DEA-C01 Question 259
Select 2You are a data engineer tasked with designing a data pipeline for a retail company that collects real-time clickstream data from its e-commerce website. The data is ingested into Amazon Kinesis Data Streams, and you need to process and store it for analytics. The company requires near real-time analytics and wants to use a serverless architecture. Which combination of services would best meet these requirements?
- A
Amazon Kinesis Data Analytics to process the data and Amazon S3 for storage
- B
AWS Lambda to process the data and Amazon DynamoDB for storage
- C
Amazon Kinesis Firehose to deliver the data to Amazon Redshift
- D
Amazon EMR to process the data and Amazon S3 for storage
- E
Amazon Kinesis Data Analytics to process the data and Amazon Redshift for storage
Show answer and explanation
Correct answers: A, C
Explanation
To meet the requirement for near real-time analytics and a serverless architecture, Amazon Kinesis Data Analytics is the best choice for processing data from Kinesis Data Streams in real-time. For storage, both Amazon S3 and Amazon Redshift are suitable for analytics, but Amazon S3 is serverless and cost-effective while Amazon Redshift requires managing a cluster. Amazon Kinesis Firehose provides a direct serverless integration to deliver data into Redshift, making it a valid choice in this scenario. Therefore, the correct answers are the combinations of Amazon Kinesis Data Analytics with Amazon S3 and Kinesis Firehose with Redshift.
- A. Correct.
Amazon Kinesis Data Analytics can process real-time data from Kinesis Data Streams, and Amazon S3 provides a cost-effective, scalable storage solution. This combination aligns well with the requirement for near real-time analytics and a serverless architecture.
- B. Incorrect.
AWS Lambda can process the real-time data from Kinesis Data Streams, but Amazon DynamoDB is not the ideal choice for analytics storage. DynamoDB is more suited for NoSQL workloads rather than analytical queries.
- C. Correct.
Amazon Kinesis Firehose can deliver data to Amazon Redshift, which is suitable for analytics. This combination also supports near real-time analytics and serverless architecture.
- D. Incorrect.
Amazon EMR is not serverless and involves managing a cluster, which goes against the company's requirement for a serverless architecture.
- E. Incorrect.
Amazon Kinesis Data Analytics is suitable for processing real-time data, but Amazon Redshift requires managing a cluster and is not fully serverless, making it less ideal for the given requirements.