Stream processing gets input data and processes it, and sends it to Output servers. The API of the stream processing’s role acts as a middle-man job. Stream processing API in the case of Kafka has multiple methods. These methods you can use in the development of Streaming applications.
How a Streaming Application Works (Example)
a. Customer Data Records
It is an input store. It has all the customer details. The data of customers as input determines reward points on the fly. So this is the prime advantage of a streaming application.
b. Transformation Value processor
It is an API. It can deal with Key, Value data store. From the Key-value data store, it gets data of the same customer. So that, it can use that to calculate total reward points. Transformation API works as ETL processing.
c. In-memory Key-Value Store
In-memory databases are purpose-built databases that rely primarily on memory for data storage, in contrast to databases that store data on disk or SSDs.
In-memory data stores are designed to enable minimal response times by eliminating the need to access disks. Because all data is stored and managed exclusively in main memory, in-memory databases risk losing data upon a process or server failure.
In-memory databases can persist data on disks by storing each operation in a log or by taking snapshots.
In-memory databases are ideal for applications that require microsecond response times or have large spikes in traffic such as gaming leaderboards, session stores, and real-time analytics.
d. Reward Points
The outcome is cumulative Reward points.