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databricks-unity-catalog
About databricks-unity-catalog
A skill guide for Databricks Unity Catalog, aimed at users managing volumes, querying system tables, and reviewing governance data. It covers file operations, lineage, audits, usage, and data quality, including workflows that use Databricks MCP tools.
Key features
- 01Upload, download, and browse volume files
- 02Query table and column lineage
- 03Analyze audit logs and permission changes
- 04Monitor billing, compute, and jobs
- 05Review query performance and data quality
Use cases
- 01Trace table dependencies and column lineage
- 02Review access records and permission changes
- 03Analyze DBU consumption and compute usage
- 04Detect data quality drift and inspect profile metrics
MCP support
Describes using Databricks MCP tools to query system tables with SQL and lists MCP tools for volume file operations.
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ databricks-solutions/ ai-dev-kit --skill databricks-unity-catalogAfter installing, type/databricks-unity-catalogin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
5-system-tables.md6-volumes.md7-data-profiling.mdDetails
From the project READMEUnity 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 | 5-system-tables.md | Lineage, audit, billing, compute, jobs, query history |
| Volumes | 6-volumes.md | Volume file operations, permissions, best practices |
| Data Profiling | 7-data-profiling.md | Data profiling, drift detection, profile metrics |
Quick Start
Volume File Operations (MCP Tools)
| Tool | Usage |
|---|---|
list_volume_files | list_volume_files(volume_path="/Volumes/catalog/schema/volume/path/") |
get_volume_folder_details | get_volume_folder_details(volume_path="catalog/schema/volume/path", format="parquet") - schema, row counts, stats |
upload_to_volume | upload_to_volume(local_path="/tmp/data/*", volume_path="/Volumes/.../dest") |
download_from_volume | download_from_volume(volume_path="/Volumes/.../file.csv", local_path="/tmp/file.csv") |
create_volume_directory | create_volume_directory(volume_path="/Volumes/.../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;
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
- Filter by date - System tables can be large; always use date filters
- Use appropriate retention - Check your workspace's retention settings
- Grant minimal access - System tables contain sensitive metadata
- 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
Comments
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