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Big data processing using Spark in cloud

Mittal, Mamta.

Big data processing using Spark in cloud

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Big data processing using Spark in cloudedited by Mamta Mittal ... [et al.].
其他作者:	Mittal, Mamta.
出版者:	Singapore :Springer Singapore :2019.
面頁冊數:	xiii, 264 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Big data.
電子資源:	http://dx.doi.org/10.1007/978-981-13-0550-4
ISBN:	9789811305504$q(electronic bk.)

Big data processing using Spark in cloud
Big data processing using Spark in cloud[electronic resource] /edited by Mamta Mittal ... [et al.]. - Singapore :Springer Singapore :2019. - xiii, 264 p. :ill., digital ;24 cm. - Studies in big data,v.432197-6503 ;. - Studies in big data ;v.1..

Concepts 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.

The 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.

ISBN: 9789811305504$q(electronic bk.)

Standard No.: 10.1007/978-981-13-0550-4doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45 / B543 2019

Dewey Class. No.: 005.7

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520 $a The 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.
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