Question 658 of 1,660
Real-Time Streaming IoT Data Processing with AWS
A company is designing a new application that will process real-time streaming data from thousands of IoT devices. The data must be ingested, processed with low latency, and stored in Amazon S3 for analytics. Which combination of AWS services should the company use to meet these requirements?
Quick Answer
The correct answer is Amazon Kinesis Data Streams, AWS Lambda, and Amazon S3. This combination works because Kinesis Data Streams provides durable, low-latency ingestion for real-time streaming IoT data processing on AWS, handling the high throughput from thousands of devices, while AWS Lambda processes each record on arrival through event source mapping, eliminating the need for managing servers or polling. The processed data is then written directly to Amazon S3, which serves as a cost-effective, scalable storage layer for downstream analytics. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of serverless, event-driven architectures for streaming workloads; a common trap is choosing Amazon Kinesis Data Firehose for processing, but Firehose lacks built-in per-record transformation logic and is better suited for near-real-time batch delivery rather than the low-latency processing required here. Remember the mnemonic "K-L-S" for Kinesis, Lambda, S3—think of it as "Keep Latency Short" to avoid overcomplicating the architecture.
⚠ Common exam trap
Candidates often confuse Amazon SQS with Kinesis Data Streams for real-time streaming, but SQS is a pull-based queue with no ordered replay or shard-level parallelism, making it unsuitable for high-throughput IoT data ingestion.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Kinesis Data Streams, AWS Lambda, Amazon S3
Amazon Kinesis Data Streams ingests real-time streaming data from thousands of IoT devices with low latency, and AWS Lambda can process each record as it arrives via event source mapping. The processed data is then stored in Amazon S3 for analytics, meeting all requirements for ingestion, low-latency processing, and durable storage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SQS, AWS Lambda, Amazon S3
Why it's wrong here
SQS is a message queue, not designed for real-time streaming ingestion.
- ✗
Amazon Kinesis Data Firehose, Amazon Redshift, Amazon S3
Why it's wrong here
Firehose delivers to Redshift or S3 but does not support real-time processing.
- ✗
Amazon MQ, AWS Lambda, Amazon RDS
Why it's wrong here
Amazon MQ is for message queues, RDS is for relational data, not streaming.
- ✓
Amazon Kinesis Data Streams, AWS Lambda, Amazon S3
Why this is correct
Kinesis Data Streams ingests streaming data, Lambda processes it, S3 stores it.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Same concept, more angles
1 more way this is tested on SAP-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is designing a new application on AWS that processes real-time IoT sensor data from thousands of devices. The data must be ingested, processed, and stored for analysis. The company wants to use a serverless architecture to reduce operational overhead. The processing includes filtering, aggregation, and transformation. Which solution should a Solutions Architect recommend?
medium- A.Use Amazon Kinesis Data Streams to ingest data, use Kinesis Data Firehose to deliver data to S3, and use Athena for queries.
- B.Use Amazon Kinesis Data Streams to ingest data, trigger a Lambda function for processing, and store results in DynamoDB.
- C.Use Amazon SQS to ingest sensor data, trigger a Lambda function for processing, and store results in DynamoDB.
- ✓ D.Use AWS IoT Core to ingest data, use IoT rules to route data to Kinesis Data Analytics for real-time processing, and store results in S3.
Why D: AWS IoT Core is purpose-built for ingesting data from IoT devices, its rules engine can route data to Kinesis Data Analytics for real-time processing using SQL, and results can be stored in S3 for analysis. Option A is incorrect because Kinesis Data Firehose delivers data in batches, not real-time processing as required. Option B is incorrect because while Lambda can process, it may have concurrency limits and is less optimal for high-throughput streaming compared to Kinesis Data Analytics. Option C is incorrect because SQS is not designed for real-time streaming ingestion from IoT devices; it's a message queue, not a streaming service.
Last reviewed: Jun 24, 2026
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