Question #1520
A company is collecting log data from IoT devices every 30 seconds and needs to process this data in real time to detect anomalies. After processing, the data must be stored in an Amazon S3 data lake. Which solution offers the MOST operational efficiency?
Use Amazon Kinesis Data Streams to ingest data. Use AWS Lambda to process the data in real time.
Use AWS Glue to ingest data. Use Amazon Kinesis Data Analytics to process the data in real time.
Use Amazon Kinesis Data Firehose to ingest data. Use Amazon Kinesis Data Analytics to process the data in real time.
Use Amazon API Gateway to ingest data. Use AWS Lambda to process the data in real time.
Explanation
The correct answer is C because:
- Kinesis Data Firehose is designed for real-time data ingestion and automatically handles scaling, batching, and loading data into S3, reducing operational complexity.
- Kinesis Data Analytics provides serverless SQL or Flink-based processing for real-time anomaly detection without infrastructure management.
Other options are less efficient:
- A: Kinesis Data Streams requires manual shard management, and Lambda adds code maintenance and timeout constraints.
- B: AWS Glue is not suited for real-time ingestion (it’s for batch ETL).
- D: API Gateway is inefficient for high-frequency IoT data ingestion and incurs higher costs with Lambda.
Key Points: Use fully managed services (Firehose + KDA) for real-time ingestion, processing, and S3 storage with minimal operational effort.
Answer
The correct answer is: C