AWS Certified Solutions Architect Associate SAA-C03 Practice Question
A manufacturer's thousands of IoT sensors continuously emit telemetry that must be ingested, processed, and analyzed in near-real time so that the operations team can react immediately to anomalies. The solution must scale automatically to handle unpredictable traffic spikes and should not require administrators to manage any clusters. Which AWS service best meets these requirements?
Amazon Kinesis
AWS IoT Analytics
Amazon Simple Queue Service (SQS)
Amazon Managed Streaming for Apache Kafka (Amazon MSK)
Amazon Kinesis (specifically Kinesis Data Streams with Kinesis Data Analytics or AWS Managed Service for Apache Flink) is a fully managed platform for real-time streaming data ingestion and processing. It can elastically scale to gigabytes per second from millions of devices and allows you to analyze data as it arrives without managing servers or clusters.
Amazon MSK provides streaming capabilities but still requires you to manage Kafka topics, partitions, and cluster scaling, so it adds operational overhead.
Amazon SQS is a message queue designed for decoupling components, not high-throughput real-time analytics.
AWS IoT Analytics can ingest IoT data but performs batch processing; it is not optimized for millisecond-level streaming analytics.
Therefore, Amazon Kinesis is the most appropriate choice for immediate, scalable processing of streaming IoT data.
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Can AWS IoT Analytics process data in real-time like Amazon Kinesis?