Databricks Unity Catalog
Databricks Unity Catalog is a comprehensive governance and security solution for data and AI assets on Databricks. With its unique centralized access control, compliance, and cost management structure, Unity Catalog empowers organizations to manage their data efficiently across multiple environments while maintaining security and regulatory standards. This blog will cover the key features, structure, technical implementation, and best practices for Unity Catalog, offering insights into how it simplifies data governance and enhances the value of Databricks for data engineering, machine learning, and analytics.
Key Features of Databricks Unity Catalog – Add project/client implementation details in few lines for these key features
- Centralized Data Access Control: Easily manage permissions across catalogs, schemas, and tables from a single interface.
- Fine-Grained Access Control with Standards-Compliant Security: Enforce granular data permissions at the column, row, and table levels to meet industry compliance requirements.
- Data Lineage and Audit Logging: Track data transformations and user actions for transparency and regulatory auditing.
- Data Discovery and Search: Quickly locate data assets with powerful search and metadata-driven discovery tools.
- Cost Efficiency through Databricks Units (DBUs): Optimize costs by tailoring compute usage to workload requirements with flexible pricing.
Unity Catalog Object Model and Structure
Unity Catalog organizes data assets within a three-tiered hierarchy and operates on a metastore — a top-level container for metadata and governance policies across Databricks.
Metastore: At the top of the hierarchy, the metastore registers all data assets and defines permissions for each.
Catalogs: These group data assets by departments, projects, or domains, managing access at a high level.
Schemas: Similar to traditional databases, schemas within catalogs group related tables, views, and other data assets, ensuring a structured and secure environment for data management.
Tables and Views: The lowest level contains the actual data, organized in managed tables (where Databricks controls storage) or external tables (with data stored externally).
This structure ensures flexibility and control, allowing organizations to define access policies at each level to meet their security and organizational needs.
Unity Catalog Storage and Managing Permissions
Unity Catalog’s access control model makes it easy to assign permissions at various levels, from catalogs down to individual columns.
Key aspects of this model include:
- Catalog-Level Permissions: Control access to all data within a catalog.
- Schema-Level Permissions: Define user access to specific tables or views within a schema.
- Row-Level and Column-Level Security: Add a layer of granularity, allowing restricted access to sensitive data fields.
Administrators can manage permissions through ANSI SQL commands or programmatically with Databricks CLI, Catalog Explorer, or REST APIs.
Advanced Security and Sharing Capabilities
Unity Catalog provides more than just table-level permissions. It also includes:
- Service and Storage Credentials: Securely manage long-term connections to cloud services and storage without repeatedly handling credentials.
- External Locations: Define paths for external data, making it accessible without directly storing it in Databricks.
- Delta Sharing: A unique feature that enables secure, shareable data links for external partners and clients without duplicating data.
Data Lineage and Auditing for Compliance
Unity Catalog’s built-in lineage tracking and audit logging ensure organizations can maintain a transparent record of data flows and transformations. This feature captures how data moves and changes across different jobs, making it easy to trace data sources, transformation steps, and destinations for compliance or auditing purposes.
Audit logs track user activity, recording every action taken on data assets to detect unauthorized access or activity anomalies.
Implementing Unity Catalog: Step-by-Step Guide
Implementing Unity Catalog on Azure Databricks ensures streamlined data governance and enhanced security for your data assets. Here’s a simplified guide to help you set it up, based on high-level steps from the Databricks documentation. Keep in mind that setup details might vary slightly across AWS and GCP.
1. Set Up a Storage Account
Start by creating a Storage Account in your Azure production subscription. This is essential as Unity Catalog stores its metadata and objects here.
- Head to the Azure Portal and create a storage account.
- Choose Standard performance with a Hot access tier, ensuring the region aligns with your Databricks workspace.
- Make a note of the container name that will store Unity Catalog objects.
2. Enable Databricks Access Connector
From the Azure Marketplace, locate and deploy the Databricks Access Connector. During the setup process, assign it a Managed Identity to facilitate secure interactions with other Azure resources.
3. Grant Storage Account Permissions
Provide the necessary access to the Databricks Access Connector:
- Navigate to the storage account’s Access Control (IAM) settings.
- Assign the Storage Blob Data Contributor role to the Managed Identity associated with the connector.
4. Log in to the Databricks Admin Console
As a Global Administrator, log in to the Databricks Admin Console at
accounts.azuredatabricks.net. This console serves as the central hub for managing Databricks accounts and Unity Catalog configurations.
5. Assign the Databricks Administration Role
Delegate the responsibility of managing Databricks to another user or group:
- Go to the Admin Console > Admin Roles section.
- Assign the Databricks Account Admin role to a trusted user or team.
6. Create a Unity Catalog Metastore
A metastore is the backbone of Unity Catalog, connecting your Databricks workspace to its data governance framework:
- In the Admin Console, navigate to Metastores.
- Click Create Metastore and provide the required details, including the metastore name and storage location.
7. Link Workspaces to the Metastore
To enable Unity Catalog in a workspace:
- Attach your Databricks workspaces to the newly created metastore.
- This step ensures the workspace is Unity Catalog-ready.
8. Sync Users, Groups, and Service Principals
Integrate your Databricks users and groups with Unity Catalog using the SCIM connector:
- This synchronization enables user and group-level permissions for data governance within Unity Catalog.
9. Assign Users and Groups to Workspaces
Through the Admin Console, assign the appropriate users and groups to your Databricks workspaces. This allows users to access and manage Unity Catalog features seamlessly.
10. Configure External Locations
Log in to your Unity Catalog-enabled workspace to set up external locations for data that resides outside the catalog. This step is necessary for managing external tables and ensuring smooth data access.
Cost Management with Databricks Units (DBUs)
Unity Catalog’s DBU-based pricing model helps organizations optimize their Databricks usage by charging based on the workload’s compute time rather than raw computing resources.
Some practical cost-saving measures include:
- Optimizing Cluster Configurations: Match clusters to workload types (interactive, ML, or Photon) to balance performance and cost.
- Using Auto-Scaling and Spot Instances: Reduce unnecessary compute time by leveraging spot instances or auto-scaling clusters.
- Scheduling Jobs Efficiently: Run cost-intensive jobs during off-peak hours and convert ad-hoc tasks into scheduled jobs to save on DBU costs.
These strategies allow organizations to align their Databricks usage with operational needs while controlling costs effectively.
Conclusion: Embracing Unity Catalog for Enhanced Data Governance
Databricks Unity Catalog provides a unified platform for data governance, combining robust security, fine-grained permissions, and cost efficiency. By centralizing access control and ensuring compliance, Unity Catalog simplifies data management and enhances collaboration for modern data teams.
