Pete Warden

Big Data Glossary (eBook, PDF)

A Guide to the New Generation of Data Tools

• Format: PDF

Produktbeschreibung

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 DatabasesDocument-oriented databases using a key/value interface rather than SQL
• MapReduceTools that support distributed computing on large datasets
• StorageTechnologies for storing data in a distributed way
• ServersWays to rent computing power on remote machines
• ProcessingTools for extracting valuable information from large datasets
• Natural Language ProcessingMethods for extracting information from human-created text
• Machine LearningTools that automatically perform data analyses, based on results of a one-off analysis
• VisualizationApplications that present meaningful data graphically
• AcquisitionTechniques for cleaning up messy public data sources
• SerializationMethods to convert data structure or object state into a storable format

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 62
• Erscheinungstermin: 13. September 2011
• Englisch
• ISBN-13: 9781449317522
• Artikelnr.: 54541599

Autorenporträt

A former Apple engineer, Pete Warden is the founder of OpenHeatMap, and writes on large-scale data processing and visualization.

Inhaltsangabe

Preface
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