Many individuals use Kafka as an alternative to a log collection system. Log aggregation gathers workers' actual log documents and saves them in a central location for processing (maybe a record worker or HDFS). Kafka abstracts record nuances, allowing for a clearer view of log or event data as a flurry of messages. This explains why inactivity management is easier and why varied information sources and distributed information consumption are easier. Because each client site visit generates many movement signals, action following is frequently very high in volume.
Kafka delivers comparably high performance to log-driven systems like Scribe or Flume, as well as better grounded solidity due to replication and significantly decreased start-to-finish idleness. Stream Processing is a real-time data processing approach. Many Kafka clients collect data in multi-stage pipelines, in which raw data from Kafka topics is burned through, then accumulated, advanced, or changed into new themes for further usage or processing.
The initial application of Kafka was to re-engineer a client movement tracking pipeline as a series of continuous distribution buy-ins. This means that site activity (such as site visits, looks, and other consumer activities) is split into focus themes, with one point awarded to each type of action. Because each client site visit generates many movement signals, action following is frequently very high in volume. Kafka collects data in stages, with raw data from Kafka topics being burned through and then aggregated, advanced, or in any case changed into new themes for future use or processing.