refresh-semantic-model

name	refresh-semantic-model
version	26.25
description	Automatically invoke this skill whenever the user asks to refresh a semantic model or a dataset. Can also be used to manage, optimize, troubleshoot, or configure a refresh or a refresh schedule.

Refreshing Semantic Models

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

Core Concepts

A semantic model refresh reloads data from upstream sources and/or recalculates dependent objects (calculated columns, calculated tables, measures). The scope can be the entire model, specific tables, or individual partitions.

Six refresh types are available via the REST API; a seventh (add) is TMSL-only:

Type	Reloads Data	Recalculates	Primary Use Case	API
full	Yes	Yes	Complete reload from scratch	REST
automatic	Conditional	Conditional	Smart refresh; process only if needed	REST
dataOnly	Yes	No*	Reload data; clear dependents	REST
calculate	No	Yes	Recalculate without reloading data	REST
clearValues	No	No	Empty data from objects	REST
defragment	No	No	Clean up column dictionaries	REST
add	Append	Yes	Append rows to a partition	TMSL

*dataOnly clears dependent objects (calculated columns, calculated tables) but does not recalculate them. Follow with a calculate refresh to restore them.

For detailed descriptions, behavior with incremental refresh policies, commit modes, and parallelism options, consult references/refresh-types.md.

Refresh Workflow

Step 1: Resolve IDs

Extract the workspace and model GUIDs needed for API calls:

WS_ID=$(fab get "WorkspaceName.Workspace" -q "id" | tr -d '"')
MODEL_ID=$(fab get "WorkspaceName.Workspace/ModelName.SemanticModel" -q "id" | tr -d '"')

Step 2: Query Baseline Data (Pre-Refresh Validation)

Before triggering the refresh, capture a baseline snapshot to later verify that data actually changed. Execute a DAX query against the model to get current row counts or max dates:

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/executeQueries" \
  -X post -i '{
    "queries": [{"query": "EVALUATE ROW(\"RowCount\", COUNTROWS(FactSales), \"MaxDate\", MAX(FactSales[OrderDate]))"}],
    "serializerSettings": {"includeNulls": true}
  }'

Record the output. This baseline is compared after refresh to confirm new data arrived.

Step 3: Trigger the Refresh

Full model refresh (simplest):

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{"type":"full"}'

Refresh specific tables:

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{
    "type": "full",
    "objects": [{"table": "FactSales"}, {"table": "DimProduct"}]
  }'

Refresh specific partitions:

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{
    "type": "full",
    "objects": [
      {"table": "FactSales", "partition": "FactSales_2024"},
      {"table": "FactSales", "partition": "FactSales_2023"}
    ]
  }'

Data-only refresh (skip recalculation):

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{"type":"dataOnly","objects":[{"table":"FactSales"}]}'

Calculate only (no data reload):

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{"type":"calculate"}'

Clear values from a table:

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{"type":"clearValues","objects":[{"table":"StagingTable"}]}'

For the script-based approach with CLI arguments, use scripts/refresh_model.py.

Step 4: Monitor Status

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes?\$top=1"

Status values: Unknown, InProgress, Completed, Failed, Disabled, Cancelled

Step 5: Post-Refresh Validation

After the refresh completes, re-run the same DAX query from Step 2 and compare:

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/executeQueries" \
  -X post -i '{
    "queries": [{"query": "EVALUATE ROW(\"RowCount\", COUNTROWS(FactSales), \"MaxDate\", MAX(FactSales[OrderDate]))"}],
    "serializerSettings": {"includeNulls": true}
  }'

If data has not changed after a successful refresh:

• The upstream data source has not been updated
• The ETL pipeline (Fabric pipeline, notebook, Data Factory, or other orchestration) needs to run first
• Check the lakehouse/warehouse/SQL database to verify fresh data exists
• For Fabric lakehouses: run fab run "Workspace.Workspace/Pipeline.DataPipeline" or trigger the notebook
• The refresh only pulls what the source provides; if the source is stale, the refresh will succeed but show no new data

Step 6: Cancel (if needed)

To cancel an in-progress enhanced refresh, first retrieve the requestId from the refresh history:

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes?\$top=1"

The response includes a requestId field. Use it to cancel:

fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes/<requestId>" \
  -X delete

Only works for refreshes triggered via the Enhanced API (not scheduled or portal refreshes).

Using the Refresh Script

The scripts/refresh_model.py script wraps the Enhanced Refresh API with CLI arguments:

# Full refresh
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID

# Refresh specific tables
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID --tables Sales,Calendar

# Refresh specific partitions
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID --partitions Sales:Sales_2024

# Data-only then calculate (two-phase)
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID -t dataOnly --tables FactSales
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID -t calculate

# Partial batch with parallelism
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID --commit partialBatch --parallelism 4

# Skip incremental refresh policy
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID --no-policy

# Check status only
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID --status-only

# Poll until complete
uv run scripts/refresh_model.py -w $WS_ID -m $MODEL_ID --poll

Enhanced Refresh Options

The Enhanced Refresh API (Premium/Fabric capacity required) extends the standard refresh with:

