Many Kafka clients acquire data using multi-stage pipelines that burn through raw data from Kafka topics. Due to replication, Kafka provides comparable execution to log-driven systems, as well as much decreased start-to-finish idleness. The term "stream processing" refers to how data is handled in real time. Many Kafka clients use multi-stage pipelines to acquire data, in which raw data is burned through from Kafka topics and then aggregated, advanced, or in any case converted into new themes for further processing. For example, a handling pipeline for recommending news stories might slither article content from channels and distribute it to a "articles" point; further processing might standardise or duplicate this substance and distribute the scrubbed article substance to another subject; and finally, a final handling stage might try to prescribe this substance to clients. The first time Kafka was employed was to turn a client movement tracking pipeline into a series of continuous distribution buy-ins. This implies that site activity (such as site visits, views, and other consumer behaviours) is separated into focus topics, with one point assigned to each type of action. Because each client site visit creates many movement signals, action following is often relatively high volume.