Kinesis Data Analytics for Real-Time Streaming Transformation
A company is designing a new application that will process streaming data from IoT devices. The data must be processed in real time and then stored in Amazon S3 for long-term analytics. Which combination of AWS services should be used?
⚠ Common exam trap
Candidates often confuse Kinesis Data Firehose (which delivers near-real-time batches) with Kinesis Data Streams (which enables per-record real-time processing), leading them to pick Option A despite its lack of a real-time processing component.
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 and buffers streaming IoT data in real time, AWS Lambda processes each record as it arrives, and the processed data is written directly to Amazon S3 for durable long-term analytics. This combination provides the low-latency, serverless pipeline required for real-time processing and S3-based 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 Kinesis Data Firehose, Amazon Redshift
Why it's wrong here
Kinesis Data Firehose delivers to S3, Redshift, OpenSearch or Splunk; pairing it with Redshift bypasses the required S3 long-term store. It is tempting because Firehose buffers and loads streams into Redshift, which would be correct when the analytics target is a Redshift warehouse rather than S3 objects.
- ✗
Amazon SQS, AWS Lambda, Amazon RDS
Why it's wrong here
SQS queues messages for polling consumers and RDS is a relational store, so nothing lands in S3 for long-term analytics. It is tempting because SQS plus Lambda decouples producers from consumers, which would be correct for asynchronous task processing rather than real-time streaming into S3.
- ✗
AWS IoT Core, Amazon DynamoDB
Why it's wrong here
IoT Core ingests device messages and DynamoDB stores key-value items; neither writes the stream to S3 for long-term analytics. It is tempting because IoT Core is the natural device-facing endpoint, and would be correct when the requirement is device registry, shadow state and low-latency item lookups.
- ✓
Amazon Kinesis Data Streams, AWS Lambda, Amazon S3
Why this is correct
Kinesis Data Streams ingests the IoT telemetry with low latency, Lambda processes each record in real time as it arrives, and S3 stores the results durably for later analytics. This satisfies both the real-time processing and long-term 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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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