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.

Databricks recommends that you use dbx by Databricks Labs for local development.

Databricks also provides a code sample that you can explore to use an IDE with dbx.

Note

Databricks also supports a tool named Databricks Connect. However, Databricks plans no new feature development for Databricks Connect at this time. Also, Databricks Connect has several limitations.

Use a connector or driver

You can use connectors and drivers to connect your code to a Databricks cluster or a Databricks SQL warehouse. These connectors and drivers include:

• The Databricks SQL Connector for Python

• The Databricks SQL Driver for Go

• The Databricks SQL Driver for Node.js

• pyodbc

• The Databricks ODBC driver

• The Databricks JDBC driver

For additional information about connecting your code through JDBC or ODBC, see the JDBC and ODBC configuration guidance.

Use a notebook

To run Python, R, or Scala code in a notebook to work with file systems, libraries, and secrets from a Databricks cluster, see Databricks Utilities.

Call Databricks 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 (latest)	Data Science & Engineering workspace assets such as clusters, global init scripts, groups, pools, jobs, libraries, permissions, secrets, and tokens, by using the latest version of the Databricks REST API.
REST API 2.1	Data Science & Engineering workspace assets such as jobs, by using version 2.1 of the Databricks REST API.
REST API 2.0	Data Science & Engineering workspace assets such as clusters, global init scripts, groups, pools, jobs, libraries, permissions, secrets, and tokens, by using version 2.0 of the Databricks REST API.
REST API 1.2	Command executions and execution contexts by using version 1.2 of the Databricks REST API.

Provision infrastructure

You can use an infrastructure-as-code (IaC) approach to programmatically provision Databricks infrastructure and assets such as workspaces, clusters, jobs, groups, and users. For details, see:

• Databricks Terraform provider

• Pulumi Databricks resource provider

Use CI/CD

To manage the lifecycle of Databricks assets and data, you can use continuous integration and continuous delivery (CI/CD) and data pipeline tools.

Area	Use these tools when you want to…
Continuous integration and delivery on Databricks using GitHub Actions	Build a CI/CD workflow on GitHub that uses GitHub Actions developed for Databricks.
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.

Use a SQL database tool

You can use these tools to run SQL commands and scripts and to browse database objects in Databricks.

Tool	Use this when you want to:
Databricks SQL CLI	Use a command line to run SQL commands and scripts on a Databricks SQL warehouse.
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 other tools

You can connect many popular third-party tools to clusters to access data in Databricks. See the Databricks integrations.