What ETL design patterns are best for handling errors and exceptions?

1 Error Handling Strategies

The first step in dealing with errors and exceptions is to decide how to handle them. There are three main strategies: ignore, retry, and fail. Ignoring errors means that you skip the problematic records or steps and continue with the rest of the ETL process. This can be useful for minor or non-critical errors, but it can also lead to data gaps or inaccuracies. Retrying errors means that you attempt to repeat the failed records or steps until they succeed or reach a limit. This can be useful for transient or intermittent errors, but it can also cause delays or loops. Failing errors means that you stop the entire ETL process and raise an alert. This can be useful for major or critical errors, but it can also cause data unavailability or inconsistency.

2 Error Logging and Notification

The second step in dealing with errors and exceptions is to log and notify them. Logging errors means that you capture and store the details of the errors, such as the source, the destination, the type, the message, the timestamp, and the stack trace. This can help you diagnose and troubleshoot the errors, and also track their frequency and impact. Notifying errors means that you send an alert or a message to the relevant stakeholders, such as the ETL developers, the data engineers, the data analysts, or the business users. This can help you inform and escalate the errors, and also trigger corrective actions or workflows.

3 Error Handling Framework

The third step in dealing with errors and exceptions is to implement an error handling framework. This set of rules, standards, and best practices defines how to manage errors and exceptions in your ETL pipelines, helping you achieve consistency, efficiency, and maintainability. An error handling framework consists of error categories and codes, which are predefined classifications for different types of errors; error handling logic that determines how to handle each type; error logging and notification mechanisms like databases or emails; and error monitoring and reporting tools like dashboards or alerts.

4 Error Handling Patterns

The fourth step in dealing with errors and exceptions is to apply error handling patterns. These are common solutions or approaches for handling errors and exceptions in specific scenarios or contexts, such as the dead letter queue, compensating transaction, circuit breaker, and retry policy. The dead letter queue pattern involves sending the failed records or messages to a separate queue or table for inspection, correction, or reprocessing. Compensating transactions are used to undo or fix the effects of a failed transaction, such as deleting, updating, or restoring the data. The circuit breaker pattern pauses the ETL process when a certain threshold of errors or failures is reached. Finally, a retry policy defines the rules and parameters for retrying the failed records or steps.

5 Error Handling Best Practices

The fifth step in dealing with errors and exceptions is to follow error handling best practices. These are general guidelines or recommendations for handling errors and exceptions in an effective and efficient way. For instance, validating and cleansing data prior to loading it to the target destination can reduce data quality errors. Additionally, using try-catch-finally blocks or equivalent mechanisms can catch and handle errors in your ETL code. Logging levels and filters can control the amount of error information that you log, avoiding sensitive or confidential data. Error notifications should be clear, concise, and actionable, avoiding spamming or overwhelming recipients. Lastly, error monitoring and reporting tools should be easy to access, understand, and use, providing relevant insights and metrics on the error trends and impacts.

6 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?