Near Real-Time IoT Data Pipeline with Kinesis Data Firehose and Analytics
A data engineer is designing a data ingestion pipeline that will receive up to 5 GB of data per hour from thousands of IoT devices. The data must be stored in Amazon S3 and analyzed in near real-time. Which TWO services should be used together to meet these requirements? (Choose TWO.)
Quick Answer
The answer is Amazon Kinesis Data Firehose and Amazon Kinesis Data Analytics. Kinesis Data Firehose is the ideal ingestion service for reliably capturing high-throughput IoT streaming data—up to 5 GB per hour—and delivering it directly to Amazon S3 for storage, while Kinesis Data Analytics enables near real-time analysis on the data stream before or after it lands in S3, using SQL or Apache Flink to detect patterns and anomalies. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this pairing tests your understanding of building a streaming data pipeline for ML workloads, where you must distinguish Firehose’s managed delivery from services like SQS (which decouples applications but lacks streaming analytics) or Lambda (a compute trigger, not a streaming engine). A common trap is choosing Athena for real-time analysis, but Athena is for ad-hoc queries on data already in S3, not for processing live streams. Remember the mnemonic “Firehose for flow, Analytics for action” to recall that Firehose handles the heavy lifting of ingestion and S3 delivery, while Analytics provides the near real-time processing engine.
⚠ Common exam trap
It's easy for candidates to confuse Amazon Kinesis Data Firehose with Amazon Kinesis Data Streams, or mistakenly think Amazon Athena can ingest streaming data because it can query S3 in near-real-time, but Athena is purely a query engine and cannot replace the ingestion and streaming analytics components required for this pipeline.
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 Analytics
Amazon Kinesis Data Firehose is the correct service because it can reliably ingest streaming data from thousands of IoT devices at up to 5 GB per hour, automatically buffer, compress, and deliver the data to Amazon S3 with near-real-time latency (typically 60 seconds). Amazon Kinesis Data Analytics is correct because it enables real-time SQL-based analytics on the streaming data before it is stored in S3, allowing the data engineer to derive insights as data arrives without needing to query the S3 bucket after 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.
- ✗
AWS Lambda
Why it's wrong here
Lambda functions have a maximum execution timeout of 15 minutes and a 10 GB ephemeral storage limit, making them incapable of processing the sustained 5 GB/hour ingestion rate from thousands of devices without dropping data or incurring throttling. This option is tempting because Lambda is commonly used for lightweight, event-driven data transformations on streaming records, such as enriching individual IoT messages before forwarding to a destination, but it lacks the buffering and throughput management required for high-volume, near-real-time ingestion into S3.
- ✓
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics can run SQL queries on streaming data for near real-time analysis.
- ✗
Amazon Athena
Why it's wrong here
Athena is an interactive query service on S3, not for real-time streaming analysis.
- ✓
Amazon Kinesis Data Firehose
Why this is correct
Firehose can ingest streaming data and deliver to S3 with near real-time latency.
- ✗
Amazon Simple Queue Service (Amazon SQS)
Why it's wrong here
SQS is a message queue, not designed for real-time analytics on streaming data.
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
Related to this question
About these practice questions
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Same concept, more angles
1 more way this is tested on MLS-C01
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 data engineer needs to process streaming data from an IoT fleet and store the results in Amazon S3 for analysis. The solution must be serverless and handle data that arrives at irregular intervals. Which AWS service should be used to ingest the data?
easy- A.Amazon S3
- ✓ B.AWS IoT Core
- C.Amazon Simple Queue Service (SQS)
- D.Amazon Kinesis Data Streams
Why B: AWS IoT Core is the correct choice because it is a fully managed, serverless service designed specifically to ingest data from IoT devices at scale, handling irregular and high-frequency message arrivals via MQTT, HTTP, or LoRaWAN protocols. It can directly route data to Amazon S3 using IoT Rules, making it ideal for this streaming IoT fleet scenario without requiring any server management.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 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 MLS-C01 exam.