Warehouse operations skill sqldw-cli

Applies to: ✅ SQL analytics endpoint and Warehouse in Microsoft Fabric

Important

This feature is in preview.

The Fabric Data Warehouse operations capability of the sqldw-cli skill is part of Skills for Fabric. For installation, supported tools, and general usage, see Skills for Fabric overview. Use the skill from a compatible AI coding tool to investigate a warehouse or lakehouse SQL analytics endpoint with bounded, read-only diagnostics.

To get started using sqldw-cli, describe the problem in natural language. The skill selects the relevant diagnostics, runs them with your existing Fabric permissions, and returns evidence-based recommendations. The sqldw-cli skill will never change data, schema, or configuration.

Prerequisites

  • Install Skills for Fabric.
  • Register the Fabric SQL endpoint MCP server if your installation doesn't configure it.
  • You need to be a member of the Contributor workspace role or higher to read Query Insights and view complete query text.
  • Provide the workspace and warehouse or SQL analytics endpoint name.
  • Install the Microsoft Fabric Capacity Metrics app for capacity investigations.
  • Use VIEW DEFINITION on a lakehouse SQL analytics endpoint for table-health analysis.

Supported diagnostics

Diagnostic What it helps you do
failure-analysis Separate failures from cancellations, identify affected workloads, and resolve failed engine codes.
resource-consumers Find expensive query patterns and distinguish higher execution volume from increased per-run cost.
capacity-metrics-correlation Identify a costly warehouse or SQL analytics endpoint, then analyze Query Insights requests in the same time window.
pool-pressure Correlate SQL pool pressure intervals with overlapping requests and workload costs.
lakehouse-health Check lakehouse tables for file-count, deleted-row, and checkpoint issues.
query-reference Select the supported system views and procedures for the requested analysis.
scenarios Combine diagnostics for incidents such as slowdowns and capacity spikes.

The skill reruns diagnostics for each request, treats zero rows as valid evidence, and identifies the source of reported measurements.

How to use Skills with SQL database in Fabric

  1. Open a terminal in your project or working folder.
  2. Type Copilot and select Enter. GitHub Copilot launches.
  3. To authenticate with the Microsoft identity that has access to the target Fabric workspace, type az login and follow the steps.

Example prompts

Include the time range in UTC when possible.

Analyze failed and canceled queries in <workspace>/<warehouse> 
during the last 24 hours. Identify affected users, applications, and query patterns.

Use the Fabric Capacity Metrics app to investigate the capacity 
spike from 14:00 to 15:00 UTC. If a warehouse caused it, identify 
the expensive users, applications, and query patterns.

Assess whether recurring workloads in <warehouse> are candidates for custom
SQL pools based on the last 30 days. Use read-only diagnostics.

Understand the response

The skill organizes results into five sections:

Section What it contains
Diagnosis The conclusion supported by the diagnostics.
Evidence Measurements and their source views or procedures.
Ruled out Tested explanations and causes that weren't evaluated.
Recommendations Actions tied to the observed evidence.
Follow-ups What to do next and what to measure again.

An overlap between a pressure interval and a request supports correlation, not causation. The skill compares CPU, elapsed time, and storage scans before it identifies a likely workload contributor.

The skill doesn't perform lakehouse maintenance or configure custom SQL pools. It can identify a stable application classifier and recommend a custom pool pilot when historical requests show repeated contention. It also identifies the pressure, latency, CPU, scans, and failures to compare after the pilot.

Checklist to review and execute the T-SQL query

  1. Verify the workspace, warehouse, schema, and object names.
  2. Review generated T-SQL for destructive operations, transaction behavior, and data scope.
  3. Ask for an impact summary and rollback approach before authoring changes. For schema changes, prepare a rollback or undo T-SQL script.
  4. Execute only after the plan matches your intent.
  5. Validate the result with a separate read-only query.

Correlate capacity and SQL activity

Capacity Metrics models can expose timestamped data, fixed windows, or broader totals. The skill reports the timeframe supported by the installed model. It doesn't present a fixed-window result as an exact arbitrary interval.

When the model identifies a costly warehouse or SQL analytics endpoint, the skill analyzes Query Insights requests that overlap the same time window.

Important

A Capacity Metrics operation identifier isn't the same as Query Insights distributed_statement_id. The skill doesn't join these fields. It correlates the resolved warehouse or SQL analytics endpoint and the overlapping time range.

Capacity unit seconds and Query Insights CPU milliseconds are different measurements. The skill doesn't convert one into the other or derive a custom SQL pool percentage from either value.

Limitations

  • Query Insights retains 30 days of history and can lag by up to 15 minutes.
  • Capacity Metrics might expose a fixed window instead of arbitrary timestamps.
  • Capacity Metrics timestamps aren't necessarily documented as UTC.
  • SQL pool event logging can pause while a warehouse is inactive.
  • The usable correlation period is the overlap between Capacity Metrics and Query Insights history.

The skill reports data gaps and timeframe constraints as evidence instead of turning them into unsupported conclusions.

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