![]() ![]() The comparison table between Apache Kafka vs Flume is mentioned below: Basis for Comparison On the other hand, Flume supports a large set of source and destination types to land data on Hadoop. Kafka supports large sets of publishers and subscribers and multiple applications.On the other hand, Kafka makes data available even in case of single-point failure. Hence, when a Flume agent crashes, access to those events in the channel is lost till the disk is recovered. Kafka replicates data in the cluster, whereas Flume does not replicate events. Apache Kafka and Flume are highly reliable when configured correctly, with zero data loss guarantees.Kafka can process and monitor data in distributed systems, whereas Flume gathers data from distributed systems to land data on a centralized data store.Kafka can support data streams for multiple applications, whereas Flume is specific for Hadoop and big data analysis.Contrarily, Flume is a special-purpose tool for sending data into HDFS. However, Kafka is a more general-purpose system where multiple publishers and subscribers can share multiple topics. Apache Kafka and Flume systems provide reliable, scalable, and high-performance systems for easily handling large volumes of data. ![]() The differences between Apache Kafka vs Flume are explored here: Key Differences Between Apache Kafka vs Flume When landed in Hadoop, these data can be analyzed by running interactive queries in Apache Hive or serve as real-time data for business dashboards in Apache HBase. For instance, data streams include application logs, sensors, machine data, social media, etc. Enterprises leverage Flume’s capabilities to manage high-volume data streams to land in HDFS. 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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