Refresh Semantic Model

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:

*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:

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