Databricks Unity Catalog

Unity Catalog

Guidance for Unity Catalog system tables, volumes, and governance.

When to Use This Skill

Use this skill when:

• Working with volumes (upload, download, list files in /Volumes/)
• Querying lineage (table dependencies, column-level lineage)
• Analyzing audit logs (who accessed what, permission changes)
• Monitoring billing and usage (DBU consumption, cost analysis)
• Tracking compute resources (cluster usage, warehouse metrics)
• Reviewing job execution (run history, success rates, failures)
• Analyzing query performance (slow queries, warehouse utilization)
• Profiling data quality (data profiling, drift detection, metric tables)

Reference Files

Quick Start

Volume File Operations (MCP Tools)

Enable System Tables Access

-- Grant access to system tables
GRANT USE CATALOG ON CATALOG system TO `data_engineers`;
GRANT USE SCHEMA ON SCHEMA system.access TO `data_engineers`;
GRANT SELECT ON SCHEMA system.access TO `data_engineers`;

Common Queries

-- Table lineage: What tables feed into this table?
SELECT source_table_full_name, source_column_name
FROM system.access.table_lineage
WHERE target_table_full_name = 'catalog.schema.table'
  AND event_date >= current_date() - 7;

-- Audit: Recent permission changes
SELECT event_time, user_identity.email, action_name, request_params
FROM system.access.audit
WHERE action_name LIKE '%GRANT%' OR action_name LIKE '%REVOKE%'
ORDER BY event_time DESC
LIMIT 100;

-- Billing: DBU usage by workspace
SELECT workspace_id, sku_name, SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
WHERE usage_date >= current_date() - 30
GROUP BY workspace_id, sku_name;

MCP Tool Integration

Use mcp__databricks__execute_sql for system table queries:

# Query lineage
mcp__databricks__execute_sql(
    sql_query="""
        SELECT source_table_full_name, target_table_full_name
        FROM system.access.table_lineage
        WHERE event_date >= current_date() - 7
    """,
    catalog="system"
)

Best Practices

1. Filter by date - System tables can be large; always use date filters
2. Use appropriate retention - Check your workspace's retention settings
3. Grant minimal access - System tables contain sensitive metadata
4. Schedule reports - Create scheduled queries for regular monitoring

Related Skills

• databricks-spark-declarative-pipelines - for pipelines that write to Unity Catalog tables
• databricks-jobs - for job execution data visible in system tables
• databricks-synthetic-data-gen - for generating data stored in Unity Catalog Volumes
• databricks-aibi-dashboards - for building dashboards on top of Unity Catalog data

Resources

• Unity Catalog System Tables
• Audit Log Reference