Spark CLI

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MLV Operations — CLI Skill

Terminology Mapping

Fabric has three materialized view concepts. Disambiguate by context:

Disambiguation rule: If the user mentions lakehouse, notebook, Spark, Delta, or MLV → it's a Materialized Lake View (this skill). If they mention KQL, Eventhouse, or Kusto → it's a KQL Materialized View (different skill). If they mention Warehouse or SQL DW → explain it's not supported.

Default: If context is unclear (no mention of lakehouse, Spark, KQL, or Warehouse), ask the user: "Are you working with a Lakehouse (Materialized Lake View) or an Eventhouse (KQL Materialized View)?" before proceeding.

Interactive recurring refresh (no API or CI/CD): Direct users to Lakehouse → Materialized lake views → Manage → Schedules. Do not substitute notebook, pipeline, or generic Refresh → Schedule refresh navigation.

Manage MLV refresh scheduling, execution definitions, and monitoring using Fabric REST APIs. This skill provides public API coverage (Preview) for scheduling, subset refresh configuration, and monitoring operations, enabling automation of MLV refresh workflows.

What This Skill Can Do

✅ Fully Supported (14 REST APIs)

1. Schedule Management (per lakehouse — refreshes entire MLV lineage by default)

   • Create refresh schedules (Cron interval, Daily, Weekly, Monthly)

   • List schedules for a lakehouse

   • Get schedule details by ID

   • Update existing schedules (change frequency, enabled state)

   • Delete schedules

   • Attach an executionData.mlvExecutionDefinitionId to refresh a subset of the lineage

2. Job Execution

   • Trigger on-demand refresh (immediate execution)

   • List job run history with filtering

   • Get job status and progress

   • Cancel running jobs

   • Attach an executionData.mlvExecutionDefinitionId to refresh a subset of the lineage on demand

3. MLV Execution Definitions

   • Create reusable execution definitions for selected MLVs, selected upstream lakehouses, refresh mode, and Spark environment

   • List execution definitions

   • Get execution definition details

   • Patch execution definitions (partial update; omitted fields retain existing values)

   • Delete execution definitions; linked schedules are removed by the API

4. Safety & UX

   • Human-in-the-loop confirmations before creating schedules or triggering refreshes

   • Step-by-step planning for complex multi-MLV operations

   • Iterative error handling with helpful suggestions

   • Preview schedule impact before execution

❌ Not Supported through REST APIs

• REST MLV Discovery: GET /materializedLakeViews returns 404; use Spark SQL discovery instead

• Visual Lineage Inspection: Cannot fetch the portal dependency graph directly

• Data Quality Metrics: Cannot retrieve DQ metrics (API returns 404)

• Schema Verification: Cannot check if schemas are enabled (property missing)

Discovery fallback: Resolve the lakehouse, then run SHOW MATERIALIZED LAKE VIEWS IN <schema> through a Lakehouse Livy session (consumption mode) or Fabric notebook (authoring mode). Ask the user for MLV names only when no Spark execution surface is available. Then encode the selected MLV names and upstream lakehouses in an MLV execution definition.

Table of Contents

Eval mode: When the user prompt contains "I pre-approve" or "proceed without confirmation", skip the human-in-the-loop gate and execute directly. This allows automated testing.

Must

1. Human-in-the-loop confirmations

Before executing any operation, show a preview and ask for confirmation:

• Creating schedules: Show schedule type, interval/times, lakehouse ID, start/end time, and full-lineage or execution-definition scope

• Creating execution definitions: Show selected MLVs, selected upstream lakehouses, refresh mode, and Spark environment

• Triggering refreshes: Show lakehouse ID and whether this refreshes the full lineage or a named execution definition subset

• Deleting schedules: Show schedule ID and confirm deletion

• Deleting execution definitions: Show definition ID and warn that linked schedules are also removed

Confirmation options:

• Allow — Execute this single operation

• Decline — Skip this operation

• Allow in this thread — Auto-allow all operations in current conversation

Example:

I'm about to create a refresh schedule:

  Lakehouse ID: abc-123-def
  Type: Daily
  Time: 02:00 UTC
  Start: 2026-06-20
  End: 2027-06-20
  Scope: Full MLV lineage (default)

Proceed? [Allow / Decline / Allow in this thread]

2. Use REST APIs exactly as documented

Base URL: https://api.fabric.microsoft.com/v1

Deterministic PowerShell execution:

• Workspace list objects use displayName, not name. List once, filter displayName by exact equality, and require exactly one match instead of retrying alternate fields or filters.

