Stream data to Hadoop using Apache Flume
Integrate Flume with your data sources
Transcode your data en-route in Flume
Route and separate your data using regular expression matching
Configure failover paths and load-balancing to remove single points of failure
Utilize Gzip Compression for files written to HDFS
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Its main goal is to deliver data from applications to Apache Hadoop's HDFS. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with many failover and recovery mechanisms.
Apache Flume: Distributed Log Collection for Hadoop covers problems with HDFS and streaming data/logs, and how Flume can resolve these problems. This book explains the generalized architecture of Flume, which includes moving data to/from databases, NO-SQL-ish data stores, as well as optimizing performance. This book includes real-world scenarios on Flume implementation.
Apache Flume: Distributed Log Collection for Hadoop starts with an architectural overview of Flume and then discusses each component in detail. It guides you through the complete installation process and compilation of Flume.
It will give you a heads-up on how to use channels and channel selectors. For each architectural component (Sources, Channels, Sinks, Channel Processors, Sink Groups, and so on) the various implementations will be covered in detail along with configuration options. You can use it to customize Flume to your specific needs. There are pointers given on writing custom implementations as well that would help you learn and implement them.
By the end, you should be able to construct a series of Flume agents to transport your streaming data and logs from your systems into Hadoop in near real time.