool. |
| SQL Workbench/J | Run SQL scripts (either interactively or as a batch) in Databricks by using this SQL query tool. |
Use BI tools
You can connect many popular third-party business intelligence (BI) tools to clusters and SQL endpoints to access data in Databricks.
To connect other BI tools through JDBC or ODBC, see the JDBC and ODBC configuration instructions.