Big Data, Big Opportunity: A Primer for Understanding The Big Data Frontier

BigData,BigOpportunityAPrimerforUnderstandingTheBigDataFrontier

SanjaiMarimadaiah

Mainframe

CATechnologiesProductManagement,OfficeoftheCTO,BigDataManagementMFX01E

@SanjaiM1#CAWorld

MichaelHarer @MikeHarer Hiren Mandalia @hiren0210

2 ©2015CA.ALLRIGHTSRESERVED.@CAWORLD #CAWORLD

Abstract

BigDataenvironmentsnowarebusiness-criticalforanyorganization.LearnthebasicsofBigDataandsomeoftheemergingtechnologiestargetingtheBigDataspace

SanjaiMarimadaiah

MichaelHarer

Hiren MandaliaCATechnologiesProductManagementOfficeoftheCTOBigDataManagement


Agenda

WHATISBIGDATA?

BIGDATAUSECASES

HADOOPBASICS

1

2

3

NOSQL BASICS4

CASSANDRABASICS5

MONGODB BASICS6


HowdoIdeliveraflawlessexperienceeverytimeanapplicationtouchesthemainframe?

Intheapplicationeconomyit’sallaboutyourcustomers.Youneedtothinkaboutyourmainframereframed.

Connectmobile-to-mainframeapplications

Createmainframeinfrastructureflexibility

forthefuture

Unleashthepowerofdataonthemainframe



WhatisBigData?

Datasetswhosevolume,velocity,varietyandcomplexityexceedabilityofcommonlyusedsoftwaretoolstocapture,process,store,manage,andanalyzethem.

Information Sources

MobileTransactionalData

SearchTextsCRM,SCM,ERP

$ € ¥

ImagesEmail SocialMedia

ITOps AudioVideo

Velocity Volume

Variety Complexity

BigData


EvolutionofDataManagementSolutionsRelationalDatabasesarenotsuitedforBigData

HierarchicalDataModels

RelationalDataModels

1960 1970 1980 1990 2000 2010

DocumentDataModels

Structured DataUnstructured Data

IBMIMS

SybaseInformixOracleIBM

GoogleHadoop


StateofDatabaseWorkloadsBigDataworkloadsenablebroaderOLAPworkloads

Database- RDBMSOnline TransactionProcessing

DataWarehouseOnlineAnalyticalProcessing

BigDataBigDataWorkloads

BetterAnalyticsforhighervaluetransactions

Collecthistoricaltransactionaldataforanalytics

Addingmorecompletedataenhances analytics

Enhancedinsightsfromoperationalworkloads&

informationaccessapplications

Multimedia

WebLogs

SocialData

Sensordata:images

RFID

TextData:emails


WhatisdrivingBigDataSolutionsCostefficiencyandStandardizedPlatformisfosteringinnovation

Scale-OutArchitecture Open-SourceSoftware

• Protects Investment : Just add more servers to expand capacity

• Lower cost of Infrastructure: Less expensive commodity servers (x86 based)

• Standardization leads to Innovation: A common programing interface is enabling innovation up the SW stack

• Lower software cost: Open source software is lowering software cost

100’s of inexpensive servers

HadoopCassandra


AdoptionofBigDataSolutions

2X INCREASEinnumberoforganizationsthathavedeployed/implementeddatadrivenprojectssince2014

KeyTrends• Greaterpriorityonstructureddatainitiatives

• Topvendorcriteria- Integrationwithexistinginfrastructure

- Security- EaseofUse

• Necessaryskill sets:BusinessAnalysts,DataArchitects,DataAnalysts&DataVisualizers

40% oforganizationsarestillplanningtoimplementdataprojects

oforganizationsarestillplanningtoimplementdataprojects30%

Source:2015CASponsoredResearch:Vanson Bourne GlobalBigDataUserSurvey


OverallBigDataMarket

§ TheBigDatamarketwas$27.36Bin2014,upfrom$19.6Bin2013.

§ 89%ofbusiness leadersbelieveBigDatawillrevolutionizebusinessopsthesamewaytheInternetdid.

§ 83%havepursuedBigDataprojectsinordertoseizeacompetitiveedge.

