Databricks Catalog Explorer: Governance Made Visual
In today’s data-driven enterprises, managing data assets, ensuring compliance, and enabling collaboration are critical. Yet, navigating hundreds or thousands of tables, databases, and pipelines can be overwhelming.
Databricks Catalog Explorer simplifies this challenge by providing a visual, interactive interface for governance, making it easy to discover, manage, and understand your data assets in the Lakehouse.
Why Catalog Explorer Matters
Consider a scenario where a data engineer or analyst needs to find all tables used in a sales dashboard or check who last modified a dataset. Without a visual governance tool:
- Data discovery is time-consuming
- Tracking data lineage is complex
- Compliance and auditing become difficult
Catalog Explorer addresses these issues by enabling:
- Visual data discovery: Quickly navigate tables, databases, and views
- Data lineage tracking: See how data flows across pipelines
- Collaboration: Share insights with team members while maintaining governance
- Audit and compliance: Track access, ownership, and modifications
How Catalog Explorer Works
- Connect to Lakehouse Metadata: Catalog Explorer pulls metadata from Databricks Catalog.
- Visualize Data Assets: Display databases, tables, views, and their relationships in an interactive UI.
- Inspect Lineage and Dependencies: See which datasets feed into dashboards, ML models, or reports.
- Governance & Access Control: Understand table ownership, access permissions, and compliance requirements.
Example: Discovering Table Lineage
Suppose you want to see all tables feeding the monthly_sales_report.
Steps in Catalog Explorer UI:
- Search for
monthly_sales_reportin the Catalog Explorer. - Click to expand lineage view.
- Visual graph shows:
raw_sales_data --> cleaned_sales_data --> monthly_sales_report
customer_info --------------------------> monthly_sales_report
Benefits of this view:
- Identify upstream dependencies
- Understand impact before modifying upstream tables
- Ensure compliance and audit readiness
Example: Checking Table Metadata
You can inspect a table directly to see its metadata:
| Property | Value |
|---|---|
| Table Name | cleaned_sales_data |
| Owner | data.engineering.team |
| Last Modified | 2025-12-10 |
| Columns | customer_id, product_id, revenue, date |
| Row Count | 1,250,000 |
| Access Permissions | read: analysts, write: engineers |
This visual and metadata-rich view reduces errors and speeds up governance tasks.
Key Benefits of Catalog Explorer
| Feature | Benefit |
|---|---|
| Visual Data Discovery | Quickly find tables, views, and datasets |
| Data Lineage Tracking | Understand dependencies and impact |
| Governance & Compliance | Ensure regulatory adherence and audit readiness |
| Collaboration | Share insights across teams securely |
| Metadata Inspection | Access ownership, modification history, and permissions |
Summary
Databricks Catalog Explorer turns data governance into a visual, intuitive process. By enabling data discovery, lineage tracking, and metadata inspection, it empowers teams to collaborate safely, maintain compliance, and make informed decisions. For modern enterprises, Catalog Explorer is the bridge between Lakehouse data assets and effective governance.