LEADER 03934nam 2200481 450 001 9910466092903321 005 20200520144314.0 035 $a(CKB)3710000000885621 035 $a(MiAaPQ)EBC4699930 035 $a(CaSebORM)9781785884696 035 $a(PPN)220197857 035 $a(Au-PeEL)EBL4699930 035 $a(CaPaEBR)ebr11350899 035 $a(CaONFJC)MIL958864 035 $a(OCoLC)974589491 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 608 $aElectronic books. 615 0$aBig data$xSecurity measures. 676 $a005.8 700 $aAnkam$b Venkat$0943309 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910466092903321 996 $aBig data analytics$92128910 997 $aUNINA LEADER 06778nam 22008175 450 001 996465425703316 005 20200705163949.0 010 $a3-540-30204-2 024 7 $a10.1007/b100480 035 $a(CKB)1000000000212551 035 $a(SSID)ssj0000098395 035 $a(PQKBManifestationID)11140409 035 $a(PQKBTitleCode)TC0000098395 035 $a(PQKBWorkID)10133861 035 $a(PQKB)10149110 035 $a(DE-He213)978-3-540-30204-9 035 $a(MiAaPQ)EBC3089042 035 $a(PPN)155229788 035 $a(EXLCZ)991000000000212551 100 $a20121227d2004 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in Databases and Information Systems$b[electronic resource] $e8th East European Conference, ADBIS 2004, Budapest, Hungary, September 22-25, 2004, Proceedings /$fedited by Georg Gottlob, Andras Benczur, Janos Demetrovics 205 $a1st ed. 2004. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2004. 215 $a1 online resource (XI, 426 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v3255 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-23243-5 320 $aIncludes bibliographical references and index. 327 $aConstraint Databases -- Quantifier-Elimination for the First-Order Theory of Boolean Algebras with Linear Cardinality Constraints -- Deductive Databases -- Update Propagation in Deductive Databases Using Soft Stratification -- Heterogenous and Web Information Systems -- Query Rewriting Using Views in a Typed Mediator Environment -- Reasoning About Web Information Systems Using Story Algebras -- Cross Enterprise Information Systems -- Component Framework for Strategic Supply Network Development -- Knowledge Discovery -- An Abstract Algebra for Knowledge Discovery in Databases -- Database Modelling -- Beyond Databases: An Asset Language for Conceptual Content Management -- Component-Based Modeling of Huge Databases -- Cognitive Load Effects on End User Understanding of Conceptual Models: An Experimental Analysis -- Template Based, Designer Driven Design Pattern Instantiation Support -- XML and Semistructured Databases -- A High-Level Language for Specifying XML Data Transformations -- Implementing a Query Language for Context-Dependent Semistructured Data -- Static Analysis of Structural Recursion in Semistructured Databases and Its Consequences -- Physical Database Design and Query Evaluation -- Catalogues from a New Perspective: A Data Structure for Physical Organisation -- Database Caching ? Towards a Cost Model for Populating Cache Groups -- Towards Quadtree-Based Moving Objects Databases -- A Content-Based Music Retrieval System Using Multidimensional Index of Time-Sequenced Representative Melodies from Music Database -- Solving Stochastic Optimization in Distributed Databases Using Genetic Algorithms -- Transaction Management and Workflow Systems -- ML-1-2PC: An Adaptive Multi-level Atomic Commit Protocol -- Making More Out of an Inconsistent Database -- Process Query Language: A Way to Make Workflow Processes More Flexible -- Triggering Replanning in an Integrated Workflow Planning and Enactment System -- Query Processing and Data Streams -- Grouped Processing of Relational Algebra Expressions over Data Streams -- Processing Sliding Window Join Aggregate in Continuous Queries over Data Streams -- Spatial Databases -- How to Integrate Heterogeneous Spatial Databases in a Consistent Way? -- Vague Spatial Data Types, Set Operations, and Predicates -- Agents and Mobile Systems -- Intelligent Multi-agent Based Database Hybrid Intrusion Prevention System -- Energy Efficient Transaction Processing in Mobile Broadcast Environments. 330 $aThis book constitutes the refereed proceedings of the 8th East European Conference on Advances in Databases and Information Systems, ADBIS 2004, held in Budapest, Hungary, in September 2004. 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