What are the most effective ETL error handling patterns for your environment?

1 Pattern 1: Reject and log

This pattern is suitable for ETL environments where data validation is performed before loading and the errors are not critical for the business. The idea is to reject and log the records that fail the validation rules, such as missing values, invalid formats, or duplicates. The rejected records are stored in a separate table or file, along with the error details and timestamps. This allows the ETL developers or data analysts to review and fix the errors later, without affecting the rest of the data load.

2 Pattern 2: Retry and notify

This pattern is suitable for ETL environments where data loading is dependent on external factors, such as network connectivity, API availability, or system performance. The idea is to retry the data load in case of transient errors, such as timeouts, connection failures, or server errors. The number and frequency of retries can be configured based on the expected recovery time and the data urgency. If the retries fail, the ETL process should notify the relevant stakeholders, such as the ETL developers or the data owners, via email, SMS, or other channels. This allows them to investigate and resolve the issues as soon as possible.

4 Pattern 4: Divide and conquer

This pattern is suitable for ETL environments where data volume and complexity are high and the errors are heterogeneous and unpredictable. The idea is to divide the data load into smaller batches or chunks, based on some criteria, such as source, date, category, or key. Each batch or chunk is processed independently and in parallel, using one of the previous patterns. This allows the ETL process to isolate and handle the errors more efficiently and effectively, without affecting the entire data load. It also improves the scalability and performance of the ETL process, by reducing the memory and resource consumption.