Ensure data integrity for Change Data Capture for DynamoDB Stream

DynamoDB guarantees order on the item level, so multiple actions on the same item will always appear in the same sequence they occur even when Lambda scales up.

This is due to how DynamoDB streams map their shards to Lambda invocations, this 1:1 mapping will make sure ordering will remain intact. This blog will help you understand it in more detail:

https://aws.amazon.com/blogs/database/build-scalable-event-driven-architectures-with-amazon-dynamodb-and-aws-lambda/

Your concern about maintaining data integrity during Change Data Capture (CDC) with DynamoDB Streams and Lambda functions is valid. However, DynamoDB Streams provides key features to ensure data consistency and integrity:

1. Deduplication: Each stream record appears exactly once in the stream, preventing any duplicates. This is achieved through a monotonically increasing sequence number assigned to each record.

2. Ordering: DynamoDB ensures that stream records for modifications to a specific item are presented in the exact order in which the modifications occurred, preserving the sequence of changes.

These features mean that even if multiple Lambda functions are processing events concurrently, they will receive and process the records in the correct order. In your example of modifying an item and then deleting it, the stream will contain these events in the correct sequence, and they will be processed in that order.

However, to further ensure data integrity in your CDC process, consider the following best practices:

1. Use batch processing: Configure your Lambda function to process records in batches. This can help maintain the order of operations for a given item.

2. Implement idempotent operations: Design your Lambda function to handle duplicate or out-of-order processing gracefully. This can involve checking the current state of the item in the destination table before applying changes.

3. Use DynamoDB transactions: If your use case allows, consider using DynamoDB transactions in the destination table to ensure atomic updates.

4. Implement error handling and retries: Ensure your Lambda function can handle errors and retry operations if necessary.

5. Monitor and alert: Set up monitoring for your Lambda functions and DynamoDB Streams to quickly detect and respond to any issues.

By following these practices and leveraging the inherent ordering and deduplication features of DynamoDB Streams, you can maintain data integrity in your cross-account replication process.
Sources
Build scalable, event-driven architectures with Amazon DynamoDB and AWS Lambda | AWS Database Blog
Change data capture for DynamoDB Streams - Amazon DynamoDB