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4.2.2. Dataflow and Notebook Errors
💡 First Principle: Dataflow errors typically stem from data quality or connection issues. Notebook errors range from code bugs to resource exhaustion. Each requires different troubleshooting approaches.
Common Dataflow Errors
| Error Type | Cause | Resolution |
|---|---|---|
| Connection failed | Credentials expired, network issue | Refresh connection credentials |
| Type mismatch | Source data type changed | Update data type mapping |
| Query timeout | Source system slow or large dataset | Optimize query or paginate |
Common Notebook Errors
| Error Type | Cause | Resolution |
|---|---|---|
| Out of memory | Dataset too large for executor | Increase executor memory, reduce data |
| Schema mismatch | Expected columns missing | Validate schema before processing |
| Permission denied | Insufficient access to data | Check workspace and item permissions |
Written byAlvin Varughese
Founder•15 professional certifications