You can still utilize Java within the Databricks environment through several approaches:

1. Databricks SDK for Java:

• The official Databricks SDK for Java allows you to interact with Databricks programmatically. Using Java code, you can manage clusters, jobs, workspaces, and more.
• This is useful for automation, integrating existing Java-based systems, and building custom applications around Databricks.
• Documentation and examples can be found here:
  • https://docs.databricks.com/en/dev-tools/sdk-java.html
  • https://learn.microsoft.com/en-us/azure/databricks/dev-tools/sdk-java
  • https://github.com/databricks/databricks-sdk-java

2. Running Java Code on Databricks Clusters:

• You can package your Java code (Spark applications, utility classes, etc.) into JAR files and then deploy them onto your Databricks clusters.
• You can then submit Spark jobs referencing those JARs using the Databricks CLI, SDKs, or REST API.
• This is suitable for running existing Java-based Spark applications or libraries on Databricks.

3. Databricks Connect:

• Databricks Connect lets you connect your local IDE (like Eclipse or IntelliJ) to a Databricks cluster.
• This enables you to develop and run Java (or other languages) code locally while leveraging the resources of the Databricks cluster.
• It’s a convenient way to work with Java in a familiar environment while still benefiting from Databricks’ scalability.

Resources:

• How to run Java code on Azure Databricks with Example: https://azurelib.com/how-to-run-java-code-on-azure-databricks/