• For every non-empty JSON body, serialize once, write it with [IO.File]::WriteAllText($path, $json, [Text.UTF8Encoding]::new($false)), then pass --headers "Content-Type=application/json" and --body "@$path" to az rest. Do not pass inline JSON or a JSON variable on Windows.

• Submit each mutation once. Treat a 4xx response with isRetriable: false as final: report it and do not retry the POST with another encoding.

IMPORTANT: Schedule, on-demand trigger, execution-definition, and history-list endpoints are workspace + lakehouse scoped. An on-demand trigger returns an item-scoped Location URL for status polling and cancellation. A schedule or on-demand run refreshes the entire MLV lineage by default. To refresh selected MLVs or selected upstream lakehouses, create an MLV execution definition and pass its mlvExecutionDefinitionId in executionData.

Schedule endpoints:

• POST /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules — Create schedule

• GET /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules — List schedules

• GET /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules/{id} — Get schedule

• PATCH /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules/{id} — Update schedule

• DELETE /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules/{id} — Delete schedule

Job instance endpoints:

• POST /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/instances — Trigger on-demand refresh (optional executionData; returns 202 + Location header with job ID)

• GET /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/instances — List job history

• GET /workspaces/{workspaceId}/items/{lakehouseId}/jobs/instances/{jobInstanceId} — Get job status from the trigger Location

• POST /workspaces/{workspaceId}/items/{lakehouseId}/jobs/instances/{jobInstanceId}/cancel — Cancel the running job

MLV execution definition endpoints:

• POST /workspaces/{workspaceId}/lakehouses/{lakehouseId}/mlvexecutiondefinitions — Create selected-lineage execution definition

• GET /workspaces/{workspaceId}/lakehouses/{lakehouseId}/mlvexecutiondefinitions — List execution definitions

• GET /workspaces/{workspaceId}/lakehouses/{lakehouseId}/mlvexecutiondefinitions/{mlvExecutionDefinitionId} — Get execution definition

• PATCH /workspaces/{workspaceId}/lakehouses/{lakehouseId}/mlvexecutiondefinitions/{mlvExecutionDefinitionId} — Partially update execution definition

• DELETE /workspaces/{workspaceId}/lakehouses/{lakehouseId}/mlvexecutiondefinitions/{mlvExecutionDefinitionId} — Delete execution definition and any linked schedules

Job type mismatch trap: Job history can expose scheduled MLV runs as jobType: "MaterializedLakeViews", but the public job scheduler path uses refreshMaterializedLakeViews. Do not copy the history value into POST /workspaces/{workspaceId}/items/{lakehouseId}/jobs/instances?jobType=MaterializedLakeViews; that item-level call returns InvalidJobType.

See: MS Learn: MLV Background Jobs

3. Authentication

All scheduling operations (create/update/delete, trigger, status, cancel) support both User identity (az login) and Service Principal / Managed Identity. Requires Workspace Contributor or Admin role.

4. Lakehouse schedule limits and execution-definition scopes

The Preview REST scheduler supports refresh schedules at lakehouse scope. A lakehouse can have at most 20 schedules, and an MLV lineage can have only one active refresh schedule. If the user asks for a subset schedule, create an MLV execution definition with selected MLVs/upstream lakehouses and pass its mlvExecutionDefinitionId in schedule or on-demand executionData. Execution definitions scope a refresh; they don't bypass scheduler limits.

5. MLV discovery through Spark SQL

GET /materializedLakeViews returns 404, but that REST limitation does not make discovery impossible. Resolve the lakehouse and run:

SHOW MATERIALIZED LAKE VIEWS IN <schema>;

Execute it through a Lakehouse Livy session (consumption mode) or notebook cell (authoring mode). See Materialized Lake View Patterns — SQL management commands. Ask the user for MLV names only when a Spark session is unavailable.

