1. Overloading Transformation Logic A common pitfall is embedding too much business logic directly into the ETL pipeline, making it rigid and hard to maintain. For example, if transformations rely on hardcoded rules (e.g., filtering specific customer IDs or calculating values using fixed formulas), changes to business requirements force developers to rewrite large portions of the pipeline. This also complicates testing, as transformations are tightly coupled to extraction and loading steps. A better approach is to separate transformation rules into configurable modules or external lookup tables. For instance, using parameterized SQL scripts or storing rules in a database allows updates without redeploying the entire ETL process.
2. Neglecting Incremental Data Processing ETL pipelines that reprocess entire datasets instead of handling only new or changed data waste resources and slow down performance. For example, reloading a 1TB table daily when only 1% of rows change consumes unnecessary compute time and storage. This becomes unsustainable as data scales. Implementing incremental loads—using timestamps, change data capture (CDC), or delta flags—reduces processing overhead. Tools like Apache Kafka for streaming changes or database triggers to track updates help identify deltas. However, this requires careful design to avoid missing data (e.g., ensuring all changes are captured) and handling late-arriving data.
3. Poor Error Handling and Monitoring Failing to plan for failures leads to incomplete data loads and manual recovery efforts. For instance, if a network outage disrupts data extraction, the pipeline might halt without logging the error, leaving developers unaware of missing data. Robust ETL systems should include retries for transient errors, dead-letter queues for unprocessable records, and detailed logging (e.g., which records failed and why). Tools like Airflow or AWS Glue provide built-in retry mechanisms, while custom solutions might use checkpoints to resume from the last successful step. Without monitoring dashboards to track job durations, row counts, or data quality metrics, issues like performance degradation or silent data corruption go undetected until downstream systems fail.