In the realm of big data and AI, managing and securing data assets efficiently is crucial. Databricks addresses this challenge with Unity Catalog, a comprehensive governance solution designed to streamline and secure data management across Databricks workspaces. This explores Unity Catalog features, advantages, and how it is different from other data catalog solutions
Topics:
This Articles Covers topics such as
- What is Unity Catalog and its Features?
- Advantages of Unity Catalog
- Unity Catalog vs. Other Data Catalog Tools: A Simple Comparison.
- Understanding the Object Hierarchy in Metastore
- Identifying the Admin Roles in Unity Catalog
- Unveiling Data Lineage in Unity Catalog: Capture and Visualize
- Simplifying Data Access using Delta Sharing
1. What is Unity Catalog?
The Unity Catalog is Databricks governance solution which integrates with Databricks workspaces and provides a centralized platform for managing metadata, data access, and security. It acts as a sophisticated metastore that not only organizes metadata but also enforces security and governance policies across various data assets and AI models.
1.1 Main Features of Unity Catalog
- Centralized Access Control: Administrators can establish and enforce access policies for all Databricks workspaces from a single location by centralizing data access management using Unity Catalog.
- Detailed Auditing and Lineage: It automatically tracks user activities and data lineage, providing insights into how data is created, transformed, and used across different environments.
- Data Discovery: Users can find and use data more effectively because to Unity Catalog’s tagging and documentation features.
- Unified Governance: It offers a comprehensive governance framework by supporting notebooks, dashboards, files, machine learning models, and both organized and unstructured data.
- Security Model: With a familiar syntax, the security model simplifies authorization management by adhering to ANSI SQL standards.
2. Advantages of the Unity Catalog
1. Efficient Data Governance
This centralized approach ensures consistent data management policies across all workspaces, reducing administrative overhead and minimizing the risk of mismanagement.
2. Enhanced Data Security
With its robust security model, Unity Catalog provides granular access control and compliance with industry standards. The ability to manage permissions at various levels like catalogs, schemas, tables, and views ensures that sensitive data is protected and accessible only to authorized users.
3. Improved Data Discovery
The tagging and documentation features in Unity Catalog facilitate better data discovery. Users can easily find and access the data they need, which enhances productivity
4. Comprehensive Metadata Management
It provides a unified view of all metadata, including data lineage and audit logs. This comprehensive approach helps organizations maintain data integrity and traceability, which is essential for regulatory compliance and data quality management.
5. Integration with Databricks Ecosystem
It seamlessly integrates with other Databricks services, such as Delta Lake and MLflow. This integration ensures that data governance is cohesive and consistent across all aspects of the data workflow.
3. Unity Catalog vs. Other Data Catalog Tools: A Simple Comparison
1. Integration:
Unity Catalog works easily with Databricks and ideal for those using Databricks for data & AI.
For DataHub & Microsoft Purview: Open-source and flexible, integrates with various tools. and Best for Azure environments.
2. Governance:
Unity Catalog is a unified governance for both data and AI.
For DataHub & Microsoft Purview: Focuses on metadata management and Comprehensive governance features.
3. Access Control:
It has a granular control at table, column, and row levels and whereas for others generally role-based access control.
4. Data Lineage:
Unity Catalog: Automatically captures and visualizes lineage.
Others: Support lineage but may vary in detail and visualization.
5. Data Discovery:
In Unity Catalog it is consolidated and user-friendly whereas for others it is robust and has extensive search capabilities.
6. Metadata Management:
All the tools are Centralized management for metadata.
7. Audit and Compliance:
In Unity Catalog it has Built-in features for auditing and compliance whereas in others it only supports a few audit logs and compliance features.
8. External Data Sharing:
In Unity Catalog it supports Delta Sharing for secure data sharing whereas in case of others it varies in their support for data sharing.
9. AI and ML Integration:
In Unity Catalog it is Integrated with MLflow for AI model management. Whereas for others Limited support for AI and ML.
10. Cloud Support:
In Unity Catalog it Supports AWS, Azure, and GCP. Whereas for others it might vary in cloud support, with some focused on specific clouds.
4. Understanding the Object Hierarchy in Metastore
In the world of data engineering, organizing and managing data assets efficiently is crucial. Unity Catalog on Databricks introduces a three-level database object hierarchy to streamline this process. Let’s understand the details of this hierarchy and its components.
Level I: Catalogs & Non-Data Securable Objects
Catalogs: Its purpose as Catalogs serve as the Top tier container for your data assets, helping you organize them effectively.
Its usage is to often mirror organizational units or different stages of the software development lifecycle, providing a clear structure for data isolation.
Non-Data Securable Objects: The main components include storage credentials and external locations.
The function of these objects is essential for managing your data governance model within Unity Catalog and reside directly under the metastore.
Level II: Schemas
The content of the Schemas contains tables, views, volumes, models, and functions and these help in organizing data and AI assets into more granular logical categories than catalogs, making data management more efficient.
Level III: Volumes, Tables, Views, Functions & Models
Volumes: It is a Logical volume of unstructured, non-tabular data stored in cloud object storage.
Tables: It is a collection of data organized by rows and columns and forming the core of structured data storage.
Views: It saves queries against one or more tables, providing a way to present data in a specific format without duplicating it.
Functions: A Units of saved logic that return a scalar value or a set of rows and is reusable for computations.
Models: AI models packaged with MLflow and registered in Unity Catalog, facilitating the management and deployment of machine learning models.
4.1 Pre-requisites for Using Unity Catalog
To Utilize Unity Catalog in Databricks, you need to meet several prerequisites:
Databricks Account: Ensure that Databricks account must be on the Premium plan or above.
Azure Active Directory (AAD): Must have the Azure Active Directory Global Administrator role.
Metastore: If there is no metastore in the same region as your workspace, then account admin must create one.
Workspace Configuration: Should Attach your Databricks workspace to the Unity Catalog metastore. The compute resource must be Databricks Runtime 11.3 or above and should use a Unity Catalog-compliant access mode.
Permissions: Must be a Databricks metastore admin or else user needs to have the CREATE CATALOG privilege.
Cloud Storage: Ensure you have the necessary permissions to create and manage storage resources Eg., GCS buckets on Google Cloud.
Now that you have a solid understanding of Unity Catalog’s features and comparisons, let’s dive deeper into admin roles, data lineage, and Delta Sharing—click here to continue.