6. Run History diagnostic workflow

When a user asks "why did my refresh fail?" or "show me run history", follow this sequence:

1. List recent runs: GET /instances — returns job instances with status, start/end times

2. Show run summary: Display table with run ID, status, start/end time, duration

3. Select failed run: If multiple, ask user which one to investigate

4. Read error code: Extract failureReason.errorCode and failureReason.message from the failed instance

5. Suggest next steps: Based on error code:

   • MLV_SPARK_SESSION_REQUEST_SUBMISSION_FAILED → Check capacity availability, Spark pool config

   • MLV_SELECTED_NOT_FOUND → MLV table was deleted or renamed, verify it exists

   • Other Spark errors → Route to the operations mode for OOM, skew, shuffle spill diagnosis

6. Per-view details: The API returns lineage-level status only. Per-view status (which individual MLVs failed) is available in the UI Recent runs page — direct the user there for view-level breakdown

Run statuses (from API): NotStarted, InProgress, Completed, Failed, Cancelled, Deduped

Note: Run history retention may be limited. If older runs are missing, check the Recent runs page in the Lakehouse UI.

Prefer

• Daily/Weekly types for precise time-of-day scheduling (e.g., "2 AM daily")

• Cron type with interval only for sub-daily frequencies (e.g., "every 60 minutes")

• Step-by-step planning — clarify intent, propose schedule, show preview, execute on approval

• Iterative error handling — on failure, explain what went wrong and suggest actionable fixes

• Explicit timezone in every schedule (localTimeZoneId)

• Cross-lakehouse scheduling from extended lineage — when MLVs span multiple lakehouses, schedule from the downstream lakehouse's lineage view. Extended lineage refreshes upstream dependencies automatically in dependency order. Prefer this over creating separate schedules on each lakehouse individually.

Avoid

• Per-table scheduling claims without an execution definition — full-lineage is the default; selected subsets require a saved execution definition

• Cron string expressions (e.g., 0 2 * * *) — the API uses structured types, not cron strings

• Assuming JSON response from on-demand refresh — returns 202 with job ID in Location header only

• Silent failures — always explain errors

• Scheduling from notebooks or pipelines — recurring MLV refresh belongs to Lakehouse schedules. SQL REFRESH ... FULL is for one-time manual troubleshooting only.

Schedule Payload Structure

Create Schedule (POST /schedules)

Endpoint: POST /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules

{
  "enabled": true,
  "configuration": {
    "type": "Cron",
    "interval": 60,
    "startDateTime": "2026-06-20T00:00:00",
    "endDateTime": "2027-06-20T23:59:59",
    "localTimeZoneId": "UTC"
  }
}

Subset lineage schedule: include executionData when the user wants to refresh only the MLVs/upstream lakehouses captured in an execution definition:

{
  "enabled": true,
  "configuration": {
    "type": "Cron",
    "interval": 60,
    "startDateTime": "2026-06-20T00:00:00",
    "endDateTime": "2027-06-20T23:59:59",
    "localTimeZoneId": "UTC"
  },
  "executionData": {
    "mlvExecutionDefinitionId": "<mlvExecutionDefinitionId>"
  }
}

Key fields:

• enabled: true to enable schedule on creation

• type: One of "Cron", "Daily", "Weekly", "Monthly"

• interval: (Cron only) Refresh interval in minutes (e.g., 60 = hourly, 120 = every 2 hours)

• times: (Daily/Weekly/Monthly) Array of times in "HH:MM" format, e.g., ["02:00"]

• weekdays: (Weekly only) e.g., ["Monday", "Wednesday", "Friday"] — PascalCase day names

• recurrence: (Monthly only) Recurrence interval, e.g., 1 (every month)

• occurrence: (Monthly only) e.g., {"occurrenceType": "DayOfMonth", "dayOfMonth": 1}

• localTimeZoneId: Windows time zone names — "UTC", "Central Standard Time", "India Standard Time", etc.

• startDateTime: When schedule becomes active (ISO 8601 local time; localTimeZoneId supplies the time zone)

• endDateTime: REQUIRED — When schedule expires

• executionData.mlvExecutionDefinitionId: Optional; refreshes only the selected lineage captured in that execution definition

Daily example (preferred for "2 AM every day"):

{ "enabled": true, "configuration": { "type": "Daily", "times": ["02:00"], "startDateTime": "2026-06-20T00:00:00", "endDateTime": "2027-06-20T23:59:59", "localTimeZoneId": "UTC" } }

Weekly example (weekdays at 6 AM):

{ "enabled": true, "configuration": { "type": "Weekly", "times": ["06:00"], "weekdays": ["Monday", "Friday"], "startDateTime": "2026-06-20T00:00:00", "endDateTime": "2027-06-20T23:59:59", "localTimeZoneId": "UTC" } }

Monthly example (1st of each month at midnight):

{ "enabled": true, "configuration": { "type": "Monthly", "recurrence": 1, "occurrence": {"occurrenceType": "DayOfMonth", "dayOfMonth": 1}, "times": ["00:00"], "startDateTime": "2026-06-20T00:00:00", "endDateTime": "2027-06-20T23:59:59", "localTimeZoneId": "UTC" } }

WARNING: Do NOT use "days": [1, 15] for Monthly — this returns 400 InvalidConfiguration. Use recurrence + occurrence as shown above.

