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UNIFIED DATA ARCHITECTURE
02.14 EB 7805
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BIG DATA: TERADATA UNIFIED DATA ARCHITECTURE™ IN ACTION
BIG DATA: TERADATA UNIFIED DATA
ARCHITECTURE™ IN ACTION
UNIFIED DATA ARCHITECTURE
02.14 EB 7805
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BIG DATA: TERADATA UNIFIED DATA ARCHITECTURE™ IN ACTION
TABLE OF CONTENTS
2 What’sTeradata’sPerspectiveonBigDataanditsValue?
6 WhatistheTeradataUnifiedDataArchitecture™anditsValue?
11 HowdoesTeradataUnifiedDataArchitecture™Work?
15 HowcanMyCompanygetStarted?
16 Summary
WHAT’S TERADATA’S PERSPECTIVE ON BIG DATA AND ITS VALUE?
Teradatabelievesbigdataisreal,hasvalue,andwillchangethespeedandqualityofbusinessinsights.Wealsobelieveitisanevolution,notarevolution,andthatincorporatingbigdataintoyourexistingITframeworkisfairlystraightforward,buttherearepitfallswecanhelpyoueasilyavoid.
Is Big Data Real?
Yes.FromaTeradataperspective,bigdataisaphenomenonweaddressedwithourcustomersaspartofourongoingproductinnovationprocessoverthepasttwoyears.FromaStrengths,Weaknesses,Opportunities,Threats(SWOT)perspective,wewereabletoturnbigdatafromathreatintoanopportunity.TheopportunityisforourcustomerstobeattheircompetitorsbyleveragingTeradataexpertisetostayaheadoftechnologytrends.Itmeantmakingsomeproductacquisitions(suchasAsterData),creatingnewpartnerships(Hortonworks,forinstance)anddoingaconsiderableamountofproductandservicesintegrationwork.Thebigdatatrendfitswiththemulti-yearanalyticaleco-systemevolutionofour“centralizeddatawarehouse”approachtodatawarehousing.Werecognizethatthereareavarietyofcost-effectivebest-of-breeddataandcomputationplatforms,andthatourprimaryvaluetoourcustomersistoprovideflexible,well-integratedoptionsforcaptur-ingdata,generatinginsight,andtakingaction.
Does Big Data have Value?
Yes,butsofarit’smostly“atthemargin,”meaningtheanalysisofnewdatasourcesandtypescanimprovewhatyouarealreadydoing.BasedonTeradatacustomeruses,weseethreebroadcategories—customerexpe-rience/service,operationalefficiency,andcorporatemarketing1—thatcanbemade5to20percentbetterbyexploitingbigdatatogleandeeperinsights.Gartner2surveyed720ITprofessionalsacrossindustriesinearly2013andaskedwhattopthreebusinessareastheirbigdataprojectstargeted.TheirresultsareshowninFigure1.
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Is Big Data an Evolution or a Revolution?
Whilesomevendorsgooutoftheirwaytoover-hypethistechnologyasrevolutionary,webelievebigdataisanaturalevolutionofseveraltrendsalreadyhappeningwithtraditionaldata.Specifically,threetrendswithbigdatastaythesame:
~ Same #1. The goal of using data and analytics as a competitive weapon.
IthasbeenprovenbyTomDavenportandMIT3,4thatcompanieswithadata-decisioncultureare5to6per-centmoreproductive,withassociatedimprovementsinassetutilization,ROE,andmarketvalue.Butismoredatathegoal?No.Whilesizeisn’treallyaconcernsinceTeradatawasarchitectedformassiveamountsofdataandparallelcomputation,werecognizedyearsagothatalldataisnotcreatedequal.Theplacementofdataonthemostcost-effectivelayersofastoragehier-archyiscriticalbecauseitcontributestohealthyROI.*
~ Same #2. The need for people using analytical and visualization tools to glean insights from data and accelerate the rate of generating insights.
Youwillalwaysneedskilledusersofthetools,whetherwecallthemdatascientistsorbusinessanalysts.Bottomline:thedatahaszerovalueunlessyouhavethepeople,processes,andtoolstotransformitandaccessitintimelyandrelevantways.Thegoodnewsisthatthetoolsaregettingeasiertouse,andthenumberofpre-builtanalyticmodulesthatplug-and-playtocreatenewinsightsisgrowingrapidly.Butthathasbeenthetrendforyears.†
~ Same #3. The substantial effort to drive insights into production.
