Learn how to use AWS Lambda and Kinesis Data Firehose to remove pattern-based customer identifiers from data and store the modified data in an Amazon S3 bucket. This tutorial will show you how to implement data transformation and configure the delivery stream.
Table of Contents
Question
An Amazon Kinesis Data Firehose delivery stream is receiving customer data that contains personally identifiable information. A developer needs to remove pattern-based customer identifiers from the data and store the modified data in an Amazon S3 bucket.
What should the developer do to meet these requirements?
A. 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.
B. 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.
C. 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.
D. 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.
Answer
A. 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.
Explanation
The correct answer is A. 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.
Here is an explanation of why this solution will meet the requirements:
- The problem is that the developer needs to remove pattern-based customer identifiers from the customer data that is received by a Kinesis Data Firehose delivery stream, and store the modified data in an S3 bucket. Customer identifiers are sensitive information that should not be exposed or stored without proper encryption or masking.
- To solve this problem, the developer needs to use a feature of Kinesis Data Firehose that allows data transformation before delivery. Data transformation is a process that modifies the data records in a delivery stream by using a custom AWS Lambda function. The Lambda function can perform any logic on the data records, such as filtering, enriching, aggregating, or masking.
- The solution that meets the requirements is to implement Kinesis Data Firehose data transformation as a Lambda function, and configure the function to remove the customer identifiers. The developer can write a Lambda function in any supported language, such as Python, Node.js, or Java, and use regular expressions or other methods to identify and remove the customer identifiers from the data records. The developer can then set an S3 bucket as the destination of the delivery stream, where the transformed data will be stored in batches.
- This solution will meet the requirements because it will provide a simple and scalable way to remove sensitive information from the customer data, and store the modified data in a durable and cost-effective storage service.
Therefore, option A is the correct solution that will meet the requirements. Option B is incorrect because it will require launching and managing an EC2 instance, which will add more resources and costs to the application. Option C is incorrect because it will require creating and managing an OpenSearch Service instance, which is not designed for data transformation or export. Option D is incorrect because it will require creating and managing a Step Functions workflow, which is not compatible with Kinesis Data Firehose as a destination.
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