Parameter	Default	Purpose
type	automatic	Refresh type (full, automatic, dataOnly, calculate, etc.)
commitMode	transactional	Atomic commit or per-object partial batch
maxParallelism	10	Number of parallel processing threads
retryCount	0	Automatic retries on failure
objects	Entire model	Array of table/partition targets
applyRefreshPolicy	true	Apply or skip incremental refresh policy
effectiveDate	Current date	Override date for incremental policy window
timeout	05:00:00	Per-attempt timeout (max total 24h with retries)

Common Patterns

Two-Phase Refresh (Large Models)

Split data loading and recalculation for better control and failure isolation:

1. dataOnly with partialBatch to reload all tables (each committed independently)
2. calculate with transactional to recalculate everything atomically

Selective Partition Refresh

For tables with incremental refresh, refresh only specific time-range partitions rather than the entire table. To discover partition names, query the model's TMSL metadata via the XMLA endpoint using Tabular Editor, SSMS, or by exporting with the Fabric CLI:

fab export "Workspace.Workspace/Model.SemanticModel" -o /tmp/model -f

Inspect the exported TMDL table files; each partition block lists the partition name. Target specific partitions in the objects array of the refresh request.

Refresh After ETL

When orchestrating a data pipeline:

1. Run the upstream ETL (Fabric pipeline, notebook, ADF, or custom)
2. Verify fresh data in the source (lakehouse, warehouse, SQL)
3. Trigger the semantic model refresh
4. Validate with a DAX query that row counts or max dates changed
5. If unchanged, investigate the ETL output; the semantic model refresh succeeded but the source was stale

Troubleshooting

Quick reference for the most common failures. For the full troubleshooting guide with debugging workflows and detailed error tables, read references/troubleshooting.md.

Symptom	Likely Cause	Resolution
Failed with credential error	Credentials expired, missing, or didn't carry over after copy	Update in dataset settings; only shared cloud connections transfer with fab cp
Type mismatch on a table	Source column types don't match model column types	Check column data types in the model definition vs source schema; add Table.TransformColumnTypes in partition expression
Column does not exist	Source column renamed, removed, or differently cased	Check source schema; add Table.RenameColumns in partition expression
Timeout (2h shared / 5h Premium)	Model too large for a single refresh window	Implement incremental refresh; use partition-level refresh via XMLA; reduce model size
Calculated tables empty	dataOnly refresh clears but doesn't rebuild	Follow with a calculate refresh via the Enhanced Refresh API to rebuild calculated tables and calc groups
Throttled on Premium	Too many concurrent refreshes	Stagger refresh schedules; refresh during off-peak

Debugging per-table failures

When a full refresh fails, isolate the failing table by refreshing individual tables via the Enhanced Refresh API:

# Refresh dimensions first
fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{"type":"full","objects":[{"table":"Customers"}]}'

# Then facts
fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{"type":"full","objects":[{"table":"Invoices"}]}'

# Then recalculate
fab api -A powerbi "groups/$WS_ID/datasets/$MODEL_ID/refreshes" \
  -X post -i '{"type":"calculate"}'

Check the failing table's partition expression and compare source schema against the model's expected column names and types via fab export or fab table schema.

Large Model Strategies

Models over 1 GB or with refresh times exceeding an hour benefit from targeted approaches:

• Partition-level refresh: Refresh individual table partitions via the enhanced REST API or XMLA endpoint instead of the full model. Requires Premium/Fabric capacity.
• Incremental refresh: Automatically partition large tables by date; only recent data refreshes each cycle. Configure RangeStart/RangeEnd parameters in Power Query. Also supports detect-data-changes to skip unchanged partitions entirely.
• Aggregations: Pre-aggregate large fact tables at a coarser grain into an import-mode aggregation table. Detail queries fall through to DirectQuery. Reduces both refresh time and memory.
• Hybrid tables: Historical partitions in import mode; a real-time DirectQuery partition for recent data. Related tables must be Dual storage mode.
• Scale-out: Isolate refresh from query workloads by enabling semantic model scale-out on Premium capacities. A read-only replica handles queries while the primary refreshes.

Capacity Limits

Capacity Type	Max Refreshes/Day	Default Timeout	Enhanced Features
Pro	8	2 hours	No
Premium Per User	48	5 hours	Yes
Premium / Fabric	48	5 hours	Yes

Pro capacity supports only full-model standard refreshes. Enhanced refresh features (table/partition targeting, commit modes, parallelism, cancel, timeout override) require Premium or Fabric capacity.

Requirements

• Workspace contributor or higher permissions
• fab CLI authenticated: fab auth login

Additional Resources

Reference Files

• references/refresh-types.md -- Complete reference for all 7 refresh types, commit modes, parallelism, incremental policy interaction, status values, XMLA/TMSL details, and the two-phase refresh pattern
• references/troubleshooting.md -- Comprehensive troubleshooting guide: credential errors, type/schema mismatches, timeouts, capacity limits, incremental refresh issues, debugging workflows, and large model strategies

Scripts

• scripts/refresh_model.py -- CLI tool for triggering and monitoring refreshes with all enhanced options

Related Skills

• semantic-model -- Model design, build, and quality/performance review
• lineage-analysis -- Downstream report discovery and impact analysis
• standardize-naming-conventions -- Naming audit and remediation
• fabric-cli (fabric-cli plugin) -- Workspace and item management via fab CLI