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DataScienceonHadoop

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JustinEricksonSeniorDirector,ProductManagement

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AgeofMachineLearning

2

Costofcompute

Datavolume

Time

MachineLearning

NOMachineLearning

1950s 1960s 1970s 1980s 1990s 2000s 2010s

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TheEnterprisePlatformforDataScienceandMachineLearning

Thedataisnowhere

30BCONNECTEDDEVICES

440xMOREDATA

ClouderafirsttointegrateSpark

ModernPlatformforMachineLearningandAdvancedAnalytics

Leadingadoptionamongenterprises

500Customers

RunSparkon

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Sampledatascience/machinelearningworkflowFromdatatoexplorationtoaction

DataEngineering DataScience(Exploratory) Production(Operational)

DataWrangling

VisualizationandAnalysis

ModelTraining&Testing

ProductionDataPipelines BatchScoring

OnlineScoringServing

DataGovernanceGovernance

Processing

AcquisitionReports,

Dashboards

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Thegoodnews

DataEngineering DataScience(Exploratory) Production(Operational)

DataWrangling

VisualizationandAnalysis

ModelTraining&Testing

ProductionDataPipelines BatchScoring

OnlineScoringServing

DataGovernanceGovernance

Processing

AcquisitionReports,

Dashboards

Datahasneverbeenmoreplentiful

Opensourcedatascienceandmachinelearninglibrariesarerapidlyevolving

Commodity(andon-demand)computemakesscalableproductionmachinelearningaffordable

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Thebadnews

DataEngineering DataScience(Exploratory) Production(Operational)

DataWrangling

VisualizationandAnalysis

ModelTraining&Testing

ProductionDataPipelines BatchScoring

OnlineScoringServing

DataGovernanceGovernance

Processing

AcquisitionReports,

Dashboards

Mostdatasciencedoneatsmallscale,individually,andisdifficulttoreplicate

Veryfewmodelsreachproduction

Teamshavedifferent,conflictingrequestsforlanguages&libraries

Dataneedstomoveacrossmultipledifferentsystems

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Additionalchallenges

AccessForsensitivedata,secureclustersaredifficulttoaccess.AndITtypicallydoesn’twantrandompackagesinstalledonasecurecluster.

Popularopensourcetoolsdon’teasilyconnecttotheseenvironments,oralwayssupportHadoopdataformats.

ScaleLaptopsrarelyhavecapacityformedium,letalonebigdata.Thisleadstoalotofsampling.

Popularframeworksdon’teasilyparallelizeonacluster.Typicallycodehastogetrewrittenforproduction.

DeveloperExperienceNotebooks,whileawesome,don’teasilysupportvirtualenvironmentanddependencymanagement,especiallyforteams.Thismakessharingandreproducibilityhard.

Notebooksarealsochallengingto“putintoproduction.”

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Thisyear,ourgoalistoenabledatascienceandmachinelearningatscale.

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OpendatascienceintheenterpriseIT

driveadoptionwhilemaintainingcompliance

DataScientistexplore,experiment,iterate

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Ourgoal:Anopenplatformfordatascienceatscale

HelpmoredatascientistsusethepowerofHadoop

Useapowerful,familiarenvironmentwithdirectaccessto

Hadoopdataandcompute

DataScientistDataEngineer

Makeiteasyandsecuretoaddnewusers,usecases

Offersecureself-serviceanalyticsandafasterpathtoproductiononcommon,affordableinfrastructure

EnterpriseArchitectHadoopAdmin

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IntroducingCloudera DataScienceWorkbenchSelf-servicedatasciencefortheenterprise

Acceleratesdatasciencefromdevelopmenttoproductionwith:• Secureself-serviceenvironmentsfordatascientiststoworkagainstCloudera clusters• SupportforPython,R,andScala,plusprojectdependencyisolationformultiplelibraryversions• Workflowautomation,versioncontrol,collaborationandsharing

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Demo

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Datascientistscan:• UseR,Python,orScalafromawebbrowser,withnodesktopfootprint• Installanylibraryorframeworkwithinisolatedprojectenvironments• DirectlyaccessdatainsecureclusterswithSparkandImpala• Shareinsightswiththeirteamforreproducible,collaborativeresearch• Automateandmonitordatapipelinesusingbuilt-injobscheduling

ITcan:• Givetheirdatascienceteamthefreedomtoworkhowtheywant,whentheywant• Staycompliantwithout-of-the-boxsupportforfullplatformsecurity,especiallyKerberos• Runon-premisesorinthecloud,whereverdataismanaged

WithCloudera DataScienceWorkbench…

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SolvingDataScienceisaFull-StackProblem

• Supportunlimiteddata• Providesufficienttools forAnalysts• Providesufficienttools forDataScientists+DataEngineers

• Enablereal-timeusecases• Providedatagovernance• Providefull-stacksecurity• Deployinthecloud• Integratewithpartnertools• BeeasyforITtodeploy/maintain

ü Hadoopü Impala,Hive,Hueü Spark,DataScienceWorkbench

ü Kafka,SparkStreamingü Navigator+Partnersü Kerberos,Sentry,RecordService,

KMS/KTSü Cloudera Directorü RichEcosystemü Cloudera Manager +Director

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Theimportanceofanopenecosystem

OpenEcosystem BlackBox

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ThankyouThankYouJustinErickson

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