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University of Missouri, UBS
Ready to unleash the power of your massive dataset? With the latest edition of this comprehensive resource, you'll learn how to use Apache Hadoop to build and maintain reliable, scalable, distributed systems. It's ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
This third edition covers recent changes to Hadoop, including new material on the new MapReduce API, as well as version 2 of the MapReduce runtime (YARN) and its more flexible execution model. You'll also find illuminating case studies that demonstrate how Hadoop is used to solve specific problems.Store large datasets with the Hadoop Distributed File System (HDFS), then run distributed computations with MapReduce Use Hadoop's data and I/O building blocks for compression, data integrity, serialization (including Avro), and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud Use Pig, a high-level query language for large-scale data processing Analyze datasets with Hive, Hadoop's data warehousing system Load data from relational databases into HDFS, using Sqoop Take advantage of HBase, the database for structured and semi-structured data Use ZooKeeper, the toolkit for building distributed systems
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