Wikibon projectstheBigDatamarketwilltop$84Bin2026,attaininga17%Compound AnnualGrowthRate(CAGR)fortheforecastperiod2011to2026.

Source:2015Wikibon BigDataMarketForecast


DatabaseforBigDataOverallBigDatadatabasemarkettoprojectedtogrowat33%CAGRuntil2017

Source:©Wikibon BigDataModel2011-2017,BigDataMarketDatabase Projection,2011-2017($USbillions)

• BigDatadatabasemarketwillgrowatapprox.60%from2011-2017(6-year)

• MarketforNoSQLdatabasewas$0.2Bin2012,growingto$1.6Bin2017.

• Technologyprogression inData-in-DRAM-MemoryandData-in-Flash-Memorywillimprovethescalability ofSQLdatabases.

• Applications areeasiertoprogramandrequirelowermaintenanceifSQLisused;NoSQLhasgreaterscalabilityandlowertechnologycostsforverylargebig-dataapplications.

Source:2015Wikibon BigDataModel2011-2017


VendorLandscape– BroaderParticipantsBIGDATAMARKETSEGMENT

HARDWARESERVERS(CHIPS) STORAGE NETWORKING

HP EMC/Dell CiscoDell NetApp AristaNetworksIntel Fusion-io Infeineta Systems

SOFTWAREHADOOP NOSQL *NGDW ANALYTICS &BI Management Solutions

Hortonworks Cassandra HP Vertica DigitalReasoning CABigDataControlCenter

Informatica

Cloudera MongoDB EMCGreenplum RevolutionAnalytics Vmware IBM BigInsights

MapR Couchbase TeradataAster Jaspersoft HPHAVEn ZettasetHadapt DataStax IBMNetezza Dataeet BluedataEPIC Syncsort

EMCGreenplum 10gen SAP Pentaho StackIQ BMC Control-M

SERVICESCLOUD SERVICES TECHNICAL SERVICES PROFESSIONALSERVICES

Amazon Hortonworks ThinkBigAnalyticsGoogle Cloudera IBMMapR Cloudwick EMCIBM EMC Accenture

Microsoft IBM Deloitte

*NGDW=NextGenerationDataWarehouse

CoreInfrastructureHadoopCassandraMongoDB AmazonBigDataMAPRElasticSearch

BigDataUseCaseStudies


Media&EntertainmentUseCasePROBLEM SOLUTION POTENTIALBENEFITS§ Acompany’s streamingbusiness

hasexpandedfromthousandsofmemberswatchingoccasionallytomillionsofmemberswatchingovertwobillionhourseverymonth.

§ Acollectionofeventsdescribingwhat isbeing viewedmust begathered. Giventhatviewingiswhatmembersspendmostoftheirtimedoing,what’sneededisarobustandscalablearchitecturetomanageandprocessthis.

§ Certain thingswillbreakthearchitecturethatprocessesbillionsofviewing-relatedeventsperday.

§ Focusontheminimumviablesetofusecases

§ Availabilityoverconsistency- ourprimaryusecasescantolerateeventuallyconsistentdata,sodesignfromthestartfavoringavailabilityratherthanstrongconsistencyinthefaceoffailures.

§ Byfocusingontheminimumviablesetofusecases,ratherthanbuildingagenericall-encompassingsolution,wehavebeenabletobuildasimplearchitecturethatscales.

§ The company’sviewingdataarchitectureisdesignedforavarietyofusecases,rangingfromuserexperiencestodataanalytics.Thefollowingarethreekeyusecases,allofwhichaffecttheuserexperience:


HealthCareUseCase

15

*SystemzVSAMdatabaserequiresspecialskillstoaccesswithoutvStorm ConnectDataStreamingforBigData

PROBLEM SOLUTION POTENTIALBENEFITS

§ Relapsesincardiacpatients§ “Onesizefitsall”

treatment§ Medicare readmission

penalties§ Sensitivepatientdataon

zSystemsVSAMfiles§ Noefficientwaytooffload

§ Identifyriskfactorsbyanalyzingpatientdata*

§ Factorsusedtopredictlikelyoutcomes

§ Reductioninreadmissions§ Savingsinnopenalty fees§ Nomanualintervention§ Noincrease instaffing


