Building a Stream Processing Application using open source libraries

Today data has generated constantly, and business needs the latest data to be used for business decisions via intelligent applications. This requires constantly processing data in a streaming fashion to get the lower latency. This will also allow optimum usage of the resources and get the up-to-date data loaded into the systems.

Stream processing involves multiple processing steps in near real-time as the data is produced, transported, and received at the target location. Some examples of such processing requirements processing data in motion are from continuous streams from sensors in IT infrastructure, machine sensors, health sensors, stock trade activities, etc

To create an end-to-end stream processing, you will need components performing different tasks stitched together in a pipeline and workflow.

Streaming

Using the below libraries, you can build you own correct concurrent and scalable streaming applications.

Stream processing engine

The below open-source stream processing framework provide you with stream processing capabilities.

Data Pipeline

Below libraries help in defining both batch and parallel processing pipelines running in a distributed processing backends.