What are the best ETL error handling practices?

1 Logging errors

Logging errors is the first step to identify, troubleshoot, and resolve any issues that occur during the ETL process. Logging errors means capturing relevant information about the error, such as the source, the type, the location, the time, and the impact. You should use a consistent and standardized format for logging errors, and store them in a central and accessible location, such as a database or a file system. You should also use a logging tool or framework that supports different levels of severity, filtering, and aggregation of errors.

2 Validating data

Validating data is the second step to prevent or minimize errors in the ETL process. Validating data means checking the data for accuracy, completeness, consistency, and compliance with the business rules and the target system specifications. You should perform data validation at different stages of the ETL process, such as before extraction, after transformation, and before loading. You should also use a validation tool or framework that supports different types of validation, such as schema, format, range, uniqueness, and referential integrity.

3 Recovering from errors

Recovering from errors is the third step to ensure the continuity and integrity of the ETL process. Recovering from errors means taking appropriate actions to handle the errors, such as retrying, skipping, aborting, or fixing the errors. You should design your ETL process with error recovery in mind, and use a recovery tool or framework that supports different strategies, such as checkpointing, rollback, compensation, and contingency. You should also test your recovery scenarios and document your recovery procedures.

4 Notifying stakeholders

Notifying stakeholders is the fourth step to communicate the status and the impact of the errors in the ETL process. Notifying stakeholders means sending alerts or reports to the relevant parties, such as the ETL developers, the data analysts, the data consumers, or the data owners. You should use a notification tool or framework that supports different channels, such as email, SMS, or dashboard. You should also use a notification tool or framework that supports different frequencies, such as real-time, daily, or weekly.

5 Automating error handling

Automating error handling is the fifth step to streamline and optimize the ETL process. Automating error handling means using a tool or framework that can perform the error handling tasks automatically, such as logging, validating, recovering, and notifying. You should use a tool or framework that can integrate with your ETL platform and your data sources and targets. You should also use a tool or framework that can customize your error handling logic and rules.

6 Monitoring error handling

Monitoring error handling is the sixth step to measure and improve the ETL process. Monitoring error handling means using a tool or framework that can track and analyze the error handling performance, such as the error rate, the error type, the error location, the error resolution time, and the error impact. You should use a tool or framework that can provide visual and interactive reports and dashboards. You should also use a tool or framework that can provide insights and recommendations for error handling improvement.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?