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Spring Data by Michael Hunger, Jon Brisbin, Thomas Risberg, Oliver Gierke, Mark Pollack

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Chapter 13. Creating Big Data Pipelines with Spring Batch and Spring Integration

The goal of Spring for Apache Hadoop is to simplify the development of Hadoop applications. Hadoop applications involve much more than just executing a single MapReduce job and moving a few files into and out of HDFS as in the wordcount example. There is a wide range of functionality needed to create a real-world Hadoop application. This includes collecting event-driven data, writing data analysis jobs using programming languages such as Pig, scheduling, chaining together multiple analysis jobs, and moving large amounts of data between HDFS and other systems such as databases and traditional filesystems.

Spring Integration provides the foundation to coordinate event-driven activities—for example, the shipping of logfiles, processing of event streams, real-time analysis, or triggering the execution of batch data analysis jobs. Spring Batch provides the framework to coordinate coarse-grained steps in a workflow, both Hadoop-based steps and those outside of Hadoop. Spring Batch also provides efficient data processing capabilities to move data into and out of HDFS from diverse sources such as flat files, relational databases, or NoSQL databases.

Spring for Apache Hadoop in conjunction with Spring Integration and Spring Batch provides a comprehensive and consistent programming model that can be used to implement Hadoop applications that span this wide range of functionality. Another product, Splunk, also requires ...

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