RetailUseCase

16

PROBLEM SOLUTION POTENTIALBENEFITS

§ Streamsofuserdatanotcorrelated

§ e.g.storepurchases,websiteusagepattern,cardusage,historicalcustomerdata

§ Historical customerdataSystemzVSAM&DB2based– noefficient,secureoffload

§ HDFSsecurelypopulatedwithhistoricalcustomerdata,cardusage,storepurchases,websitelogs

§ Splunk scorescustomersbasedonthevariousdatastreams

§ Highscoringcustomersofferedcoupons,specialdealsonwebsite

§ Increaseinonlinesalesinthemiddleofretailslowdown

§ Improved conversionrateofwebsitebrowsingcustomers(shoppingcarttosales)

§ Eliminationofdatasilos–sincenowanalyticscoveralldatanomorereliance onmultiple reports/formats

HadoopBasics


WhatisHadoop?

Hadoopis…open-sourcesoftwaredesignedforHighScalability,FaultTolerant andHighlyDistributed

Keyelements:1. Distributedprocessing ofBigData(e.g.MapReduce)2. Distributedstorage(HadoopDistributedFileSystemorHDFS)

HDFS(DistributedReliableStorage)

MapReduce(ResourceManagement

&DataProcessing)

HDFS(DistributedReliableStorage)

YARN(ResourceManagement)

MapReduce(Dist.Programming)

Hadoop1.0 Hadoop2.0

Spark(InMemory)

1

23

HBase

(NoSQLstore)

Hive(Query)

Pig(Scripting)

Oozie(Workflow)

45


MapReduce– CoreHadoop1

§ Hadoop’sMapReduceframeworkinvolvestwophases:1. MapPhase:Distributesdatasetamongmultiple serversand

operatesonthedatalocally.2. ReducePhase:Recombinesthepartialresults.

AdistributedcomputingFramework


MapReduce– CoreHadoop1

AdistributedcomputingFramework

• JobTracker-OneoftheCoreHadoopservices thatmanagesthejobs andtheresourcesinthecluster(tasktrackers).JobTrackertriestoschedule a“map”asclosetotheactualdatabeingprocessed.

• TaskTracker–deployedonthedatanodes andareresponsible forrunningthemapandreducetasksasinstructedbyjobtracker

JobTracker

Job-1

Job-2

Job-3

Job-4

Job-5

MR

Processeslargejobsinparallelacrossmanynodesandcombinestheresults.

245

125

134

235

134

DataNodes

TaskTrackers

MasterNode

SlaveNodes


Job-1

Job-2

Job-3

Job-4

Job-5

HDFS

DataNodes

TaskTrackers

HadoopDistributedFileSystem(HDFS)Self-healing,highbandwidthClusteredStorage

• NameNode-OneoftheCoreHadoopservicesthatmaintainsthenamespace–knowswheredataisandmanagesblocks ondatanodes

• DataNode- serves thatactualstorethedataintheirlocaldisks.

• SecondaryNameNode-performsperiodic checkpointofprimarynamenodetoserveasabackupincaseoffailure

SlaveNodes

245

125

134

235

134

HDFSbreaksincomingfilesintoblocksandstoresthemredundantlyacrossthecluster.

NameNode(primary)

NameNode(secondary)

MasterNode

PeriodicCheckpoint

2


YARN

YARNis…§ ResourceManagement§ NextgenerationMapReduce(MRv2)§ Splits JobTrackerinto:

– ResourceManager– Scheduling /Monitoring

3

WhatdoesYARNdo?§ Provides aclusterlevelresourcemanagerfor

improvedresourcemanagement&scaling§ Formsthenewsystem formanaging

applications inadistributedmanner§ Provides slotsforjobsotherthan

Map/Reduce§ Improvesresourceutilization ResourceManagementmovesintoYARN

YetAnotherResourceNegotiator


HBASE

Whatisit?§ AHadoopopen source(Java)NoSQLdatabase§ Provides real-timeread/writeaccesstothose

largedatasets§ Distributedwithautomaticfailover

Anon-relational(NoSQL)databasethatrunson topofHDFS

4

Whyuseit?§ Provides anaturaldatastoragemechanism forall

kinds ofdata(especially unstructured)§ Forrandom,realtimeaccesstodatainHadoop§ Whentheprojectgoalistohostverylargetables

i.e.billions ofrowsandmillions ofcolumns§ Combines datasources thatuseawidevarietyof

differentstructuresandschemas§ Greatfor: storingsemi-structureddatalikelogdata

HBase(NoSQLstore)

