DEA-C01 Question 298
Select 3You are designing a data ingestion pipeline for a large-scale IoT application that generates millions of events each hour. The solution must handle high-throughput data ingestion reliably, ensure scalability, and support downstream analytics using Amazon S3 and Amazon Redshift. Which combination of AWS services should you use for the ingestion pipeline?
- A
Amazon Kinesis Data Streams for ingestion and Amazon S3 for storage
- B
Amazon SQS for ingestion and Amazon DynamoDB for storage
- C
Amazon Kinesis Data Firehose for ingestion and Amazon S3 for storage
- D
Amazon Kinesis Data Firehose for ingestion and Amazon Redshift for storage and analytics
- E
Amazon RDS for ingestion and Amazon S3 for storage
Show answer and explanation
Correct answers: A, C, D
Explanation
For a high-throughput IoT application, Amazon Kinesis Data Streams or Amazon Kinesis Data Firehose are the most appropriate services for ingestion. Both services are designed to handle real-time streaming data at scale. Amazon S3 is a cost-effective and scalable storage option, while Amazon Redshift is optimized for analytics. Combining these services ensures scalability, reliability, and support for downstream analytics.
- A. Correct.
Correct: Amazon Kinesis Data Streams can handle high-throughput, real-time data ingestion. Amazon S3 is a scalable and cost-effective storage solution for streaming data.
- B. Incorrect.
Incorrect: Amazon SQS is designed for message queuing, not high-throughput streaming. DynamoDB is suitable for key-value or document-style data, not large-scale analytics.
- C. Correct.
Correct: Amazon Kinesis Data Firehose is a managed service that simplifies real-time data ingestion and can deliver data directly to Amazon S3 for storage.
- D. Correct.
Correct: Amazon Kinesis Data Firehose can also deliver data directly to Amazon Redshift for analytics, making it an excellent choice for end-to-end streaming and analytics pipelines.
- E. Incorrect.
Incorrect: Amazon RDS is not optimized for high-throughput real-time ingestion and is not commonly used for direct integration with streaming data pipelines.