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|a Big Data Glossary.
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|a Sebastopol :
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|a Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Chapter 1. Terms; Document-Oriented; Key/Value Stores; Horizontal or Vertical Scaling; MapReduce; Sharding; Chapter 2. NoSQL Databases; MongoDB; CouchDB; Cassandra; Redis; BigTable; HBase; Hypertable; Voldemort; Riak; ZooKeeper; Chapter 3. MapReduce; Hadoop; Hive; Pig; Cascading; Cascalog; mrjob; Caffeine; S4; MapR; Acunu; Flume; Kafka; Azkaban; Oozie; Greenplum; Chapter 4. Storage; S3; Hadoop Distributed File System; Chapter 5. Servers; EC2; Google App Engine.
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|a Elastic BeanstalkHeroku; Chapter 6. Processing; R; Yahoo! Pipes; Mechanical Turk; Solr/Lucene; ElasticSearch; Datameer; BigSheets; Tinkerpop; Chapter 7. NLP; Natural Language Toolkit; OpenNLP; Boilerpipe; OpenCalais; Chapter 8. Machine Learning; WEKA; Mahout; scikits.learn; Chapter 9. Visualization; Gephi; GraphViz; Processing; Protovis; Fusion Tables; Tableau; Chapter 10. Acquisition; Google Refine; Needlebase; ScraperWiki; Chapter 11. Serialization; JSON; BSON; Thrift; Avro; Protocol Buffers.
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|a To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools. Descriptions are based on first-hand experience with these tools in a production environment. This handy glossary also includes a chapter of key terms that help define many of these tool categories:NoSQL Databases--Document-oriented databases using a key/value interface rather than SQLMapReduce--Tools that support distributed computing on large datasetsStorage--Technologies for storing d.
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|a Warden, Pete.
|t Big Data Glossary.
|d Sebastopol : O'Reilly Media, ©2011
|z 9781449314590
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