LogicalViewofCustomerContactInformationinHBase


Hive

Whatisit?§ AqueryenginewrapperbuiltonMapReduce§ TreatedasadatawarehousetoolfortheHadoop

ecosystem§ PrimarilyforuserswithSQLskills§ ProvidesHive=QL(similartoSQL)§ StoresdatainHDFS

ADataWarehouseinfrastructurebuiltonHadoop

5

Whyuseit?§ Dataanalysisandreportingpurposes§ HidesHadoopcomplexityfromendusers§ CanbeusedwithinanELTfunction– i.e.toconvert

StructuredQuerylanguagetounstructuredMapReducejobs torunonaHadoopcluster

§ Goodfor:BatchProcessing tasks:logs, textmining,documentindexing, customerBI)

§ Notgoodfor:Onlinetransactionprocessing, real-timequeries.

Hive(Query)


Cross-IndustryUseCase– ApacheHadoopELTPROBLEM SOLUTION BENEFITS§ Traditional DataWarehousing resourcesare

EXPENSIVE (e.g.transactionalMainframesystems)

§ Needtoreducecosts associatedtoStorage,CPUcapacityand3rd partyETLtools

§ Current systems cannotscale(i.e.process§ Lackefficient tools§ Toolstypicallyonlyhandlestructured data

(RDBMS)but BigDatainsightisderivedfromalltypesofdata(structured, unstructured, semi-structured

§ ApacheHadooptoolsto:

1. perform ETLfunctions

2. forhandlingallofthespecific datatypes.

3. Toshiftawayfromtraditional ETLtoELT(extract, load, andtransform).Thisshiftismainlydrivenbybigdata,whichfollowsthe“storefirst, analyzelater”modelthatisbecomingthenewstandard.

§ Compared totraditional transactional systems,Hadoopprovidesfast,low-cost processing

§ Newvaluecanbederivedfromability tohandlestructured andnon-structured data

§ Greater flexibility &choice:e.g.theTransformfunction canuseMapReduce,Hive,Pig,R,ShellScripts, Java…etc.

§ Vastsupport model:opensourcedevelopercommunity

ExtractTransform

Load

Load

Load

DWH

DataMining

Reporting

OLAP Analysis

Traditional ETLProcess

Web

CRM

ERP

Web

CRM

ERP

Social Media

Sensor Logs

Structured

Unstructured

Flume

Sqoop

Extract/Load

DataMining

Reporting

AnalyticsHDFS

HadoopDistributedFileSystem

PigMapReduce

Hive

Transform


Pureopensource– OpenCore– Compatible

CommercialDistributionsofHadoop

Cloudera Hadoop

HDFS OOZIE

Hortonworks

MAPR

Apache


TheEvolvingHadoopEcosystemComponents Description

mahout RDataMining/machinelearningtoolsusedagainstHadoop datatodetectpatternsandtrends

PigScriptinglanguageforanalyzinglargedatasets.CompilestoMapReduce jobs

MapReduce YARNProgrammingmodelforprocessinglargedatasets.YARNperforms overall resourcemgmt

Oozie Aworkflowscheduler tooltomanageHadoop MapReduce jobs

Sqoop HiveEnableSQLforHadoop data:Sqoop - DatatransferbetweenHadoopandstructureddatastores.HIVE - datawarehouseforHadoop.Drill - opensource,lowlatencySQLqueryengineforHadoop andNoSQL.

