Big Data Analytics Beyond Hadoop (Record no. 44144)

MARC details
000 -LEADER
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789332540361
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74 AGN
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Agneeswaran, Vijay Srinivas
245 ## - TITLE STATEMENT
Title Big Data Analytics Beyond Hadoop
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc PEARSON EDUCATION
Place of publication, distribution, etc Noida, Uttar Pradesh
Date of publication, distribution, etc 2015
300 ## - PHYSICAL DESCRIPTION
Extent 216
520 ## - SUMMARY, ETC.
Summary, etc Big Data Analytics Beyond Hadoop is the first guide specifically designed to introduce these technologies and demonstrate their use in detail. An indispensable resource for data scientists and others who must scale traditional analytics tools and applications to Big Data, it illuminates these new alternatives at every level, from architecture all the way down to code. Dr. Vijay Srinivas Agneeswaran shows how to evaluate and choose the right tools and then reengineer your solutions and products to work far more effectively in Big Data environments. Agneeswaran explains the Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management and the analysis of both performance and accuracy. He presents realistic use cases and up-to-date example code for:. Spark, the next generation in-memory computing technology from UC Berkeley. Storm, the parallel real-time Big Data analytics technology from Twitter. GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Agneeswaran offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs and even Big Data governance, security and privacy issues. To position you for tomorrow's advances, he identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Science
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Topical term or geographic name as entry element Big Data
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Topical term or geographic name as entry element BDAS
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Topical term or geographic name as entry element Spark
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Topical term or geographic name as entry element Machine Learning
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Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book

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