A company is building a serverless application to process clickstream data from its website. The clickstream data is sent to an Amazon Kinesis Data Streams data stream from the application web…

Question

Options

• AUse an AWS Lambda function to process and enrich the clickstream data. Use the same Lambda
• BUse an Amazon EC2 Spot Instance to poll the data stream and enrich the clickstream data.
• CUse an Amazon Elastic Container Service (Amazon ECS) task with AWS Fargate Spot capacity
• DUse Amazon Kinesis Data Firehose to load the clickstream data from Kinesis Data Streams to

• A
  45% (17)
• B
  8% (3)
• C
  34% (13)
• D
  13% (5)

Explanation

Option Ais the best solution as it leveragesAWS Lambdafor serverless, scalable, and highly available processing and enrichment of clickstream data. Lambda can process the data in real- time, join it with the Aurora database data, and write the enriched results to Amazon S3. FromS3, Amazon Redshift Spectrumcan directly query the enriched data without needing to load the data into Redshift, enabling cost efficiency and high availability. Why Other Options Are Incorrect: Option B: EC2 Spot Instances are not guaranteed to be highly available, as Spot Instances can be interrupted at any time. This does not align with the requirement for high availability. Option C: While ECS with AWS Fargate provides scalability, using EC2 for the COPY command introduces operational overhead and compromises high availability. Option D: Kinesis Data Firehose and Athena are suitable for querying raw data, but they do not directly support enriching the data by joining with Aurora. This solution fails to meet the requirement for data enrichment. Key AWS Features Used: AWS Lambda: Real-time serverless processing with integration capabilities for Aurora and S3. Amazon S3: Cost-effective storage for enriched data. Amazon Redshift Spectrum:Direct querying of data stored in S3 without loading it into Redshift.

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