SAP-C02 Question 648
Select 2A company is building a real-time analytics platform for processing large volumes of streaming data generated by IoT devices. The solution needs to ensure scalability, fault-tolerance, and low-latency processing of data. The company also requires the ability to replay historical data for debugging purposes. Which combination of AWS services should you recommend to meet these requirements?
- A
Amazon Kinesis Data Streams for ingesting data and Amazon Kinesis Data Analytics for real-time processing
- B
Amazon SQS for ingesting messages and AWS Lambda for real-time processing
- C
Amazon Managed Streaming for Apache Kafka (Amazon MSK) for ingesting data and AWS Glue for real-time processing
- D
Amazon Kinesis Data Streams for ingesting data and AWS Lambda for real-time processing
- E
Amazon Kinesis Data Streams for ingesting data and Amazon EMR with Apache Spark for real-time processing
Show answer and explanation
Correct answers: A, E
Explanation
The company needs a solution optimized for real-time processing with high scalability, fault tolerance, and replayability. Amazon Kinesis Data Streams is a fully managed, high-throughput service designed for ingesting streaming data, and it supports data replay. For processing, Amazon Kinesis Data Analytics offers low-latency real-time analytics, while Amazon EMR with Apache Spark provides a powerful framework for real-time and batch processing with support for reprocessing historical data. These combinations align with the requirements, making them the best choices.
- A. Correct.
Correct: Amazon Kinesis Data Streams can handle large volumes of streaming data, and Amazon Kinesis Data Analytics supports real-time analytics with low latency. This combination is scalable, fault-tolerant, and enables replayability.
- B. Incorrect.
Incorrect: Amazon SQS is designed for message queuing, not streaming data processing. While AWS Lambda can process data in real time, this combination is not optimal for high-throughput streaming workloads or replayability.
- C. Incorrect.
Incorrect: Amazon MSK is a viable service for ingesting streaming data, but AWS Glue is not designed for real-time processing. It is better suited for batch ETL jobs.
- D. Incorrect.
Incorrect: While Amazon Kinesis Data Streams is suitable for ingesting streaming data, AWS Lambda is not ideal for low-latency, high-throughput real-time processing.
- E. Correct.
Correct: Amazon Kinesis Data Streams provides scalable, fault-tolerant data ingestion, while Amazon EMR with Apache Spark can handle real-time processing of large data volumes. Apache Spark also supports replayability of historical data.