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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 |
# List files in a volume
list_volume_files(volume_path="/Volumes/catalog/schema/volume/folder/")
# Upload file to volume
upload_to_volume(
local_path="/tmp/data.csv",
volume_path="/Volumes/catalog/schema/volume/data.csv"
)
# Download file from volume
download_from_volume(
volume_path="/Volumes/catalog/schema/volume/data.csv",
local_path="/tmp/downloaded.csv"
)
# Create directory
create_volume_directory(volume_path="/Volumes/catalog/schema/volume/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"
)
This skill should be used when the user asks to "write Gherkin", "create feature files", "generate BDD scenarios", "write acceptance tests in Gherkin", "create Behave features", "write Given When Then tests", "BDD test cases for my pipeline", "Gherkin for Unity Catalog", or wants to translate requirements into Gherkin feature files for Databricks.
This skill should be used when the user asks to "run BDD tests", "execute Behave", "run Gherkin tests", "run my feature files", "behave test results", "run smoke tests", "BDD test report", or needs to execute Behave test suites with specific options like tag filtering, parallel execution, or CI reporting.
This skill should be used when the user asks to "set up BDD", "create a Behave project", "scaffold BDD tests", "initialize Behave", "add BDD to my project", "set up Gherkin testing", "create test structure for Behave", or mentions setting up behavior-driven development testing. Generates a complete Behave project structure wired to Databricks SDK.
This skill should be used when the user asks to "write step definitions", "implement BDD steps", "generate step code", "create Behave steps", "implement Given When Then", "write Python steps for Gherkin", "step definitions for Databricks", or needs to create Python step implementations for existing Gherkin feature files.
Deploy and query Databricks Model Serving endpoints. Use when (1) deploying MLflow models or AI agents to endpoints, (2) creating ChatAgent/ResponsesAgent agents, (3) integrating UC Functions or Vector Search tools, (4) querying deployed endpoints, (5) checking endpoint status. Covers classical ML models, custom pyfunc, and GenAI agents.
Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.