data transfer in hadoop Apache flume

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Apache Flume is commonly used in Hadoop ecosystems to efficiently transfer large amounts of data from various sources to Hadoop Distributed File System (HDFS) or other storage systems. Flume's data transfer process involves three main steps:

  1. Ingestion: Flume's sources collect data from external sources, such as log files or message queues. Flume has a variety of built-in sources, including HTTP, syslog, and netcat, which can be used to ingest data in different formats.

  2. Aggregation: Flume channels buffer incoming data, ensuring that data transfer is optimized for high throughput and low latency. Flume channels provide a reliable and fault-tolerant mechanism for storing data before it is sent to the destination.

  3. Delivery: Flume sinks transfer the data from the channels to the destination, such as HDFS or HBase. Flume supports multiple sinks that can be used to transfer data to various destinations. The choice of sink depends on the nature of the data and the specific requirements of the use case.

In addition, Flume provides a flexible event routing mechanism that allows users to define complex routing rules for data flow within their data pipelines. This allows users to route data to different sinks based on various criteria, such as the data source, data type, and content.