Drill

ZooKeeperCoordinationofconfig.data,namingandsynchronizationofHadoop projects

Components Description

BigTopPackagingservicesforHadoopprojectstoeasetestinganddeployment

HBaseAnon-relational,distributeddatabasethatrunsontopofHDFS

Thrift /AVRO Schema-baseddata serializationsystemusingRPCcalls

Solrhutch Indexingandsearchtoolsfor

datastoredinHDFSforHadoopElasticsearch

Kafka /FlumeCollect,aggregate,andmovestreamingdatafrommultiplesourcesinto Hadoop

SparkAppDev toolfor Hadoop appscombiningbatch,streaming,andinteractiveanalytics

Anbari Chukwa Monitoring&ManagementofHadoop clustersandnodes

NoSQLBasics


NoSQL DatabasesOverview

§ Farbetterathandlingsemi-structuredandunstructureddata

§ Databaseconsistencyiscompromisedforavailabilityandeaseofpartitioning

§ Supportsobject-orientedprogrammingthatiseasytouseandflexible

§ Efficient,scale-outarchitectureinsteadofexpensive,monolithicarchitecture


NoSQLtypes

Type DatabaseexamplesColumnDataModel HBase,Cassandra, Accumulo

DocumentDataModel MongoDB

Key-ValueDataModel OpenTSDB,Redis

GraphDataModel Neo4j,ArangoDB

CassandraBasics


Cassandra– History

BigTable,2006 Dynamo,2007

OpenSource,2008

CassandraDSE– Dec2011

Google Amazon

Facebook

Datastax


CassandraisIdealFor…

§ Massive,linearscaling

§ Extremelyheavywrites

§ Highavailability

CERN Barracuda

CISCO BlueMountain

Comcast Netflix SoundCloud


Cassandra– DataModel

BenefitsofCassandraDataModel:§ Easilyaddnewcolumnswithoutdowntime

§ Schemafree/schemalessdatabase

§ Compressionpermitscolumnaroperations(MIN,MAX,SUMetc.)rapidly

ColumnFamily(similar toRDBMStable) ColumnFamily- JSONFormat


CassandraArchitecture

§ Allnodesthesame

§ Datapartitionedamongallnodesincluster

§ EachnodecommunicateswithothernodesusingGossipprotocol

§ Acommitlogisusedoneachnodetocapturewriteactivityfordatadurability

Client

Storage :CassandraFileSystemProcessing :CassandraQueryLanguage(CQL)


Cassandra– Keyfeatures

§ Nosinglepointoffailure

§ Multi-datacenterandzonesupport

§ Purepeer-to-peerclustersetup

§ Allowsfor“tunableconsistency”

§ CassandraQueryLanguage(CQL)

§ CassandraFileSystem(CFS)


CassandraatNetflix

Usecases:§ WhattitleshaveIwatched?§ Whattitlesarerecommendedforme?§ WheredidIleaveofflast?§ Whatelseisbeingwatched?§ Measurememberengagement§ Informproduct&contentdecisions

Solution:§ Captureall‘view’ eventsinscalable

Cassandraclusters

Challenges:§ Ability toscalebillionwriteevents/day§ Provideresponsive titlebrowsingexp.

Source:techblog.netflix.com

MongoDB Basics


2007Founded

2009MongoDB 1.0Open-sourced

2012MongoDB 2.0

2015MongoDB 3.0

2013MongoDB Inc.

10gen 10gen 10gen MongoDB MongoDB


MongoDB isidealfor…

§ RDBMSreplacementforWebApplications

§ Semi-structuredContentManagement

§ Real-timeAnalyticsandHigh-Speedlogging

§ CachingandHighScalability

Web2.0,Media,SAAS,Gaming

HealthCare,Finance, Telecom,Government

Notsogreatfor– HighTransactionalDatabases

DisneyEventbriteIntuitIGN

Craigslist


MongoDB – Datamodel

RDBMS Document-oriented

BenefitsofDocument-orientedDBMS:

• Databaseschemaisoptional

• Flexibleindealingwithchangeandoptionalvalues

{“streetnum”: “123”,“streetname”: “Main St.”,“unit”: “456”,“City”: “Mountain View”,“State”: “California”,“zip”: “65432”}

{“streetnum”: “123”,“streetname”: “Main St.”,“unit”: “456”,“City”: “Mountain View”,“State”: “California”,“County”: “Santa Clara”“zip”: “65432”}Present

Future


MongoDB – Sharding


ShardedProductionClusterSetup

Imagesource:mongodb.org

§ Shards storethedata.Toprovidehighavailabilityanddataconsistency,inaproductionshardedcluster,eachshardisareplicaset

§ ReplicaSetAclusterofMongoDB serversthatimplementsmaster-slavereplicationandautomatedfailover

§ QueryRouters,or mongos instances,interfacewithclientapplicationsanddirectoperationstotheappropriateshardorshards.

