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 buffers messages for polling consumers rather than delivering a continuous, ordered stream; Lambda's invocation model cannot sustain the per-shard, low-latency processing thousands of IoT devices demand. SQS fits decoupling workloads where occasional polling delay is acceptable, not real-time ingestion.
- ✗
Amazon Kinesis Data Firehose, Amazon Redshift, Amazon S3
Why it's wrong here
Kinesis Data Firehose performs near-real-time delivery to destinations but offers no stream-processing engine; Redshift is a warehouse, not a low-latency processor, and data reaches S3 without transformation. Firehose suits simple ingestion pipelines where buffering and format conversion suffice, not continuous computation.
- ✗
Amazon MQ, AWS Lambda, Amazon RDS
Why it's wrong here
Amazon MQ brokers traditional messaging protocols for legacy applications and cannot ingest thousands of device streams at low latency; Lambda and RDS likewise cannot process or store the stream as required. Amazon MQ fits migrating existing JMS or AMQP workloads, not IoT telemetry pipelines.
- ✓
Amazon Kinesis Data Streams, AWS Lambda, Amazon S3
Why this is correct
Kinesis Data Streams ingests thousands of device events with low latency and durable ordering, Lambda consumes and processes records in real time, and Lambda's native S3 integration stores the results for analytics. This combination directly satisfies the ingestion, low-latency processing, and S3 storage requirements.
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 |
Go deeper
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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 designed for ingesting data from IoT devices at scale. IoT rules can route data to Kinesis Data Analytics for real-time processing (filtering, aggregation, transformation), and then store results in S3. This solution is fully serverless, reducing operational overhead, and leverages managed services for each stage.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAP-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAP-C02 exam.