000			03234cam a2200277zu 4500
001			88853260
003			FRCYB88853260
005			20250107225322.0
006			m o d
007			cr un
008			250107s2015 fr | o|||||0|0|||eng d
020			_a9781783987665
035			_aFRCYB88853260
040			_aFR-PaCSA _ben _c _erda
100	1		_aGupta, Sumit
245	0	1	_aLearning Real-time Processing with Spark Streaming _c['Gupta, Sumit']
264		1	_bPackt Publishing _c2015
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aGupta, Sumit
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88853260 _qtext/html _a
520			_aBuilding scalable and fault-tolerant streaming applications made easy with Spark streamingAbout This BookProcess live data streams more efficiently with better fault recovery using Spark StreamingImplement and deploy real-time log file analysisLearn about integration with Advance Spark Libraries – GraphX, Spark SQL, and MLib.Who This Book Is ForThis book is intended for big data developers with basic knowledge of Scala but no knowledge of Spark. It will help you grasp the basics of developing real-time applications with Spark and understand efficient programming of core elements and applications.What You Will LearnInstall and configure Spark and Spark Streaming to execute applicationsExplore the architecture and components of Spark and Spark Streaming to use it as a base for other librariesProcess distributed log files in real-time to load data from distributed sourcesApply transformations on streaming data to use its functionsIntegrate Apache Spark with the various advance libraries like MLib and GraphXApply production deployment scenarios to deploy your applicationIn DetailUsing practical examples with easy-to-follow steps, this book will teach you how to build real-time applications with Spark Streaming.Starting with installing and setting the required environment, you will write and execute your first program for Spark Streaming. This will be followed by exploring the architecture and components of Spark Streaming along with an overview of libraries/functions exposed by Spark. Next you will be taught about various client APIs for coding in Spark by using the use-case of distributed log file processing. You will then apply various functions to transform and enrich streaming data. Next you will learn how to cache and persist datasets. Moving on you will integrate Apache Spark with various other libraries/components of Spark like Mlib, GraphX, and Spark SQL. Finally, you will learn about deploying your application and cover the different scenarios ranging from standalone mode to distributed mode using Mesos, Yarn, and private data centers or on cloud infrastructure.Style and approachA Step-by-Step approach to learn Spark Streaming in a structured manner, with detailed explanation of basic and advance features in an easy-to-follow Style. Each topic is explained sequentially and supported with real world examples and executable code snippets that appeal to the needs of readers with the wide range of experiences.
999			_c69567 _d69567