Common ETL Bugs and How to Find Them
Common ETL Bugs and How to Find Them
ETL (Extract, Transform, Load) pipelines are the backbone of data workflows, but they're often prone to bugs due to the complexity and volume of data involved. Here's a breakdown of common ETL bugs and how to detect and prevent them:
🔍 Common ETL Bugs
1. Data Loss
Symptoms: Missing records in the destination table.
Causes: Truncating tables before confirming successful data load, bad JOINs or filters.
How to Find:
Compare row counts (source vs. destination).
Use checksums or hash totals to verify data integrity.
Monitor log files for failed inserts or timeouts.
2. Data Duplication
Symptoms: Duplicate rows or inflated metrics in the target.
Causes: Poor deduplication logic, accidental multiple runs of the same load process.
How to Find:
Use primary key or unique constraint checks.
Write SQL queries to detect identical rows.
Audit logs for reruns or overlaps in batch IDs.
3. Incorrect Data Transformation
Symptoms: Inaccurate values or mismatched formats (e.g., wrong currency conversion, broken date fields).
Causes: Bugs in transformation logic, bad regex or mapping errors.
How to Find:
Validate against business rules or test data.
Unit test transformation logic.
Use data profiling tools to scan anomalies.
4. Schema Drift / Incompatible Schema Changes
Symptoms: Pipeline crashes or loads partial data.
Causes: Source table structure changes (added/removed columns).
How to Find:
Set up schema comparison checks.
Automate alerts for schema drift using tools like dbt, Great Expectations, or custom scripts.
5. Slow or Failing Jobs
Symptoms: Long run times, missed SLAs.
Causes: Inefficient queries, large joins, missing indexes.
How to Find:
Monitor job duration with timestamps and metrics.
Analyze execution plans for slow queries.
Log resource usage (memory, CPU).
6. Timezone and Timestamp Errors
Symptoms: Misaligned time-based data (e.g., duplicate records, incorrect windowing).
Causes: Inconsistent time zone handling in source vs. target.
How to Find:
Check timestamp conversions explicitly.
Standardize to UTC during ETL, then localize as needed.
7. Broken Dependencies / Job Failures
Symptoms: Downstream jobs fail due to incomplete upstream jobs.
Causes: Job orchestration issues or silent job failures.
How to Find:
Use workflow managers (e.g., Airflow, Prefect) with alerting and retries.
Implement "heartbeat" checks or status flags in a control table.
8. Hard-Coded Values or Logic
Symptoms: Pipeline breaks when configurations change (e.g., hard-coded file paths, years, schema names).
Causes: Poor configuration management.
How to Find:
Conduct code reviews.
Externalize configs to YAML, ENV vars, or a metadata store.
✅ Best Practices for Detecting ETL Bugs
Strategy Description
Row Counts & Checksums Compare counts and hash summaries before/after load.
Automated Testing Unit tests for transformations, integration tests for end-to-end pipelines.
Data Quality Tools Use Great Expectations, Deequ, or Soda for rule-based validation.
Logging & Monitoring Structured logs, metrics, and alerts for failures or anomalies.
Orchestration Tools like Airflow, Dagster, or Prefect to ensure proper task ordering and retries.
Observability Dashboards Visualize ETL performance, freshness, and errors. Use tools like Monte Carlo, Datafold, or Metaplane.
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Read More
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Step-by-Step Guide to Writing ETL Test Cases
Why ETL Testing is Crucial in Data Warehousing
How to Get Started with ETL Testing: Tools, Skills, and Roadmap
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