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Data Streams is a low latency streaming service in AWS Kinesis with the facility for ingesting at scale. On the other hand, Kinesis Firehose aims to serve as a data transfer service. The primary purpose of Kinesis Firehose focuses on loading streaming data to Amazon S3, Splunk, ElasticSearch, and RedShift.
The typical 'fire hose' you see attached coiled on a fire truck and attached to a fire hydrant, they are used to carry water to fight fires in or outdoors. They are typically 100 feet long.
Amazon Kinesis Data Streams is useful for rapidly moving data off data producers and then continuously processing the data, be it to transform the data before emitting to a data store, run real-time metrics and analytics, or derive more complex data streams for further processing.
Also known as event stream processing, streaming data is the continuous flow of data generated by various sources. By using stream processing technology, data streams can be processed, stored, analyzed, and acted upon as it's generated in real-time.
I am working on an application that will read and analyze the logs of payment transactions. I know I will use Kinesis Analytics as per my requirements, which takes the input from the Data Streams and Firehose. But I am having trouble deciding which input method should I use for my system. My requirements are:
Which input stream is suitable for my use case?
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