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Author Guller, Mohammed, author.

Title Big data analytics with Spark : a practitioner's guide to using Spark for large-scale data processing, machine learning, and graph analytics, and high-velocity data stream processing / Mohammed Guller.

Publication Info. [Berkeley, CA] : Apress, 2015.
New York, NY : Distributed to the Book trade worldwide by Springer
2015

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Location Call No. OPAC Message Status
 Axe Books 24x7 IT E-Book  Electronic Book    ---  Available
Description 1 online resource (xxiii, 277 pages) : illustrations.
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Series The expert's voice in Spark
Bibliography Includes bibliographical references and index.
Note Online resource; title from PDF title page (SpringerLink, viewed January 8, 2016).
Summary Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You?ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost?possibly a big boost?to your career.
Contents At a Glance; Contents; About the Author; About the Technical Reviewers; Acknowledgments; Introduction; Chapter 1: Big Data Technology Landscape; Hadoop; HDFS (Hadoop Distributed File System); MapReduce; Hive; Data Serialization; Avro; Thrift; Protocol Buffers; SequenceFile; Columnar Storage; RCFile; ORC; Parquet; Messaging Systems; Kafka; ZeroMQ; NoSQL; Cassandra; HBase; Distributed SQL Query Engine; Impala; Presto; Apache Drill; Summary; Chapter 2: Programming in Scala; Functional Programming (FP); Functions; First-Class; Composable; No Side Effects; Simple.
Immutable Data Structures Everything Is an Expression; Scala Fundamentals; Getting Started; Basic Types; Variables; Functions; Methods; Local Functions; Higher-Order Methods; Function Literals; Closures; Classes; Singletons; Case Classes; Pattern Matching; Operators; Traits; Tuples; Option Type; Collections; Sequences; Array; List; Vector; Sets; Map; Higher-Order Methods on Collection Classes; map; flatMap; filter; foreach; reduce; A Standalone Scala Application; Summary; Chapter 3: Spark Core; Overview; Key Features; Easy to Use; Fast; General Purpose; Scalable.
Fault Tolerant Ideal Applications; Iterative Algorithms; Interactive Analysis; High-level Architecture; Workers; Cluster Managers; Driver Programs; Executors; Tasks; Application Execution; Terminology; How an Application Works; Data Sources; Application Programming Interface (API); SparkContext; Resilient Distributed Datasets (RDD); Immutable; Partitioned; Fault Tolerant; Interface; Strongly Typed; In Memory; Creating an RDD; parallelize; textFile; wholeTextFiles; sequenceFile; RDD Operations; Transformations; map; filter; flatMap; mapPartitions; union; intersection; subtract.
Distinctcartesian; zip; zipWithIndex; groupBy; keyBy; sortBy; pipe; randomSplit; coalesce; repartition; sample; Transformations on RDD of key-value Pairs; keys; values; mapValues; join; leftOuterJoin; rightOuterJoin; fullOuterJoin; sampleByKey; subtractByKey; groupByKey; reduceByKey; Actions; collect; count; countByValue; first; max; min; take; takeOrdered; top; fold; reduce; Actions on RDD of key-value Pairs; countByKey; lookup; Actions on RDD of Numeric Types; mean; stdev; sum; variance; Saving an RDD; saveAsTextFile; saveAsObjectFile; saveAsSequenceFile; Lazy Operations.
Action Triggers Computation Caching; RDD Caching Methods; cache; persist; RDD Caching Is Fault Tolerant; Cache Memory Management; Spark Jobs; Shared Variables; Broadcast Variables; Accumulators; Summary; Chapter 4: Interactive Data Analysis with Spark Shell; Getting Started; Download; Extract; Run ; REPL Command s; Using the Spark Shell as a Scala Shell ; Number Analysis ; Log Analysis; Summary; Chapter 5: Writing a Spark Application; Hello World in Spark; Compiling and Running the Application; sbt (Simple Build Tool); Build Definition File; Directory Structure.
Subject SPARK (Electronic resource)
SPARK (Electronic resource) (OCoLC)fst01400497
Big data.
Data mining.
COMPUTERS -- Database Management -- Data Mining.
COMPUTERS -- Machine Theory.
MATHEMATICS -- General.
Big data. (OCoLC)fst01892965
Data mining. (OCoLC)fst00887946
Public administration.
Databases.
Genre/Form Electronic books.
Other Form: Print version: Guller, Mohammed. Big data analytics with Spark. [Berkeley, CA] : Apress, 2015 1484209656 9781484209653 (OCoLC)921240040
ISBN 9781484209646 (electronic bk.)
1484209648 (electronic bk.)
9781484209653
1484209656
Standard No. 10.1007/978-1-4842-0964-6 doi
CHNEW 000894098
CHVBK 374532540
DEBBG BV043627917
DEBSZ 454930623
NZ1 16308280
AU@ 000057235670
UKMGB 019139858

 
    
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