Big Data Glossary
Samenvatting
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 data in a distributed wayServers—Ways to rent computing power on remote machinesProcessing—Tools for extracting valuable information from large datasetsNatural Language Processing—Methods for extracting information from human-created textMachine Learning—Tools that automatically perform data analyses, based on results of a one-off analysisVisualization—Applications that present meaningful data graphicallyAcquisition—Techniques for cleaning up messy public data sourcesSerialization—Methods to convert data structure or object state into a storable format
Specificaties
Inhoudsopgave
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Chapter 1: Terms;
1.1 Document-Oriented;
1.2 Key/Value Stores;
1.3 Horizontal or Vertical Scaling;
1.4 MapReduce;
1.5 Sharding;
Chapter 2: NoSQL Databases;
2.1 MongoDB;
2.2 CouchDB;
2.3 Cassandra;
2.4 Redis;
2.5 BigTable;
2.6 HBase;
2.7 Hypertable;
2.8 Voldemort;
2.9 Riak;
2.10 ZooKeeper;
Chapter 3: MapReduce;
3.1 Hadoop;
3.2 Hive;
3.3 Pig;
3.4 Cascading;
3.5 Cascalog;
3.6 mrjob;
3.7 Caffeine;
3.8 S4;
3.9 MapR;
3.10 Acunu;
3.11 Flume;
3.12 Kafka;
3.13 Azkaban;
3.14 Oozie;
3.15 Greenplum;
Chapter 4: Storage;
4.1 S3;
4.2 Hadoop Distributed File System;
Chapter 5: Servers;
5.1 EC2;
5.2 Google App Engine;
5.3 Elastic Beanstalk;
5.4 Heroku;
Chapter 6: Processing;
6.1 R;
6.2 Yahoo! Pipes;
6.3 Mechanical Turk;
6.4 Solr/Lucene;
6.5 ElasticSearch;
6.6 Datameer;
6.7 BigSheets;
6.8 Tinkerpop;
Chapter 7: NLP;
7.1 Natural Language Toolkit;
7.2 OpenNLP;
7.3 Boilerpipe;
7.4 OpenCalais;
Chapter 8: Machine Learning;
8.1 WEKA;
8.2 Mahout;
8.3 scikits.learn;
Chapter 9: Visualization;
9.1 Gephi;
9.2 GraphViz;
9.3 Processing;
9.4 Protovis;
9.5 Fusion Tables;
9.6 Tableau;
Chapter 10: Acquisition;
10.1 Google Refine;
10.2 Needlebase;
10.3 ScraperWiki;
Chapter 11: Serialization;
11.1 JSON;
11.2 BSON;
11.3 Thrift;
11.4 Avro;
11.5 Protocol Buffers;
Net verschenen
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