Data Quality
The Data Quality monitor helps you track the health of your data ingestion processes and catch problems early.
Quality Dashboard
Summary Cards
| Metric | What It Means |
|---|---|
| Success Rate | Percentage of successful ingestion jobs in the last 100 runs |
| Rows Ingested | Total rows successfully imported across all jobs |
| Failures | Number of failed ingestion jobs |
| Jobs Run | Total number of ingestion jobs executed |
Ingestion History
A detailed table of all ingestion jobs with:
| Column | Description |
|---|---|
| Status | ✅ Success, ⚠️ Warning (partial), ❌ Failed |
| File/Source | Source file name or pipeline connection |
| Started At | When the job began |
| Duration | How long the job took |
| Rows | Number of rows processed |
| Transferred | Data volume transferred |
| Info | Additional notes or error messages |
The history updates in real-time — a pulsing indicator shows when new data is being loaded.
Interpreting Quality Issues
Common Problems and Solutions
| Problem | Possible Cause | Solution |
|---|---|---|
| Type mismatch | Column contains mixed types (e.g., numbers and text) | Clean the source data or use a transformation |
| Null key column | Primary key column has empty values | Remove empty rows or choose a different key |
| Schema drift | Source schema changed (new/removed columns) | Re-configure the pipeline or update the dataset |
| Connection timeout | Database connection dropped | Check network/firewall settings and retry |
| File format error | Corrupted or incorrectly formatted file | Verify the file opens correctly in Excel |
info
Tip: A healthy workspace maintains a success rate above 95%. If you see many failures, check your pipeline connections and source data quality.