Flume offers a highly fault-tolerant, robust, and reliable mechanism for fail-over and recovery with the capability to collect data in batch and in-stream modes. It is also an open-source data collection service.Īpache Flume is based on streaming data flows and has a flexible architecture. Flume is a highly reliable, configurable, and manageable distributed data collection service designed to gather streaming data from different web servers to HDFS. Some of the use cases where Kafka is widely used are:Īpache Flume is a tool that collects, aggregates, and transfers data streams from different sources to a centralized data store such as HDFS (Hadoop Distributed File System). A message published for a topic can have multiple interested subscribers the system processes data for every interested subscriber. Likewise, an application can act as both a publisher and a subscriber. Numerous publishers and subscribers can be on different topics on a Kafka cluster. A subscriber requests a subscription, and Kafka forwards the data to the requested subscriber. Subscribers can also act as publishers and vice-versa. Data published by the publisher are stored as logs. In simplistic terms, Kafka’s publish-subscribe system comprises publishers, Kafka clusters, and consumers/subscribers. Kafka also can render streaming data through a combination of Apache HBase, Apache Storm, and Apache Spark systems and can be used in various application domains. Irrespective of the application or use case, Kafka efficiently factors massive data streams for analysis in enterprise Apache Hadoop. It allows users to store data streams in a fault-tolerant manner. With Kafka, users can publish and subscribe to information as and when they occur. It is a distributed streaming platform with capabilities similar to an enterprise messaging system but has unique capabilities with high levels of sophistication. Hadoop, Data Science, Statistics & othersĪpache Kafka will process incoming data streams irrespective of their source and destination.
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