LEADER 03901nam 2200469 450 001 9910798738503321 005 20200520144314.0 035 $a(CKB)3710000000885621 035 $a(Au-PeEL)EBL4699930 035 $a(CaPaEBR)ebr11350899 035 $a(CaONFJC)MIL958864 035 $a(OCoLC)974589491 035 $a(CaSebORM)9781785884696 035 $a(MiAaPQ)EBC4699930 035 $a(PPN)220197857 035 $a(EXLCZ)993710000000885621 100 $a20170301h20162016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aBig data analytics $ea handy reference guide for data analysts and data scientists to help obtain value from big data analytics using Spark on Hadoop clusters /$fVenkat Ankam 205 $a1st edition 210 1$aBirmingham, England :$cPackt Publishing,$d2016. 210 4$dİ2016 215 $a1 online resource (326 pages) $cillustrations 300 $aIncludes index. 311 $a1-78588-469-7 311 $a1-78588-970-2 330 $aA handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components ? Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components ? HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learni... 606 $aBig data$xSecurity measures 615 0$aBig data$xSecurity measures. 676 $a005.8 700 $aAnkam$b Venkat$01496819 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910798738503321 996 $aBig data analytics$93721701 997 $aUNINA