Common ETL errors typically occur during data extraction, transformation, or loading phases. Diagnosing them requires systematic checks at each stage. Below are frequent issues and their diagnosis methods.
Common Errors
- Extraction Errors: These include connectivity failures (e.g., database timeouts), schema mismatches (e.g., missing columns), or corrupted source files. For example, an API might return malformed JSON, or a CSV file might have unexpected delimiters.
- Transformation Errors: Data type mismatches (e.g., converting strings to dates), null handling issues, or incorrect business logic (e.g., wrong aggregations) are common. A date field formatted as
MM/DD/YYYYinstead ofYYYY-MM-DDcould break transformations. - Loading Errors: Primary key violations, duplicate records, or disk space exhaustion during database inserts are frequent. A table with a unique constraint might reject rows with duplicate keys.
Diagnosis Methods
For extraction errors, verify connectivity (e.g., ping servers, test credentials) and validate source schemas. Tools like preview functions in ETL tools or scripts to sample data can catch format mismatches. For transformation errors, implement logging at each step to trace invalid records. Use data profiling (e.g., checking null rates, distinct values) or unit tests for business rules. For loading errors, check database logs for constraint violations or permission issues. Run pre-load checks (e.g., counting rows to detect duplicates).
Tools and Practices Use logging frameworks (e.g., ELK Stack) to track errors across stages. Automated validation tools like Great Expectations or custom scripts can verify data quality. For example, a script could compare row counts before and after transformation to detect data loss. Monitoring tools (e.g., Prometheus) help identify performance bottlenecks like slow queries. Debugging pipelines in smaller batches or using breakpoints in tools like Apache NiFi can isolate issues. Regularly testing pipelines with sample datasets ensures errors are caught early.