SAA-C03 Question 350
Single answerA company needs to ingest large volumes of log data from thousands of IoT devices deployed across multiple geographical locations. The data needs to be processed near real-time for analytics and monitoring. Which AWS service and ingestion pattern should the company use to handle the data ingestion effectively?
- A
Use Amazon Kinesis Data Streams with a continuous data streaming pattern
- B
Use Amazon S3 with a batch data upload pattern
- C
Use AWS Snowball Edge for bulk data transfer
- D
Use Amazon SQS with a message queuing pattern
Show answer and explanation
Correct answer: A
Explanation
To handle large volumes of log data from IoT devices and process it in near real-time, a real-time streaming service like Amazon Kinesis Data Streams is the best choice. It supports high-throughput data ingestion and real-time processing, which aligns with the company's requirements. Other services like S3, Snowball Edge, and SQS are not optimized for continuous real-time data ingestion.
- A. Correct.
Amazon Kinesis Data Streams is designed for real-time data ingestion and processing, making it the most suitable choice for a continuous data streaming pattern required for near real-time analytics.
- B. Incorrect.
Amazon S3 is ideal for storing and retrieving large amounts of data, but it is not designed for real-time ingestion. Batch uploads to S3 are better suited for periodic or less frequent data transfers.
- C. Incorrect.
AWS Snowball Edge is a physical device used for bulk data transfer when there is a need to move petabytes of data between on-premises and AWS. It is not suitable for real-time ingestion.
- D. Incorrect.
Amazon SQS is a message queuing service designed for decoupling and buffering messages between distributed components, but it is not optimized for high-throughput, real-time data streaming.