DEA-C01 Question 157
Select 2You are designing a data pipeline to process streaming data from IoT devices that send temperature readings every second. The data must be processed in near real-time, stored for future analysis, and queried using SQL. Which combination of AWS services is the most appropriate for this use case?
- A
Amazon Kinesis Data Streams for ingesting data, AWS Lambda for processing, and Amazon S3 for storage
- B
Amazon Kinesis Data Firehose for ingesting and transforming data, Amazon S3 for storage, and Amazon Athena for querying
- C
Amazon Redshift for ingesting and storing data, and AWS Glue for processing and querying
- D
Amazon Kinesis Data Streams for ingesting data, Amazon Elasticsearch Service for near real-time analysis, and Amazon S3 for storage
- E
Amazon DynamoDB Streams for ingesting data, AWS Glue for ETL processing, and Amazon Redshift for storage and querying
Show answer and explanation
Correct answers: A, B
Explanation
To handle IoT streaming data, a combination of services like Amazon Kinesis Data Streams or Kinesis Data Firehose for ingestion is essential. Kinesis Data Streams is ideal for custom processing with AWS Lambda, while Kinesis Data Firehose simplifies ingestion and transformation. For storage, Amazon S3 provides scalable and cost-effective options, and Amazon Athena enables SQL-based querying directly from S3. The correct answers reflect solutions optimized for streaming data, near real-time processing, and SQL-based analysis.
- A. Correct.
Correct: Amazon Kinesis Data Streams can handle high-throughput streaming data ingestion, AWS Lambda can process the data in near real-time, and Amazon S3 can store the processed data for future analysis.
- B. Correct.
Correct: Amazon Kinesis Data Firehose can ingest and transform streaming data automatically, Amazon S3 provides scalable storage, and Amazon Athena enables querying using SQL directly from S3.
- C. Incorrect.
Incorrect: Amazon Redshift is not optimized for direct ingestion of high-throughput streaming data. It is better suited for structured batch data and analytics.
- D. Incorrect.
Incorrect: While Amazon Elasticsearch Service can perform near real-time analysis, it is not designed for long-term storage or SQL-based querying.
- E. Incorrect.
Incorrect: DynamoDB Streams is designed for capturing database changes, not for high-throughput IoT streaming data ingestion. Additionally, this option does not provide a SQL-based querying solution.