ploosh.
Documentation
Fabric reporting
By persisting Ploosh test results into a Delta table (see Fabric notebook), you can build Power BI reports to monitor data quality over time.
Semantic model
Create a Semantic Model on top of the ploosh_results Delta table in your Lakehouse:
- In your Ploosh workspace, click New → Semantic model
- Select the
ploosh_resultstable from the Lakehouse - Define a date hierarchy on
execution_datefor time-based analysis - Publish the model
Suggested measures
| Measure | DAX formula |
|---|---|
| Total tests | COUNTROWS('plooshresults') |
| Passed tests | CALCULATE(COUNTROWS('plooshresults'), 'plooshresults'[state] = "passed") |
| Failed tests | CALCULATE(COUNTROWS('plooshresults'), 'plooshresults'[state] = "failed") |
| Error tests | CALCULATE(COUNTROWS('plooshresults'), 'plooshresults'[state] = "error") |
| Pass rate | DIVIDE([Passed tests], [Total tests], 0) |
| Avg source duration | AVERAGE('plooshresults'[sourceduration]) |
| Avg compare duration | AVERAGE('plooshresults'[compare_duration]) |
Dashboard layout
A typical data quality dashboard includes:
Overview page
- KPI cards: Total tests, pass rate, failed count, error count
- Trend chart: Pass rate over time (by
execution_date) - Table: Latest execution results with state, duration, and error details
Detail page
- Filter by test name: Drill into a specific test case history
- Duration chart: Source/expected/compare durations over time
- Error breakdown: Error types distribution (headers, count, data, compare)
Operational page
- Execution timeline: Gantt-style view of test execution times
- Success rate heatmap: Success rates by test case and date
- Alert list: Tests with success rate below threshold
Alerting
Use Power BI data alerts or Fabric Data Activator to trigger notifications when:
- A test case fails for the first time
- The overall pass rate drops below a threshold
- A test case duration exceeds a limit (potential performance regression)