Channels : It is used to hold data queues and serve as conduits in between sources and sinks, which is very useful when the incoming flow rate exceeds the outgoing data flow rate.Sources : It retrieve data and handover it to channels.To know and learn how Flume works within a Hadoop cluster, we required to know that Flume executes as one or more agents, and that every agent consists of three pluggable components: sources, channels, and sinks: Single Unit of the data that Flume processes is called an event a good example of an event is a log record. The data can be of any kind, but Flume is typically well-suited to handling log data, like the log data from web servers. In other words, Flume is designed and engineered for the continuous ingestion** of data into HDFS.
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Some amount of data volume that ends up in HDFS might land there through database load operations or other types of batch processes, but what if we want to capture the data that’s flowing in high-throughput data streams, for example application log data? Apache Flume is the widely popular standard way to do that with ease, efficiently, and safely.Īpache Flume is a top-level project from the Apache Software Foundation, works as a distributed system for aggregating and moving massive amounts of streaming data from various sources to a centralized data store.
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