Integrity

Data integrity was a top priority. I couldn’t risk inaccurate or incomplete insights compromising our decision-making. To safeguard this, I incorporated robust data validation and error handling mechanisms throughout the pipeline.

Within my Lambda functions, I implemented rigorous checks at various stages:

Upon Ingestion: Validating the incoming data’s structure, format, and content against predefined rules. During Transformation: Identifying and handling potential outliers, missing values, or inconsistencies. Before Storage: Confirming that the processed data adhered to expected schemas and quality standards. In case of errors, the pipeline was designed to halt gracefully. It would log detailed error messages, providing a clear audit trail for troubleshooting. In some instances, I implemented automated retries for transient errors, ensuring resilience and minimizing manual intervention.

I also leveraged AWS’s built-in capabilities for data integrity. For example, S3’s versioning feature allowed me to track changes to data files and revert to previous versions if necessary. Additionally, CloudWatch alarms alerted me to potential issues, enabling proactive problem resolution.