Amazon Kinesis Firehose vs Google Cloud Dataflow: What are the differences?
What is Amazon Kinesis Firehose? 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.
What is Google Cloud Dataflow? A fully-managed cloud service and programming model for batch and streaming big data processing. Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
Amazon Kinesis Firehose and Google Cloud Dataflow can be primarily classified as "Real-time Data Processing" tools.
Some of the features offered by Amazon Kinesis Firehose are:
- Integrated with AWS Data Stores
- Automatic Elasticity
On the other hand, Google Cloud Dataflow provides the following key features:
- Fully managed
- Combines batch and streaming with a single API
- High performance with automatic workload rebalancing Open source SDK
According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 32 company stacks & 8 developers stacks; compared to Amazon Kinesis Firehose, which is listed in 32 company stacks and 7 developer stacks.
Use case for ingressing a lot of data and post-process the data and forward it to multiple endpoints.
Kinesis can ingress a lot of data easier without have to manage scaling in DynamoDB (ondemand would be too expensive) We looked at DynamoDB Streams to hook up with Lambda, but Kinesis provides the same, and a backup incoming data to S3 with Firehose instead of using the TTL in DynamoDB.