databricks-unity-catalog

name	databricks-unity-catalog
description	Unity Catalog system tables and volumes. Use when querying system tables (audit, lineage, billing) or working with volume file operations (upload, download, list files in /Volumes/).
compatibility	Requires databricks CLI (>= v1.0.0)
metadata	{"version":"0.1.0"}
parent	databricks-core

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

Topic	File	Description
System Tables	references/5-system-tables.md	Lineage, audit, billing, compute, jobs, query history
Volumes	references/6-volumes.md	Volume file operations, permissions, best practices
Data Profiling	references/7-data-profiling.md	Data profiling, drift detection, profile metrics

Quick Start

Create Unity Catalog Objects (CLI)

IMPORTANT: Use --json for creating UC objects. Positional args vary by command and version.

# Create a catalog
databricks catalogs create my_catalog

# Create a schema  (args: NAME CATALOG_NAME — positional, name first)
databricks schemas create my_schema my_catalog

# Create a volume  (args: CATALOG_NAME SCHEMA_NAME NAME VOLUME_TYPE — catalog first)
databricks volumes create my_catalog my_schema my_volume MANAGED

# List catalogs, schemas, volumes
databricks catalogs list
databricks schemas list my_catalog
databricks volumes list my_catalog.my_schema

Volume File Operations (CLI)

databricks fs requires the dbfs: scheme prefix even for UC Volume paths — without it the CLI treats the path as local filesystem and errors with no such directory.

# List files in a volume
databricks fs ls dbfs:/Volumes/catalog/schema/volume/path/

# Upload a directory's contents to a volume (-r copies contents, not the directory itself)
databricks fs cp -r --overwrite /tmp/data dbfs:/Volumes/catalog/schema/volume/dest

# Download a file from a volume
databricks fs cp dbfs:/Volumes/catalog/schema/volume/file.csv /tmp/file.csv

# Create a directory in a volume
databricks fs mkdirs dbfs:/Volumes/catalog/schema/volume/new_folder

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;

SQL Queries via CLI

Use databricks experimental aitools tools query for system table queries:

# Query lineage via CLI
databricks experimental aitools tools query --warehouse WAREHOUSE_ID "
  SELECT source_table_full_name, target_table_full_name
  FROM system.access.table_lineage
  WHERE event_date >= current_date() - 7
"

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-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