Here’s the difference between AWS Kinesis and Gridgain. The comparison is based on pricing, deployment, business model, and other important factors.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.
GridGain enables organizations to accelerate data processing in their Hadoop deployments by providing an in-memory platform. The platform can be used as a database, a stream computing platform, a messaging platform or an accelrator for hadoop based computations. GridGain's in-memory file system accelerates I/O in the Hadoop stack. GridGain requires zero code change to existing MapReduce code for similar performance improvement. It also offers a management and monitoring tool called GridGain Visor for its Hadoop accelerator platform. It is built on top of Apache Ignite, an In-Memory data Fabric. Its clients include Apple, Avis, Canon, IHG, McGraw-Hill Education, Moody's KMV, Sberbank, Sony, TomTom, and Worldpay. The company was named a 'Cool Vendor' for In-Memory Computing Technologies by Gartner in 2014. Gridgain has incubated and open sourced the In-Memory platform in Apache as Ignite.