DEA-C01 Question 198
Select 2You are a data engineer tasked with designing a real-time analytics pipeline for a stock trading platform. The platform needs to ingest high-velocity trading data, process it in near real-time, and store the results for querying and visualization. Which combination of AWS services is the most appropriate for building this pipeline?
- A
Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, and Amazon S3 for storage
- B
Amazon SQS for ingestion, Amazon EMR for processing, and Amazon Elasticsearch Service (OpenSearch) for storage and querying
- C
Amazon Kinesis Data Firehose for ingestion, AWS Glue for processing, and Amazon Redshift for storage and querying
- D
Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon DynamoDB for storage
- E
Amazon MQ for ingestion, AWS Batch for processing, and Amazon RDS for storage
Show answer and explanation
Correct answers: A, D
Explanation
Designing a real-time analytics pipeline requires services optimized for high-throughput data ingestion, real-time processing, and scalable storage. Amazon Kinesis Data Streams is ideal for ingesting high-velocity data, while AWS Lambda or Amazon Kinesis Data Analytics can provide real-time processing. Storage solutions like Amazon S3 or Amazon DynamoDB are well-suited for storing processed results in a scalable and queryable format. The correct combinations ensure the pipeline meets the requirements for real-time performance and scalability.
- A. Correct.
Correct. Amazon Kinesis Data Streams is designed for high-throughput, real-time data ingestion, AWS Lambda can process streaming data in real-time, and Amazon S3 provides cost-effective, scalable storage for analytical results.
- B. Incorrect.
Incorrect. Amazon SQS is better suited for message queuing, not high-throughput, real-time data ingestion. Amazon EMR is more suitable for batch processing rather than real-time processing, and Elasticsearch (OpenSearch) can be used for querying but is not typically used as a primary storage solution.
- C. Incorrect.
Incorrect. While Amazon Kinesis Data Firehose is suitable for data ingestion, AWS Glue is optimized for ETL workflows, not real-time processing. Amazon Redshift is effective for data warehousing but does not support real-time analytics natively.
- D. Correct.
Correct. Amazon Kinesis Data Streams supports real-time ingestion, Amazon Kinesis Data Analytics enables real-time data processing, and Amazon DynamoDB offers low-latency storage for real-time analytics results.
- E. Incorrect.
Incorrect. Amazon MQ is a message broker service that is not designed for high-throughput, real-time ingestion. AWS Batch is optimized for batch processing, not real-time, and Amazon RDS is not typically used for high-velocity, real-time analytics pipelines.