§ Config servers storethecluster’smetadata.Thisdatacontainsamappingof thecluster’sdatasettotheshards.


MongoDB– KeyFeatures

§ ScalableHigh-PerformanceOpen-Source,Document-orienteddatabase

§ BuiltforSpeed

§ RichdocumentformatallowsforEasyReadability

§ FullindexsupportforHighPerformance

§ ReplicationandFailoverforHighAvailability

§ Auto-Sharding forEasyScalability

§ Map/ReduceforAggregation


MongoDB atCraigslist

Usecases:§ Createnewposts§ Browseallmyposts§ Allowforpostclassification§ Searchrelevantposts

Solution:§ MigratefromMySQLtoMongoDB

Challenges:§ Archivebillions ofrecordsinmultiple formats§ Query/reportonarchivesatruntime§ Needcontinuous availabilitymandatedfor

regulatorycompliance§ Support 700sitesin70differentcountries

CraigslistEnvironment

• 5Billiondocuments• Avg Size:2KB• 3Replicasets/3serverseach• 2Datacenters• Sharding key– PostingID

Closing


CABigDataControlCenter– Vision

Bringefficiencytoroot-causeanalysis atalllevelsofBigDatasolution stack

SimplifymanagementbyabstractingthecomplexitiesofunderlyingBigDataTechnologies

HolisticallymeettheneedsofDevOpsbymanagingthelifecycleofApplications,DataandServices

BigDataTechnologies

LOB/BizAnalysts

AppDev./DataSci.

DataEng./DataAdmin

ITOps/ITMgmt.

BigData/SysAdmin

PrimaryPersonas

1

2

3

SecondaryPersona

End-to-EndManagementofBigDataEnvironments fortheApplicationEconomy

Application

Data Services

DataSources

ITSolutions CABigDataControlCenter


ManageBigDataWithAUnifiedView

JobMonitoring

HeterogeneousSystemManagement

IntelligentAlertManagement

ResourceReporting

Cluster/Job/NodeManagement


UnifiedView– Details


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ForInformationalPurposesOnlyTermsofthisPresentation

©2015CA.Allrightsreserved.Alltrademarksreferencedhereinbelongtotheirrespectivecompanies.Thepresentationprovided atCAWorld2015isintendedforinformationpurposesonlyanddoesnotformanytypeofwarranty.Someofthespecificslideswith customerreferences relatetocustomer'sspecificuseandexperienceofCAproductsandsolutionssoactualresultsmayvary.

CertaininformationinthispresentationmayoutlineCA’sgeneralproductdirection.Thispresentationshallnotserveto(i)affecttherightsand/orobligationsofCAoritslicenseesunderanyexistingorfuturelicenseagreement orservicesagreementrelatingtoanyCAsoftwareproduct;or(ii)amendanyproductdocumentationorspecificationsforanyCAsoftwareproduct.Thispresentationisbasedon currentinformationandresourceallocationsasofNovember18,2015,andissubjecttochangeorwithdrawalbyCAatanytimewithoutnotice.Thedevelopment,release andtimingofanyfeaturesorfunctionalitydescribedinthispresentationremainatCA’ssolediscretion.

Notwithstandinganythinginthispresentationtothecontrary,uponthegeneralavailabilityofanyfutureCAproductrelease referenced inthispresentation,CAmaymakesuchrelease availabletonewlicenseesintheformofaregularlyscheduledmajorproductrelease.SuchreleasemaybemadeavailabletolicenseesoftheproductwhoareactivesubscriberstoCAmaintenanceandsupport,onawhen andif-availablebasis.Theinformationinthispresentationisnotdeemedtobeincorporatedintoanycontract.


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