Whileinsightsaregreat,theyarenotenough.Youcan’thaveascienceexperimentinthebasement,inisolationfromtherestofthecompany.This“operationaliza-tionproblem”istheAchillesheelforbigdataprojects
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Enhanced security capabilities
Regulatory compliance
Monetize information directly
Improved risk management
Cost reduction
More targeted marketing
New products/new business models
Process efficiency
Enhanced customer experience
Percentage of Respondents
Top priority Second ThirdN = 687
Source: “Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype”, by Lisa Kart, Nick Heudecker, Frank Buytendijk. Gartner, September 12, 2013
Figure 1. Top Priorities for Big Data.
For technical readers, we’ll add occasional technical footnotes that can be skipped by business readers.* Teradata’snoveltechnicalapproachiscalled“TeradataVirtualStorage”andautomatestheintelligentstorageofdatabasedonusage.Ourfastinterconnect
(highthroughputTeradataBYNET™V5onInfiniBand)ensuresthatcombiningandmovingdataacrosssystemsisalsocost-effectiveinthearchitectureswebuild.† Teradatahasbuiltourlibrariesoffunctionsintwoways.TheAsterDataacquisitionbroughtus80newanalyticandvisualizationfunctionsspecificallyforbigdata.
WehaveinvestedinpartnershipswithBIvendorslikeSAS,IBMSPSS,RevolutionAnalytics,ZementisandInformationBuilderstoensurewehavethebest-of-breedanalyticsontopofthedataplatforms.OurrecentpartnershipwithFuzzyLogixaddedanother600functions.Finally,wehavealsopartneredwithvisualizationvendorslikeTableauandTibcoSpotfire.
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BIG DATA: TERADATA UNIFIED DATA ARCHITECTURE™ IN ACTION
becausemostpeoplegrosslyunderestimatethecostsoftakinginsightsintoproduction,whichcanbeordersofmagnitudelargerthaninitialbigdataprojectinsightgenerationcosts.*
Bottom line: thereisacertainconstancytotheproblemsbeingaddressedwithbigdata.Theage-oldquestionsofhowtoincreaseprofits,decreasecosts,improveman-ufacturingefficiency,andcurtailfraudhavebeenaroundforalongtimeandarestillbeingasked.
Sowhat’snew?Wecanaddressthesequestionsinnewer,fasterwaysthatwouldpreviouslyhavebeentooexpensiveortaketoomucheffort.Sothatleadstothefourthingswithbigdatathataredifferent:
~ Different #1. The amount of data you can economically collect is going up rapidly.
Oftencalled“dataexhaust,”thingsleftinthedatacentercorner—likedigitizedphonecallstocustomercare,orWebserverdetailedlogs,orevenexternaldataliketweets—cannowbecapturedandanalyzedtocreatenewvalue-addedinsights.Withever-decreasingstorageprices,thischangestheeconomicsbutrequiresmoresophisticationabouthowmuchdatatocapture,wheretotransformandstoreit,howlongtoretainthedata,andmore.
~ Different #2. New analytics on these new, non-relational data types (coupled with existing relational data) open up exciting new insight possibilities.
Example:youcancreatenewcustomerattritionfactorsforpeoplewhoareangryandshouting,orusingcodewordslike“ripoff”whenspeakingtoCustomerServicereps.Further,youcancombineanalyticsonvariousdatatypes.Forexample,arethoseangrycustomersalllocatedinonegeography?Subjecttonewcontracttermsandconditions?Experiencingrecentcustomerserviceissues?Discoveringrootcasesmayrequirecombinatorialanalyticsonvoice,text,geospatial,temporal,andtraditionalcustomerdata—aswellastheabilitytodothisquicklyandseamlessly.
Otherexamples:Teradatacustomersareexploitinghaildamageandweatherreportstofindagriculturefraud;grabbingTwitterfeedstodiscoverunhappycustomersinrealtimeinordertoreactquickly;andmonitoringemailinvestmentpromisesbybrokerstoclients.
~ Different #3. The process for generating insights is changing.
The“oldway”ofdoinganalyticsassumedthatnewdatawouldbestoredinthedatawarehouse.Hencemanyprocessesthatprecedethediscoverysteprequiredrigorindatamodeling,dataintegration,andETL.The“newway”separatesdiscoveryfromintegrationanduse,freeingupsometimeuntilafterinsightshavebeengeneratedandexplored.Inshort,itfreesupmoretimeforanalysisandexploration.
