Massively Parallel Databases and MapReduce Systems - Couverture souple

Babu, Shivnath; Herodotou, Herodotos

 
9781601987501: Massively Parallel Databases and MapReduce Systems
Couverture souple
ISBN 10 : 1601987501 ISBN 13 : 9781601987501
Editeur : now publishers Inc, 2013

Synopsis

Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in many businesses, scientific and engineering disciplines, and government endeavors. Web clicks, social media, scientific experiments, and datacenter monitoring are among data sources that generate vast amounts of raw data every day. The need to convert this raw data into useful information has spawned considerable innovation in systems for large-scale data analytics, especially over the last decade. Massively Parallel Databases and MapReduce Systems addresses the design principles and core features of systems for analyzing very large datasets using massively-parallel computation and storage techniques on large clusters of nodes. It first discusses how the requirements of data analytics have evolved since the early work on parallel database systems. It then describes some of the major technological innovations that have each spawned a distinct category of systems for data analytics. Each unique system category is described along a number of dimensions including data model and query interface, storage layer, execution engine, query optimization, scheduling, resource management, and fault tolerance. It concludes with a summary of present trends in large-scale data analytics. Massively Parallel Databases and MapReduce Systems is an ideal reference for anyone with a research or professional interest in large-scale data analytics.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in many businesses, scientific and engineering disciplines, and government endeavors. Web clicks, social media, scientific experiments, and datacenter monitoring are among data sources that generate vast amounts of raw data every day. The need to convert this raw data into useful information has spawned considerable innovation in systems for large-scale data analytics, especially over the last decade. Massively Parallel Databases and MapReduce Systems addresses the design principles and core features of systems for analyzing very large datasets using massively-parallel computation and storage techniques on large clusters of nodes. It first discusses how the requirements of data analytics have evolved since the early work on parallel database systems. It then describes some of the major technological innovations that have each spawned a distinct category of systems for data analytics. Each unique system category is described along a number of dimensions including data model and query interface, storage layer, execution engine, query optimization, scheduling, resource management, and fault tolerance. It concludes with a summary of present trends in large-scale data analytics. Massively Parallel Databases and MapReduce Systems is an ideal reference for anyone with a research or professional interest in large-scale data analytics.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
now publishers Inc
Date d'édition
2013
Langue
anglais
ISBN 10 
1601987501
ISBN 13 
9781601987501
Reliure
Broché
Nombre de pages
120
Coordonnées du fabricant
non disponible
Personne responsable
non disponible