Amazon Kinesis Firehose vs Kafka: What are the differences?
Developers describe Amazon Kinesis Firehose as "Simple and Scalable Data Ingestion". Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today. On the other hand, Kafka is detailed as "Distributed, fault tolerant, high throughput pub-sub messaging system". Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Amazon Kinesis Firehose and Kafka are primarily classified as "Real-time Data Processing" and "Message Queue" tools respectively.
Some of the features offered by Amazon Kinesis Firehose are:
- Integrated with AWS Data Stores
- Automatic Elasticity
On the other hand, Kafka provides the following key features:
- Written at LinkedIn in Scala
- Used by LinkedIn to offload processing of all page and other views
- Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)
Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Amazon Kinesis Firehose, which is listed in 33 company stacks and 9 developer stacks.