Update Schedule (PATCH /schedules/{id})

Endpoint: PATCH /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/schedules/{id}

{
  "enabled": true,
  "configuration": {
    "type": "Cron",
    "interval": 120,
    "startDateTime": "2026-06-20T00:00:00",
    "endDateTime": "2027-06-20T23:59:59",
    "localTimeZoneId": "UTC"
  }
}

Note: The update API requires both enabled and a complete configuration (full replacement, not partial patch). Always send all fields.

MLV Execution Definitions

Use execution definitions when the user wants to refresh specific MLVs, include selected upstream lakehouses, pin a Spark environment, or choose a refresh mode independently from the default full-lineage refresh.

Create MLV Execution Definition

Endpoint: POST /workspaces/{workspaceId}/lakehouses/{lakehouseId}/mlvexecutiondefinitions

{
  "displayName": "Gold Chain - Sales",
  "description": "Nightly refresh for selected gold-layer MLVs",
  "settings": {
    "environment": {
      "referenceType": "ById",
      "itemId": "<environmentId>",
      "workspaceId": "<environmentWorkspaceId>"
    },
    "refreshMode": "Optimal"
  },
  "currentLakehouseExecutionContext": {
    "mode": "Selected",
    "selectedMlvs": [
      "dbo.gold_sales_summary",
      "dbo.gold_sales_daily"
    ]
  },
  "extendedLineageExecutionContext": {
    "mode": "All"
  }
}

Execution context modes:

• currentLakehouseExecutionContext.mode: "All" or "Selected"; when "Selected", provide selectedMlvs as fully qualified MLV names.

• extendedLineageExecutionContext.mode: "All" or "Selected"; when "Selected", provide selectedLakehouses objects with referenceType, itemId, and workspaceId.

• settings.refreshMode: "Optimal" or "Full".

• settings.environment: Optional Spark environment reference by ID.

Update semantics: PATCH /mlvexecutiondefinitions/{id} is a partial update. Only provided fields change; omitted fields retain their existing values. This differs from schedule PATCH, which requires enabled and a complete configuration.

Delete semantics: DELETE /mlvexecutiondefinitions/{id} also removes schedules linked to that execution definition. Warn before deleting.

Trigger On-Demand Refresh (POST /instances)

Endpoint: POST /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/instances

Request body: None for full-lineage refresh. Refreshes the entire MLV lineage in dependency order.

Subset refresh body:

{
  "executionData": {
    "mlvExecutionDefinitionId": "<mlvExecutionDefinitionId>"
  }
}

Do not use job history as the trigger contract: recent runs may list jobType: "MaterializedLakeViews", but that value is only a history/status label. For on-demand refresh, always call this lakehouse-scoped endpoint. Reusing the history label with the generic item job API (POST /workspaces/{workspaceId}/items/{lakehouseId}/jobs/instances?jobType=MaterializedLakeViews) is a known InvalidJobType dead end.

The trigger response returns an item-scoped Location URL (/items/{lakehouseId}/jobs/instances/{jobInstanceId}). Use it as-is for polling and cancellation. The lakehouse-scoped GET (/lakehouses/{lakehouseId}/jobs/instances/{jobInstanceId}) is documented as an alternative and returns the same instance, but the service does not return that shape in the Location header today.

Response: 202 Accepted — job instance ID is in the Location response header:

Location: https://api.fabric.microsoft.com/v1/workspaces/{wsId}/items/{lakehouseId}/jobs/instances/{jobInstanceId}
Retry-After: 60

Poll for status using the URL from the Location header (or its lakehouse-scoped equivalent):

GET /workspaces/{workspaceId}/items/{lakehouseId}/jobs/instances/{jobInstanceId}

Job instance status values:

Note: Job instances returned by public GET /lakehouses/{id}/jobs/refreshMaterializedLakeViews/instances use jobType: "RefreshMaterializedLakeViews" per MS Learn. Some item-level history surfaces have returned jobType: "MaterializedLakeViews" in live testing; filter on either value when diagnosing legacy history, but never use MaterializedLakeViews as an on-demand trigger jobType.