Example:afraudteamthinksthatperhapsafraudsterwhohasstolensomeone’sidentityspendsadifferentamountoftime—andclicksindifferentways—ontheirbankingwebsitefromthetrueownerofanaccount.Capturingwebclicksandtimedurations,andthesequenceofclicks,theycreatea“normalpattern,”thenwatchfordeviationstothatpatterninend-endexperiments.Theygraballthewebclickdatatheyrequireforexperimentationbutdonotneedtomodelthedatarigorouslyordesignproductionfraudpatternsuntiltheyarecertaintheyhavefoundaninterestingandworthwhilecaseoffraud.
~ Different #4. One kind of processing engine (e.g., just an EDW) is not going to be enough—the architecture must evolve to accommodate specialized platforms, integrated into a comprehensive system.
Justlikeinyourgarage,youwillneedaworkbenchthatincludesavarietyoftools.Inthedataanalyticsworldthismeansworkload-specificengines/platformsforstorageandanalysis,newtype-specificanalyticfunctionsandengines,andintegrationtechnologiestocopyandmovedataorinsightsintoaneasy-to-useenvironmentforyourbusinessandtechanalysts.Gartnercallsthisthe“LogicalDataWarehouse,”andwecallthistheTeradataUnifiedDataArchitecture.™TeradatacustomerslikeIntel®agreeandhaveheadedinthisdirection.5
* It’sanareawhereTeradatahastraditionallyexcelled:rock-solidarchitectureandengineering,allthetechnologiesandservicesthatyouneed(withstraightfor-wardextensionsforbigdataprojects),includingthingslikedatasynchronization,datagovernance,workloadmanagement,backupandrecovery,systemman-agement,metadatamanagement,configurationplanning—many“real-world”requirementsthatareNOTeasytoputinplaceonyourown.Yes,anarchitectureforbigdatalookscomplicatedbutthevaluefromTeradataistheintegrationofour(andpartnertechnology)componentstoreducethetimetooperationalizeinsights.Andit’sengineeredwithflexibilityinmindsonewcomponents,newpartners,canbeincludedwhilemaintainingcontrolofITandensuringROI.
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Bottom line:theeconomicsofhowtocapturedataandwheretostoreit,wheretocomputeonit,andhowtouseit,areconstantlychanging.ThediscoveryprocessisacceleratingwiththehelpofnewtoolkitstogenerateinsightswithouttheusualrigorrequiredbyanEDW.
Andasaconsequence,Teradatahasextendeditsarchi-tecturetoaccommodatetheneedsofbigdata.
Who is adopting big data, and am I late to the party?
ThesameGartnersurvey2alsotriedtogaugehowfaralongrespondents’companieswereinplanningandexecutingbigdataprojects.Weretheygatheringknowl-edgeaboutbigdata?Developingstrategy?Doingpilots?Alreadyinproduction?Figure2showstheresultsforboththosecompanieswhohavealreadyinvested,andthoseplanningtoinvest.
AquickestimateofTeradatacustomersisthatabout15percenthaveabigdataprojectunderway(mostlyusingTeradataAster®and/orHadoopwithTeradataDatabase),andanother20to30percentareconsideringprojects.Earlyadopterswhohavefinishedprojects,believetheyhaveachievedaone-totwo-yearcompetitiveadvantage.Someofthesehavefiveormorebigdataprojectsundertheirbelts.Mostleadingcompaniesareoperationalizingtheirfirstprojectsnow,whilethelaggardsarestillinlearn-ingmode.However,it’snevertoolatetocatchup.
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Planning to invest (n = 247)
Percentage of Respondents
Source: Gartner (September 2013)Source: “Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype”, by Lisa Kart, Nick Heudecker, Frank Buytendijk. Gartner, September 12, 2013
Figure 2. Big Data Adoption by Investment Stage.
TIP:ASKPROPONENTSOFBIGDATAPROJECTSTHREEQUESTIONS:1. Whatbusinessareaswillimprovebecauseofthe
collection,analysis,anduseofbigdata?
