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Hadoop Online Training course c

Online Bigdata Hadoop Training in USA also Hyderabad, RStrainings is providing classroom & Online Training on Hadoop Bigdata. Our Trainers are real time work experience with 12 years. We allocate Trainings on Hadoop globally UK, India, Aus, Canada, Saudi, Singapore

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Hadoop Online Training course c

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  1. www.rstrainings.com Contactus:-9052699906 HADOOPONLINETRAININGCOURSECONTENT: Duringthiscourse,youwilllearn:  IntroductiontoBigDataandAnalytics  IntroductiontoHadoop  Hadoopecosystem-Concepts  HadoopMap-reduceconceptsandfeatures  Developingthemap-reduceApplications  Pigconcepts  Hiveconcepts  Sqoopconcepts  FlumeConcepts  Oozieworkflowconcepts  ImpalaConcepts  HueConcepts  HBASEConcepts  ZooKeeperConcepts  RealLifeUseCases ReportingTool

  2. Tableau 1.Virtualbox/VMWare Basics Installations Backups Snapshots 2.Linux Basics Installations Commands 3.Hadoop WhyHadoop? Scaling DistributedFramework Hadoopv/sRDBMS Briefhistoryofhadoop 4.Setuphadoop Pseudomode Clustermode Ipv6 Ssh Installationofjava,hadoop Configurationsofhadoop HadoopProcesses(NN,SNN,JT,DN,TT) Temporarydirectory UI

  3. Commonerrorswhenrunninghadoopcluster,solutions 5.HDFS-HadoopdistributedFileSystem HDFSDesignandArchitecture HDFSConcepts InteractingHDFSusingcommandline InteractingHDFSusingJavaAPIs Dataflow Blocks Replica 6.HadoopProcesses Namenode Secondarynamenode Jobtracker Tasktracker Datanode 7.MapReduce DevelopingMapReduceApplication PhasesinMapReduceFramework MapReduceInputandOutputFormats AdvancedConcepts SampleApplications Combiner 8.JoiningdatasetsinMapreducejobs Map-sidejoin Reduce-Sidejoin

  4. 9.Mapreduce–customization CustomInputformatclass HashPartitioner CustomPartitioner Sortingtechniques CustomOutputformatclass 10.HadoopProgrammingLanguages:- I.HIVE Introduction InstallationandConfiguration InteractingHDFSusingHIVE MapReduceProgramsthroughHIVE HIVECommands Loading,Filtering,Grouping…. Datatypes,Operators….. Joins,Groups…. SampleprogramsinHIVE II.PIG Basics InstallationandConfigurations Commands…. OVERVIEWHADOOPDEVELOPER 11.Introduction 12.TheMotivationforHadoop Problemswithtraditionallarge-scalesystems Requirementsforanewapproach

  5. 13.Hadoop:BasicConcepts AnOverviewofHadoop TheHadoopDistributedFileSystem Hands-OnExercise HowMapReduceWorks Hands-OnExercise AnatomyofaHadoopCluster OtherHadoopEcosystemComponents 14.WritingaMapReduceProgram  TheMapReduceFlow  ExaminingaSampleMapReduceProgram  BasicMapReduceAPIConcepts  TheDriverCode  TheMapper  TheReducer  Hadoop’sStreamingAPI  UsingEclipseforRapidDevelopment  Hands-onexercise  TheNewMapReduceAPI 15.CommonMapReduceAlgorithms SortingandSearching Indexing MachineLearningWithMahout TermFrequency–InverseDocumentFrequency WordCo-Occurrence Hands-OnExercise.

  6. 16.PIGConcepts.. DataloadinginPIG. DataExtractioninPIG. DataTransformationinPIG. HandsonexerciseonPIG. 17.HiveConcepts.  HiveQueryLanguage.  AlterandDeleteinHive.  PartitioninHive.  Indexing.  JoinsinHive.Unionsinhive.  Industryspecificconfigurationofhiveparameters.  Authentication&Authorization.  StatisticswithHive.  ArchivinginHive.  Hands-onexercise 18.WorkingwithSqoop Introduction. ImportData. ExportData. SqoopSyntaxs. Databasesconnection. Hands-onexercise 19.WorkingwithFlume Introduction. ConfigurationandSetup.

  7. FlumeSinkwithexample. Channel. FlumeSourcewithexample. Complexflumearchitecture. 20.OOZIEConcepts 21.IMPALAConcepts 22.HUEConcepts 23.HBASEConcepts 24.ZooKeeperconcepts ReportingTool.. Tableau Thiscourseisdesignedforthebeginnertointermediate-levelTableauuser.Itisforanyonewhoworkswithdata–regardlessoftechnicaloranalyticalbackground.ThiscourseisdesignedtohelpyouunderstandtheimportantconceptsandtechniquesusedinTableautomovefromsimpletocomplexvisualizationsandlearnhowtocombinethemininteractivedashboards. CourseTopics Overview Whatisvisualanalysis? strengths/weaknessofthevisualsystem. LayingtheGroundworkforVisualAnalysis AnalyticalProcess Preparingforanalysis Getting,CleaningandClassifyingYourData Cleaning,formattingandreshaping. Usingadditionaldatatosupportyouranalysis. Dataclassification VisualMappingTechniques VisualVariables:BasicUnitsofDataVisualization WorkingwithColor

  8. Marksinaction:Commoncharttypes SolvingReal-WorldProblemswithVisualAnalysis GettingaFeelfortheData-ExploratoryAnalysis. Makingcomparisons Lookingat(co-)Relationships. Checkingprogress. SpatialRelationships. Try,tryagain. CommunicatingYourFindings Fine-tuningformoreeffectivevisualization Storytellingandguidedanalytics Dashboards OurOnlineServicesprovidingworldwidelikeAsia,Europe,America,Africa,Sweden,NorthKorea, SouthKorea,Canada,Netherland,Itely,Russia,Israel,NewZealand,Norway,Singapore,Malasia,etc

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