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Answer: Use Amazon Kinesis Data Streams for data ingestion and Amazon Kinesis Data Firehose for data loading
In this scenario, using Amazon Kinesis Data Streams for data ingestion and Amazon Kinesis Data Firehose for data loading would be the most appropriate choice. Kinesis Data Streams can handle high throughput and low latency for real-time data ingestion, while Kinesis Data Firehose can efficiently load the ingested data into various AWS data stores and analytics services. This combination provides a scalable and performant solution for large-scale IoT applications.
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You are tasked with designing a data ingestion pipeline for a large-scale IoT application that generates massive amounts of data in real-time. The pipeline should be able to handle high throughput and low latency. Which AWS service would you choose for this scenario, and how would you design the pipeline to meet the requirements?
A
Use Amazon S3 for data storage and Amazon Kinesis Data Analytics for real-time processing
B
Use Amazon DynamoDB for data storage and AWS Lambda for real-time processing
C
Use Amazon Kinesis Data Streams for data ingestion and Amazon Kinesis Data Firehose for data loading
D
Use Amazon Redshift for data storage and Amazon Kinesis Data Analytics for real-time processing