Big data glossary
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 envi...
| Autor principal: | |
|---|---|
| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing :
O'Reilly
[2011]
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628063406719 |
Tabla de Contenidos:
- 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
- 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
