Getting Started with Unity Catalog: Enabling Unity Catalog for Azure Databricks (Chapter 1)

5 min readJun 1, 2024

Unity Catalog is a powerful tool that enables seamless integration between Unity and Azure Databricks, facilitating efficient data processing and analysis for Unity projects.

In this tutorial series, we will guide you through the process of enabling Unity Catalog for Azure Databricks, starting with Part 1. By the end of this tutorial, you will have a solid understanding of the initial setup required to leverage the capabilities of Unity Catalog within your Azure Databricks environment.

Prerequisites:

Basic understanding of Unity and Azure Databricks.
You’ll need access to an Azure account with permissions for creating and managing resources.
Unity Editor is installed on your development machine.

Step 1: Provisioning Azure Databricks

The first step in enabling Unity Catalog for Azure Databricks is to provision an Azure Databricks workspace. Follow these steps:

Sign in to the Azure Portal using your Azure account credentials.
In the Azure Portal dashboard, click on the “+ Create a resource” button located in the upper left corner.
Search for “Databricks” in the search bar and select “Azure Databricks” from the search results.
Click on the “Create” button to initiate the creation process.
Fill in the required information such as subscription, resource group, workspace name, and region.
Choose the pricing tier according to your requirements and click on the “Review + Create” button.
Review the configuration details, then click on the “Create” button to provision the Azure Databricks workspace.
Wait for the deployment to complete, which may take a few minutes. Once the deployment is successful, proceed to the next step.

Step 2: Setting up the Unity Catalog

Now that you have provisioned an Azure Databricks workspace, you need to set up Unity Catalog to integrate it with Azure Databricks. Follow these steps:

Open Unity Editor on your development machine.
Create a new Unity project or open an existing one where you want to enable Unity Catalog.
In Unity, navigate to “Window” > “Package Manager” to open the Package Manager window.
Search for “Unity Catalog” in the Package Manager search bar.
Click on the “Install” button next to Unity Catalog to install the package into your Unity project.

Unity Catalog will now be integrated into your Unity project, allowing you to leverage its features for data processing and analysis.

Step 3: Configuring Azure Databricks Integration

With Unity Catalog installed in your Unity project, you need to configure the integration with Azure Databricks. Follow these steps:

In Unity Editor, navigate to “Edit” > “Project Settings” to open the Project Settings window.
Select “Unity Catalog” from the list of categories on the left side of the Project Settings window.
In the Unity Catalog settings, locate the “Azure Databricks” section.
Enter the necessary information such as the Azure Databricks workspace URL, authentication token, and default cluster-ID.
Azure Databricks Workspace URL: The URL of your Azure Databricks workspace (e.g., https://<workspace-name>.<region>.azuredatabricks.net).
Authentication Token: The token used to authenticate requests to the Azure Databricks REST API.
Default Cluster ID: The ID of the default Databricks cluster to use for data processing.

Ensure that you have the required permissions to access the Azure Databricks workspace and perform operations such as creating and managing clusters.

Step 4: Testing the Integration

To verify that Unity Catalog is successfully integrated with Azure Databricks, you can perform a simple test:

In Unity Editor, create a new C# script or open an existing one.
Write a script to interact with Azure Databricks using Unity Catalog APIs. For example, you can query data from a table in an Azure Databricks Delta Lake.
Run the script in the Unity Editor and observe the output in the console window.
If the integration is successful, you should see the data retrieved from Azure Databricks displayed in the console.

Best Practices for Using Unity Catalog with Azure Databricks

To ensure optimal performance and efficiency when using Unity Catalog with Azure Databricks, consider the following best practices:

Data Partitioning: Utilize data partitioning techniques such as Delta Lake partitioning in Azure Databricks to optimize data storage and query performance. Partitioning data based on commonly used query predicates can significantly improve query execution times.
Cluster Sizing: Properly size your Azure Databricks clusters based on the workload requirements of your Unity project. Monitor cluster performance metrics such as CPU utilization, memory usage, and input/output (I/O) throughput to determine the appropriate cluster size for your workload.
Query Optimization: Optimize your data processing and analysis queries in Unity Catalog to minimize resource utilization and execution time. Use techniques such as query caching, predicate pushdown, and query parallelization to improve query performance.

Troubleshooting Unity Catalog Integration Issues

If you encounter any issues during the integration of Unity Catalog with Azure Databricks, follow these troubleshooting steps:

Verify Connection Settings: Double-check the Azure Databricks workspace URL, authentication token, and default cluster ID configured in the Unity Catalog settings. Ensure that the provided credentials are correct and have the necessary permissions to access the Azure Databricks workspace.
Check Network Connectivity: Ensure that your development machine has network connectivity to the Azure Databricks workspace. Check for any firewall or network restrictions that may be blocking outgoing requests from Unity to Azure Databricks.
Review Error Messages: If you encounter any error messages or exceptions when interacting with Azure Databricks through Unity Catalog, carefully review the error messages for clues about the underlying issue. Check the Azure Databricks documentation and Unity Catalog API documentation for guidance on resolving common integration issues.

Wrapping Up

Now that you have successfully enabled Unity Catalog for Azure Databricks and configured the integration within your project.

In the next chapter, “Configuring Storage, External Location, and Cluster Settings for Unity Catalog | chapter 2,” we’ll delve deeper into the configuration process required to optimize Unity Catalog for Azure Databricks.

This includes setting up storage, configuring external locations, and fine-tuning the Databricks cluster for efficient data processing and analysis within Unity projects. By following the steps outlined in Chapter 2, you’ll further enhance the capabilities of Unity Catalog, enabling seamless integration with Azure Databricks and unlocking its full potential for your Unity development workflow.