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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/).
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SOC 職業分類に基づく
| 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/). |
Guidance for Unity Catalog system tables, volumes, and governance.
Use this skill when:
/Volumes/)| 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 |
| 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") |
-- 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`;
-- 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;
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"
)
Create and configure Declarative Automation Bundles (formerly Asset Bundles) with best practices for multi-environment deployments (CICD). Use when working with: (1) Creating new DAB projects, (2) Adding resources (dashboards, pipelines, jobs, alerts), (3) Configuring multi-environment deployments, (4) Setting up permissions, (5) Deploying or running bundle resources
Patterns and best practices for Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. Use when creating Lakebase instances, connecting applications or Databricks Apps to PostgreSQL, implementing reverse ETL via synced tables, storing agent or chat memory, or configuring OAuth authentication for Lakebase.
Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill", "evaluate skill", "skill regression", "ground truth", "GRP pipeline", "skill quality", and "skill metrics".
Builds Databricks applications. Prefers AppKit (TypeScript + React SDK) for new apps; falls back to Python frameworks (Dash, Streamlit, Gradio, Flask, FastAPI, Reflex) when Python is required. Handles OAuth authorization, app resources, SQL warehouse and Lakebase connectivity, model serving, foundation model APIs, and deployment. Use when building web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions AppKit, Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app.
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.