Hadoop: The Definitive Guide Storage and Analysis at Internet Scale
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Beschreibung
Details
Einband
Taschenbuch
Erscheinungsdatum
05.05.2015
Verlag
O'Reilly and AssociatesSeitenzahl
728
Maße (L/B/H)
23.6/17.7/4.5 cm
Gewicht
1260 g
Auflage
4th edition
Sprache
Englisch
ISBN
978-1-4919-0163-2
Ready to unlock the power of your data? With the fourth edition of this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This edition includes new case studies, updates on Hadoop 2, a refreshed HBase chapter, and new chapters on Crunch and Flume. Author Tom White also suggests learning paths for the book.
* Store large datasets with the Hadoop Distributed File System (HDFS)
* 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
* Load data from relational databases into HDFS, using Sqoop
* Perform large-scale data processing with the Pig query language
* Analyze datasets with Hive, Hadoop’s data warehousing system
* Take advantage of HBase for structured and semi-structured data, and ZooKeeper for building distributed systems
You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This edition includes new case studies, updates on Hadoop 2, a refreshed HBase chapter, and new chapters on Crunch and Flume. Author Tom White also suggests learning paths for the book.
* Store large datasets with the Hadoop Distributed File System (HDFS)
* 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
* Load data from relational databases into HDFS, using Sqoop
* Perform large-scale data processing with the Pig query language
* Analyze datasets with Hive, Hadoop’s data warehousing system
* Take advantage of HBase for structured and semi-structured data, and ZooKeeper for building distributed systems
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