DEA-C01 Question 389
Select 2You are a data engineer managing a data pipeline that ingests streaming data from IoT devices into Amazon Kinesis Data Streams. The data then needs to be analyzed in near real-time and archived for long-term storage. Which combination of services and configurations would best meet this requirement?
- A
Use Amazon Kinesis Data Analytics to process the data in real-time and send analyzed results to Amazon S3 for long-term storage.
- B
Use AWS Lambda to process the data from Kinesis Data Streams and store the results directly in Amazon Aurora.
- C
Use Amazon Kinesis Firehose to deliver data from Kinesis Data Streams to Amazon Redshift for analysis and long-term storage.
- D
Use Amazon Kinesis Data Analytics to process the data in real-time and deliver the results to Amazon DynamoDB.
- E
Use AWS Lambda to process the data in Kinesis Data Streams and store raw data in Amazon S3 for long-term storage.
Show answer and explanation
Correct answers: A, E
Explanation
To meet the requirement of near real-time processing and long-term storage, using Amazon Kinesis Data Analytics for processing and Amazon S3 for storage is an optimal solution. AWS Lambda can also be used to process data from Kinesis Data Streams, and the raw data can be archived in Amazon S3 for long-term cost-effective storage. Solutions involving Aurora or DynamoDB are not ideal for long-term storage of large streaming datasets, and Kinesis Firehose cannot directly pull data from Kinesis Data Streams.
- A. Correct.
This is correct because Amazon Kinesis Data Analytics can process streaming data in real-time, and Amazon S3 is an ideal service for long-term, cost-effective storage.
- B. Incorrect.
This is incorrect because Amazon Aurora is not optimized for storing large volumes of streaming data, and Lambda functions are not typically used for direct storage into relational databases for this use case.
- C. Incorrect.
This is incorrect because Kinesis Firehose cannot directly pull data from Kinesis Data Streams; it typically ingests data directly from sources like producers or Kinesis Agent.
- D. Incorrect.
This is incorrect because while Amazon Kinesis Data Analytics can process data in real-time, Amazon DynamoDB is not designed for long-term storage of large streaming datasets.
- E. Correct.
This is correct because AWS Lambda can process the data from Kinesis Data Streams, and Amazon S3 is suitable for long-term storage of the raw data.