Databricks Reliability

Acceldata Data Observability Cloud (ADOC) allows you to integrate with Databricks to ensure data reliability by monitoring dataset freshness, schema drift, and anomalies. This guide provides step-by-step instructions to connect your Databricks workspace (on AWS or Azure) to ADOC for Data Reliability, enabling you to maintain high-quality data for analytics and pipelines.

Prerequisites

Ensure the following requirements are met before you connect Databricks as a data source.

Common Prerequisites

• Access to ADOC Platform: Ensure you have login credentials for ADOC.
• Databricks Workspace: A running Databricks workspace on AWS or Azure.
• Databricks Workspace ID: Find in the workspace properties page (e.g., 1234567890).
• Databricks Warehouse ID: Obtain from the SQL Warehouse URL (e.g., your-warehouse-id from https://<instance>/sql/1.0/warehouses/your-warehouse-id).
• Personal Access Token: A secure key for Databricks API access.

AWS-Specific Prerequisites

1. Create a Databricks Personal Access Token

   1. In your Databricks workspace, navigate to User avatar > User Settings > Access Tokens.
   2. Click Generate New Token, assign a nickname (e.g., “ADOC-Token”), and set an expiration date (e.g., 90 days).
   3. Copy the token (displayed only once) and store it securely.

2. (Optional) Use AWS Secrets Manager

   1. Store the Personal Access Token in AWS Secrets Manager.
   2. Reference the secret by Amazon Resource Name (ARN) in ADOC for enhanced security.

Azure-Specific Prerequisites

1. Create an Azure Service Principal

   1. In Azure Portal, navigate to Azure Active Directory > App Registrations > New Registration.
   2. Name the app (e.g., “ADOC-Databricks-SP”) and click Register.
   3. Copy the Application (Client) ID, Directory (Tenant) ID, and create a Client Secret (under Certificates & secrets).
   4. Save these values securely.

2. Create and Assign a Custom Role

   1. In Azure Portal, navigate to Subscription or Databricks Resource Group > Access Control (IAM).
   2. Click Add > Add custom role and define permissions:

{

  "permissions": [

    {

      "actions": [

        "Microsoft.Databricks/workspaces/*/read",

        "Microsoft.Databricks/workspaces/clusters/read"

      ],

      "notActions": [],

      "dataActions": [],

      "notDataActions": []

    }

  ]

}

c. Assign the role to your Service Principal.

3. Add Service Principal to Databricks Workspace: In Databricks, navigate to Admin Settings > Users or Groups, add the Service Principal using its Client ID, and assign roles (e.g., Contributor).
4. Grant Workspace Admin Access: In the Databricks user list, locate the Service Principal and enable the Workspace Admin toggle for deep monitoring.
5. Gather Workspace Details: Collect your Databricks workspace URL (e.g., https://adb-xxxxxx.azuredatabricks.net) and workspace ID.

Prerequisite Comparison Table

Add Databricks as a Data Source

Follow these steps to connect your Databricks workspace to ADOC for Data Reliability. Steps are identical for AWS and Azure unless specified.

Step 1: Start Setup

1. In ADOC, select Register from the left menu.

2. Click Add Data Source and choose Databricks.

3. On the Data Source Details page:

   1. Enter a name (e.g., “Prod-Databricks-Reliability”).
   2. (Optional) Add a description (e.g., “Data reliability for analytics tables”).
   3. Enable the Data Reliability toggle.
   4. Select your data plane (e.g., Spark) from the dropdown.

4. Click Next.

Step 2: Add Connection Details

Provide the following details. Refer to Databricks Documentation for help finding IDs or URLs.

• Cloud Provider: Select AWS or Azure.
• Workspace Name: Enter a descriptive name (e.g., “Analytics-Workspace”).
• Token: Provide the Personal Access Token.
• Token Expiry Time: Set the expiration date (e.g., 90 days).
• Use Service Principal (optional, Azure):
• Enter Client ID, Client Secret, and Tenant ID.
• JDBC URL: Provide the connection string (e.g., jdbc:spark://adb-1234567890.azuredatabricks.net:443/default;transportMode=http;ssl=1;httpPath=sql/protocolv1/o/0/your-warehouse-id).
• Data Plane Engine: Select Spark (*Note: Pushdown is not supported with Service Principal*).

Step 3: Validate and Save Connection

1. Click Test Connection. If successful, you’ll see “Connected.” If it fails, check:

   • Invalid or expired Personal Access Token.
   • Incorrect Workspace URL or ID.
   • Wrong JDBC URL or SQL Warehouse issues.
   • Insufficient permissions for token or Service Principal.

2. Click Next.

Step 4: Configure Data Reliability

Configure settings for ADOC to monitor data reliability:

• Unity Catalog Enabled: Toggle ON if using Unity Catalog to monitor metadata and assets.
• JDBC Records Fetch Size: Set records per query (e.g., 1000) based on workspace size for optimal performance.
• Enable Crawler Execution Schedule: Toggle ON and set a schedule (e.g., daily at 2 AM UTC) for automated reliability checks.
• Click Submit to save. A Databricks card will appear on the ADOC Data Sources page, showing connection and crawler status.

What’s Next

With Data Reliability enabled, you can:

• Monitor Data Quality: Use ADOC to track dataset freshness, schema drift, and anomalies in your cloud-based platform.
• Set Policies: Define rules (e.g., missing-value checks, duplicates) for Unity Catalog or standard tables.
• Visualize Health: Use ADOC dashboards to view table and pipeline health trends.

Glossary

• DBU (Databricks Unit): A unit measuring compute usage for Databricks billing.
• Unity Catalog: Databricks’ governance solution for metadata and assets.
• JDBC URL: A connection string for Databricks SQL Warehouses.
• Personal Access Token: A secure key for Databricks API access.
• Torch: ADOC’s tool for data reliability, crawling Delta Lake and other sources.

Additional References

1. Databricks Documentation
2. Azure Custom Roles
3. Create or update Azure Custom Roles using the Azure Portal
4. AWS IAM Setup Documentation
5. Azure Service Principal Guide