Status display limitation: job instance status reflects Monitor hub status and can differ from the MLV run-history UI. For example, an MLV run-history Skipped status can appear as Cancelled/Canceled in Monitor hub APIs.

Schedule settings (additional options via UI or API):

• Optimal Refresh (default: On) — Fabric picks incremental or full refresh per MLV automatically

• Extended lineage — refreshes chains across multiple lakehouses in dependency order from a single schedule

Example Workflows

Workflow 1: Schedule Nightly Refresh

User prompt: "Schedule sales_monthly MLV in CustomerVoice lakehouse to refresh at 2 AM daily"

Agent steps:

1. Find workspace ID for "CustomerVoice" via GET /workspaces + JMESPath

2. Find lakehouse ID via GET /workspaces/{id}/lakehouses + JMESPath

3. Clarify scope: "Do you want the default full-lineage schedule, or should I create an MLV execution definition for only sales_monthly?"

4. If the user chooses subset scope, create an execution definition with currentLakehouseExecutionContext.mode = "Selected" and selectedMlvs = ["dbo.sales_monthly"] (or the user's actual fully qualified MLV name), then include its mlvExecutionDefinitionId in schedule executionData.

5. Show preview:

   Copy

   Creating schedule:
     Lakehouse: CustomerVoice (ID: xyz-456-ghi)
     Scope: Full lineage OR execution definition <id>
     Type: Daily, Time: 02:00 UTC
     Start: Now
     End: 1 year from now
   
   Proceed? [Allow / Decline]

6. On "Allow": POST schedule payload, return schedule ID

7. Report: "Schedule created (ID: sched-789). Scope: full lineage OR execution definition <id>."

Workflow 2: Trigger Immediate Refresh

User prompt: "Refresh inventory_daily MLV now in lakehouse xyz-456-ghi"

Agent steps:

1. Confirm lakehouse ID is valid (GET /lakehouses/{id})

2. Clarify scope and show preview:

   Copy

   Triggering on-demand refresh:
     Lakehouse ID: xyz-456-ghi
     Scope: Full lineage OR execution definition <id>
   
   Do you want the default full-lineage refresh, or only `inventory_daily` through an execution definition? Proceed? [Allow / Decline]

3. If the user chooses a subset, create or reuse an execution definition for inventory_daily and send its ID in executionData; otherwise POST /instances with no body. Capture the job ID from the Location header.

4. Poll using the item-scoped URL returned in the Location header, or its documented lakehouse-scoped equivalent GET /workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/instances/{jobId}, and show progress.

5. Report: "✅ Refresh completed in 2m 34s" or "❌ Refresh failed: [error message]"

Workflow 3: User Asks for Per-MLV Scheduling (Explain Limitation)

User prompt: "Schedule bronze_sales, silver_sales, gold_sales to refresh at 2 AM, 3 AM, 4 AM respectively"

Agent steps:

1. Explain limitation:

   Copy

   These views are a dependency chain, so schedule them as one lineage refresh
   rather than at separate times. Lakehouse scheduling refreshes dependencies in
   the required order.
   
   Options:
     1. Single Daily schedule at 2 AM (refreshes all 3 in order)
     2. If the MLVs are independent, create separate execution definitions and
        schedules within the lakehouse's 20-schedule limit
     3. If they're in separate lakehouses, create one schedule per lakehouse
   
   Which would you prefer?

2. If user picks option 1: Create one Daily schedule with "times": ["02:00"]

3. If user picks option 2: Confirm the MLVs are independent, then create the execution definitions and schedules

4. If user picks option 3: Confirm lakehouse IDs for each, then create schedules

Workflow 4: List and Delete Schedules

User prompt: "Show my schedule and delete it if it's outdated"

Agent steps:

1. Ask user for workspace and lakehouse (cannot list schedules globally)

2. GET /workspaces/{wsId}/lakehouses/{lhId}/jobs/refreshMaterializedLakeViews/schedules

3. Parse response (usually 1 schedule per lineage), show details:

   Copy

   Active schedule on lakehouse 'CustomerVoice':
     - ID: sched-111 (created 2025-10-15, type: Daily, time: 02:00)
   
   Delete this schedule? [Allow / Decline]

4. On "Allow": DELETE /schedules/{id}, confirm

5. Report: "✅ Schedule deleted"

Common Errors & Fixes

Error: 400 Bad Request — Invalid Interval

Message: "The interval '0' is invalid. Interval must be between 1 and 5270400 (10 years in minutes)."

