I’ve recently been working on a streaming component in a project and have been spending a lot of time with both Kinesis Streams and Kinesis Firehose. I tend to think of the two as event queue frameworks, with Firehose also having the ability to forward events to other AWS services like ElasticSearch (for Kibana-style dashboarding) and backup the same data to a S3 bucket. If you don’t need either of those destinations, then most likely you will get plenty of mileage out of working with Streams alone.
Potential uses abound, but one powerful pattern is making Kinesis a destination for CloudWatch Logs streams via subscription filters. By creating a Kinesis stream and making it a CloudWatch log destination in one account, you can readily add CloudWatch subscription filters in other accounts to create a cross-account log sink. Once your CloudWatch Logs are in one or more Kinesis Streams shards, you can process that log data via Lambda and/or possibly forward to Kinesis Firehose for ES/S3 delivery. There’s a great blog post over at Blend about this exact sort of usage, including a link to their GitHub repo for the CloudFormation templates they use to build and deploy the solution.
One of the best overviews I’ve read recently about design and scale-out issues around event queue processing and how Kinesis resolves, by design, a lot of the challenges therein (e.g., data duplication, ABA problems) is by the fine folks over at Instrumental, entitled “Amazon Kinesis: the best event queue you’re not using“. If you are considering using Kinesis at scale, or are already designing/deploying a consumer/producer pattern to be used with Kinesis, I highly recommend you check out the Instrumental blog post.