What should the developer do to meet these requirements?
Implement Kinesis Data Firehose data transformation as an AWS Lambda function. Configure the function to remove the customer identifiers. Set an Amazon S3 bucket as the destination of the delivery stream.
Launch an Amazon EC2 instance. Set the EC2 instance as the destination of the delivery stream. Run an application on the EC2 instance to remove the customer identifiers. Store the transformed data in an Amazon S3 bucket.
Create an Amazon OpenSearch Service instance. Set the OpenSearch Service instance as the destination of the delivery stream. Use search and replace to remove the customer identifiers. Export the data to an Amazon S3 bucket.
Create an AWS Step Functions workflow to remove the customer identifiers. As the last step in the workflow, store the transformed data in an Amazon S3 bucket. Set the workflow as the destination of the delivery stream.
Explanations:
Implementing Kinesis Data Firehose data transformation using an AWS Lambda function allows for on-the-fly data manipulation. The Lambda function can be configured to identify and remove customer identifiers from the incoming data stream before storing the modified data in an Amazon S3 bucket. This approach is efficient, serverless, and directly integrates with Kinesis Data Firehose.
While using an EC2 instance to process the data is a possible solution, it is not the most efficient or scalable method. It involves managing infrastructure and deploying an application, which adds complexity and operational overhead compared to using a Lambda function. Moreover, the EC2 instance would not be the ideal choice as a delivery destination in this context.
Using Amazon OpenSearch Service as the destination does not align with the requirement of pattern-based data modification before storage. OpenSearch is primarily for search and analytics and is not designed to perform direct data transformations during the delivery process. Additionally, the need to export data to S3 adds unnecessary steps and complexity.
AWS Step Functions are useful for orchestrating workflows but are not designed for real-time data processing within Kinesis Data Firehose. Using Step Functions would introduce latency and complexity since the transformation would occur outside the Firehose stream, making it less efficient than using a Lambda function directly in the Firehose pipeline.
I structure that the answer is:
Implement Kinesis Data Firehose data transformation as an AWS Lambda function. Configure the function to remove the customer identifiers. Set an Amazon S3 bucket as the destination of the delivery stream.