From the course: Complete Guide to Data Lakes and Lakehouses
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Error handling, logging, and monitoring
From the course: Complete Guide to Data Lakes and Lakehouses
Error handling, logging, and monitoring
- Sooner or later, our data pipelines will break. If you've been a data engineer for some time, you know that it's just the reality. Error handling in data ingestion involves anticipating, catching, and resolving errors that occur during the data loading process. It prevents corrupt data from entering the data lake and ensures that ingestion processes are not disrupted. Some examples of error handling strategies include implementing retry logic for transient errors and setting up notification systems for immediate alerts on failures. Now let's discuss some best practices. Design ingestion processes to fail gracefully, ensuring that partial failures do not impact the entire system. You can also categorize errors to handle them based on their criticality and type. This helps in applying a specific remediation techniques for different error types. Where possible, you can also automate the recovery processes to restart and rerun failed processes without manual intervention. One of the…
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