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mlv-operations-cli

Manage Microsoft Fabric Materialized Lake View refresh schedules and job execution via REST APIs.

Updated Jul 14, 2026
Search Tags:#rest api#spark#lakehouse#job monitoring#microsoft fabric#refresh schedule#materialized lake view
One-click install
npx skills add https://github.com/9vantage/skills-for-fabric-clone --skill mlv-operations-cli-9vantage
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlv-operations-cli
Source: https://github.com/9vantage/skills-for-fabric-clone/tree/main/plugins/fabric-operations/skills/mlv-operations-cli
Command: npx skills add https://github.com/9vantage/skills-for-fabric-clone --skill mlv-operations-cli-9vantage

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Automating refresh schedules and monitoring for Materialized Lake Views (MLVs) in Microsoft Fabric lakehouses requires manual UI work or custom scripting against undocumented endpoints. This Skill provides validated REST API workflows to create, update, and delete refresh schedules, trigger on-demand refreshes, and monitor or cancel running jobs with human-in-the-loop safety confirmations. ## Core Features & Use Cases - Schedule Management: Create, list, update, and delete MLV refresh schedules (Cron interval, Daily, Weekly, Monthly) scoped to a lakehouse lineage. - Job Execution & Monitoring: Trigger on-demand refreshes, poll job status via the Location header, list run history, and cancel running jobs. - Failure Diagnostics: Read failureReason error codes from failed runs and route to actionable fixes (capacity issues, deleted MLVs, Spark errors). - Use Case: A data engineer wants the CustomerVoice lakehouse MLVs refreshed nightly at 2 AM UTC. The Skill resolves the workspace and lakehouse IDs, previews the schedule for confirmation, creates it via the Fabric REST API, and later helps investigate a failed run by extracting its error code. ## Quick Start Ask the assistant to schedule the MLVs in your Fabric lakehouse to refresh daily at 2 AM, providing your workspace and lakehouse names.

Frequently Asked Questions about mlv-operations-cli

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I schedule a Materialized Lake View refresh in Microsoft Fabric?▼

Use the Fabric REST API endpoint POST /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules with a configuration of type Cron, Daily, Weekly, or Monthly. The schedule refreshes the entire MLV lineage in the lakehouse, not individual tables.

How do I trigger an on-demand MLV refresh via REST API?▼

Send an empty POST to /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/instances. The API returns 202 Accepted with the job instance ID in the Location header, which you poll for status until it reaches Completed or Failed.

Can I schedule individual MLV tables at different times in Fabric?▼

No. The Fabric API supports only one active refresh schedule per lakehouse lineage, and each refresh runs all MLVs in dependency order. If MLVs live in separate lakehouses, you can create one schedule per lakehouse instead.

Can I list Materialized Lake Views in a lakehouse using the REST API?▼

No. The GET /materializedLakeViews endpoint returns 404 because discovery APIs are not yet available. You must provide the lakehouse ID and MLV table names manually, or use the Fabric Lakehouse UI for discovery, lineage, and data quality metrics.

Why did my MLV refresh job fail and how do I diagnose it?▼

List job instances via GET /instances, find the failed run, and read its failureReason.errorCode and message. MLV_SPARK_SESSION_REQUEST_SUBMISSION_FAILED indicates capacity or Spark pool issues, while MLV_SELECTED_NOT_FOUND means the MLV was deleted or renamed.

What permissions are required to manage MLV refresh schedules?▼

You need the Workspace Contributor or Admin role in Microsoft Fabric. Both user identity (az login) and Service Principal or Managed Identity authentication are supported for schedule and job operations.

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