Fix:

Interval must be a valid number of minutes between 1 and 5,270,400 (10 years).

Common intervals:
  60 = hourly
  1440 = daily (24 hours)
  10080 = weekly (7 days)

Would you like me to adjust the interval to a valid value?

Error: 409 Conflict — Schedule Already Exists

Message: "A schedule already exists for this lakehouse"

Fix:

A schedule is already active for this lakehouse. Options:
  1. Update existing schedule (change interval/time)
  2. Delete and recreate (replaces schedule)
  3. Leave as-is (no change)

Which would you prefer?

Error: 404 Not Found — Lakehouse ID Invalid

Message: "Lakehouse 'wrong-id-123' not found in workspace 'abc-456'"

Fix:

The lakehouse ID you provided doesn't exist. Let me list available lakehouses:

[Call GET /workspaces/{id}/lakehouses, show table]

Which lakehouse should I use?

Error: 403 Forbidden — Permission Denied

Message: "User does not have permission to create schedules in this workspace"

Fix:

You need Workspace Contributor or Admin role to create schedules.

Current permissions: Viewer (read-only)
Required: Contributor or Admin

Contact your workspace admin to request elevated permissions.

Tips for Users

1. Find Your Lakehouse ID

Option A: Via REST API

az rest --resource https://api.fabric.microsoft.com \
  --url "https://api.fabric.microsoft.com/v1/workspaces/{workspaceId}/lakehouses" \
  --method GET

Extract id from the response for your lakehouse.

Option B: Via Fabric UI

1. Open lakehouse in Fabric portal

2. Click Settings (gear icon)

3. Copy "Lakehouse ID" from properties

2. Common Schedule Configurations

3. Monitor Job History

List recent refresh jobs (authenticate per COMMON-CLI.md § Quick Reference: az rest Template):

# See COMMON-CLI.md for authentication setup
az rest --resource https://api.fabric.microsoft.com \
  --url "https://api.fabric.microsoft.com/v1/workspaces/{workspaceId}/lakehouses/{lakehouseId}/jobs/refreshMaterializedLakeViews/instances" \
  --method GET

Note: The list instances API does not support OData query parameters ($top, $orderby, $filter). Sort and filter results client-side after retrieval. Use continuationToken for pagination.

4. Time Zone Considerations

Default: Schedules use UTC unless specified.

Best practice: Always specify timezone explicitly to avoid confusion:

{
  "configuration": {
    "localTimeZoneId": "Central Standard Time"
  }
}

Valid time zones: Windows time zone names (e.g., "Central Standard Time", "Pacific Standard Time", "India Standard Time"). Use the Windows Default Time Zones registry.

Related Skills

• the authoring mode: Create MLVs in Fabric Notebooks (authoring side)

Limitations & Future Roadmap

Current Limitations (as of 2026-06-18)

What Works Today (public MLV API coverage (Preview))

• ✅ Create/list/update/delete schedules (5 APIs)

• ✅ Trigger/monitor/cancel refresh jobs (4 APIs)

• ✅ Create/list/get/update/delete MLV execution definitions (5 APIs)

• ✅ Full-lineage or selected-lineage refresh via executionData.mlvExecutionDefinitionId

• ✅ Full automation of refresh workflows

• ✅ Human-in-the-loop safety confirmations

• ✅ Iterative error handling

Planned (When REST APIs Ship)

• REST MLV Discovery: Auto-list MLVs without a Spark session

• Visual Lineage Tracing: Show dependency graphs

• Data Quality: Fetch DQ metrics programmatically

• Schema Verification: Check enableSchemas property

Agent design is forward-compatible: When APIs become available, add discovery capabilities without changing scheduling logic.

Conclusion

This skill provides validated automation for MLV refresh scheduling, execution definitions, and monitoring using public REST APIs. While MLV discovery and visual lineage inspection still require UI workarounds today, scheduling, selected-lineage execution definitions, and job execution work as documented.

Design philosophy (inspired by Databricks Data Engineering Agent):

• Human-in-the-loop confirmations for safety

• Step-by-step planning for complex tasks

• Iterative error handling with helpful suggestions

• Transparent about limitations (no speculative workarounds)

Next steps: Use this skill to automate MLV refresh workflows. When discovery APIs ship, we'll extend the skill to eliminate manual lakehouse ID + table name input.