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010   $a981-13-0550-1
024 7 $a10.1007/978-981-13-0550-4
035   $a(CKB)4100000004835584
035   $a(DE-He213)978-981-13-0550-4
035   $a(MiAaPQ)EBC5431158
035   $a(EXLCZ)994100000004835584
100   $a20180616d2019      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBig Data Processing Using Spark in Cloud /$fedited by Mamta Mittal, Valentina E. Balas, Lalit Mohan Goyal, Raghvendra Kumar
205   $a1st ed. 2019.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019.
215   $a1 online resource (XIII, 264 p. 89 illus., 62 illus. in color.) 
225 1 $aStudies in Big Data,$x2197-6503 ;$v43 
311 08$a981-13-0549-8 
327   $aConcepts of Big Data and Apache Spark -- Big Data Analysis in Cloud and Machine Learning -- Security Issues and Challenges related to Big Data -- Big Data Security Solutions in Cloud -- Data Science and Analytics -- Big Data Technologies -- Data Analysis with Casandra and Spark -- Spin up the Spark Cluster -- Learn Scala -- IO for Spark -- Processing with Spark -- Spark Data Frames and Spark SQL -- Machine Learning and Advanced Analytics -- Parallel Programming with Spark -- Distributed Graph Processing with Spark -- Real Time Processing with Spark -- Spark in Real World -- Case Studies. .
330   $aThe book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data?s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments.
410  0$aStudies in Big Data,$x2197-6503 ;$v43 
606   $aBig data
606   $aComputer security
606   $aBig Data$3http://scigraph.springernature.com/things/product-market-codes/I29120
606   $aSystems and Data Security$3http://scigraph.springernature.com/things/product-market-codes/I28060
606   $aBig Data/Analytics$3http://scigraph.springernature.com/things/product-market-codes/522070
615  0$aBig data.
615  0$aComputer security.
615 14$aBig Data.
615 24$aSystems and Data Security.
615 24$aBig Data/Analytics.
676   $a005.7
702   $aMittal$b Mamta$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aBalas$b Valentina E$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aGoyal$b Lalit Mohan$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aKumar$b Raghvendra$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910739483403321
996   $aBig Data Processing Using Spark in Cloud$93553269
997   $aUNINA