DEA-C01 Question 453
Select 2You are designing a data pipeline using AWS services to process and analyze large volumes of clickstream data in real time. The pipeline must ingest data, process it with low latency, and store the results for querying. Which combination of AWS services should you use to meet these requirements?
- A
Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, and Amazon DynamoDB for storage
- B
Amazon S3 for ingestion, Amazon Athena for processing, and Amazon Redshift for storage
- C
Amazon Kinesis Firehose for ingestion, Amazon EMR for processing, and Amazon S3 for storage
- D
Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for processing, and Amazon S3 for storage
- E
Amazon SQS for ingestion, AWS Glue for processing, and Amazon RDS for storage
Show answer and explanation
Correct answers: A, D
Explanation
To build a real-time data pipeline for clickstream data, you need services that support high-throughput ingestion, low-latency processing, and scalable storage. Amazon Kinesis Data Streams is optimized for real-time ingestion, AWS Lambda or Amazon Kinesis Data Analytics can process data in real time, and Amazon DynamoDB or Amazon S3 can store the processed results. Choosing the right combination of services ensures the pipeline meets both performance and scalability requirements.
- A. Correct.
Correct: Amazon Kinesis Data Streams is ideal for real-time ingestion of clickstream data, AWS Lambda can process the data with low latency, and Amazon DynamoDB is a low-latency database suitable for storing results.
- B. Incorrect.
Incorrect: While Amazon S3 and Athena are great for batch processing, they are not well-suited for low-latency real-time processing requirements.
- C. Incorrect.
Incorrect: Amazon Kinesis Firehose is more suited for near real-time ingestion into S3, and Amazon EMR is designed for big data batch processing, not real-time analytics.
- D. Correct.
Correct: Amazon Kinesis Data Streams supports real-time ingestion, Amazon Kinesis Data Analytics can process streaming data in real time, and Amazon S3 can be used to store the processed data.
- E. Incorrect.
Incorrect: Amazon SQS is a message queue service, not optimized for high throughput streaming like Kinesis Data Streams. AWS Glue is primarily used for ETL jobs, not for low-latency real-time processing.