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InsightfulAnalytics
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refresh-semantic-model

Trigger, monitor, and troubleshoot Power BI semantic model refreshes via the Enhanced Refresh REST API.

6|3|Updated Jul 4, 2026
Search Tags:#troubleshooting#rest api#refresh#power bi#semantic model#fabric cli#incremental refresh
One-click install
npx skills add https://github.com/InsightfulAnalytics/PBI_Agentic_Dev --skill refresh-semantic-model-insightfulanalytics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: refresh-semantic-model
Source: https://github.com/InsightfulAnalytics/PBI_Agentic_Dev/tree/main/plugins/semantic-models/skills/refresh-semantic-model
Command: npx skills add https://github.com/InsightfulAnalytics/PBI_Agentic_Dev --skill refresh-semantic-model-insightfulanalytics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Refreshing Power BI semantic models reliably requires coordinating the Enhanced Refresh REST API, validating that data actually changed, and diagnosing failures like credential errors, schema mismatches, and timeouts. This Skill automates that entire workflow so refreshes are triggered, monitored, and verified without manual portal work. ## Core Features & Use Cases - Full Refresh Lifecycle: Resolve workspace and model IDs, capture a pre-refresh DAX baseline, trigger refreshes (full, automatic, dataOnly, calculate, clearValues, defragment), monitor status, and validate post-refresh data changes. - Targeted and Large-Model Refreshes: Refresh specific tables or partitions, run two-phase dataOnly + calculate patterns, control commit modes, parallelism, retries, and incremental refresh policy behavior. - Troubleshooting Guidance: Diagnose credential errors, gateway-free Fabric source refresh via workspace identity and shareable cloud connections, type mismatches, timeouts, throttling, and incremental refresh issues. - Use Case: After an ETL pipeline loads new data into a Fabric lakehouse, trigger a partition-level refresh of FactSales, poll until completion, and confirm via DAX that row counts and max dates increased. ## Quick Start Ask the agent to refresh the semantic model in a given Power BI workspace and verify that new data arrived.

Frequently Asked Questions about refresh-semantic-model

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

FAQPage Schema
How do I refresh a Power BI semantic model via the REST API?▼

Trigger a refresh by POSTing to the groups/{workspaceId}/datasets/{modelId}/refreshes endpoint with a body specifying the type, such as full or dataOnly. The included refresh_model.py script wraps this call with CLI arguments for tables, partitions, commit mode, and polling.

How to refresh specific tables or partitions in Power BI?▼

Use the Enhanced Refresh API objects array to target specific tables or table/partition pairs in the refresh request body. This requires Premium or Fabric capacity; Pro workspaces only support full-model standard refreshes.

What is the difference between dataOnly and calculate refresh types?▼

A dataOnly refresh reloads data from sources but clears dependent calculated columns and tables without rebuilding them. A calculate refresh recalculates those dependent objects without reloading data, so the two are often chained in a two-phase refresh.

Why does my semantic model refresh succeed but show no new data?▼

The refresh only pulls what the upstream source provides, so a stale lakehouse, warehouse, or SQL source means no new rows arrive. Run the upstream ETL pipeline first, verify fresh data at the source, then re-trigger the refresh and compare DAX baseline queries.

Can I refresh a model over a Fabric source without a gateway?▼

Yes, by provisioning a workspace identity, granting it a workspace role, creating a shareable cloud connection backed by that identity, and binding the model to it. This requires an F SKU or trial capacity and is documented in the troubleshooting reference.

Why did my refresh fail with a credential or permission error?▼

Common causes include expired OAuth tokens, credentials not carrying over after copying a model, or a workspace identity missing an explicit workspace role assignment. The troubleshooting reference maps each error message to its resolution steps.

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