2. What’stheincrementalReturnonInvestment(ROI)fromexploitingmoredata?Howdoyouplantocalculatethis?
3. Willourenvironmentsupportthegenerationofmoreusefulinsightsandactionsfasterthanourcompetitors?
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BIG DATA: TERADATA UNIFIED DATA ARCHITECTURE™ IN ACTION
WHAT IS TERADATA UNIFIED DATA ARCHITECTURE™ AND ITS VALUE?
Justlikehybridcarsgetsuperiorgasmileagefromcouplingtraditionalgasengineswithelectricbatteries,theTeradataUnifiedDataArchitecture™integratesthreetypesofenginestoprovidethebestfit-for-purposeanalyticalcapabilitiesforanykindofdata.
Teradatahasbeeninthebigdatamarketlongerthananyone,sowe’veleveragedourexpertisetotacklethe“what’snew”partofthebigdataphenomenonwithfiveparallelengineeringactivities.Beginningwithourcoreproduct,theTeradataIntegratedDataWarehouse,we:
1. Defined a new architecturecalledtheTeradataUnifiedDataArchitecture™thataddsadiscoveryplatformandadataplatformtocomplementtheTeradataIntegratedDataWarehouse.IntheTeradataadvocatedsolution,thediscoveryplatformisTeradataAster,whilethedataplatformcaneitherbeHadooporaTeradataIntegratedBigDataPlatformforlarge,cost-effectivestorageandprocessingofbigdata.
2. Engineered new access connectorsbetweentheplatformsandtoexternalsourcesofbigdata,andextendedourhardwareplatformportfolioandinter-connectoptionstoprovideevenfasterdatatransfers.
3. Created a library of pre-built Teradata Aster analytic modules to speed up and simplify discovery,allinaneasy-to-useTeradataAsterSQL-MapReduce®programmingparadigm.WealsocontinuetoaddmoretechnicalpartneranalyticstotheTeradataAsterandTeradataengines.
4.Extended our traditional systems management products(Viewpoint,Studio™)forsimilarusewithHadoopandTeradataAster.
5. Developed new Professional and Customer Service offerstohelpcustomersquicklydesignanddeployenterprisedataarchitectureandanalyticsprojects.
Teradata Unified Data Architecture™
We’lldividetheexplanationoftheengineeringworkintotwoparts:logicalandphysicalarchitectures.
ThelogicalviewofTeradataUnifiedDataArchitecture™inFigure3showstheflowfromlefttorightofdatato
Marketing Executives
Operational Systems
Frontline Workers
Customers Partners
Engineers
Data Scientists
Business Analysts
htaMand Stats
Data Mining
Business Intelligence
Applications
Languages
Marketing
USERS
ERP
SCM
CRM
Images
Audio and Video
enihcaMLogs
Text
Web and laicoS
SOURCES
DATA INSIGHTS ACTION
FastLoading
Filtering andProcessing
OnlineArchival
DataDiscovery
PatternDetection:
Path, Graph,Time-series
Analysis
New Modelsand
Model Factors
ReportsDashboards
Real-timeRecommendations
OperationalInsights
Rules Engines
ACCESSMANAGE
GOVERNANCE AND INTEGRATION TOOLS
MOVE
ANALYTICTOOLS
Figure 3. Teradata Unified Data Architecture™—logical view.
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insightstoactionthatarerequiredtoprocessbigdata.Datasourcesappearontheleft(bothtraditionalandnewsources),andconsumersofdataandinsightsappearwiththeirend-usertoolsontheright.
Thispictureismeanttobevendor-agnosticbecauseTeradataUnifiedDataArchitecture™isatoolkit.Thelogicalflowalsoshowsablockforthegovernanceandintegrationfunctionsneededforoperationalization,includingpoliciesformoving,managing,andaccessingdataandinsights.
Figure4showstheadvocatedphysicalimplementationofTeradataUnifiedDataArchitecture™withthreeenginecategories—dataplatform,integrateddatawarehouseanddiscoveryplatform.WhileanyoftheseplatformscouldbeusedforloadingandstoringrawdatafromawidevarietyofsourcesusingmanydifferentETLtools,
neweconomicsdrivetowardtheuseofHadoopforland-ing,transforming,andstoringbigdata(asopposedtotraditionaldata,whichcontinuestoflowintoTeradata).6Somepeoplecallthisa“datalake”.AnotheroptionifyouwantthepowerandsimplicityofTeradataistousethenewhigh-capacityTeradataIntegratedBigDataPlatform(IBDP).
TheTeradataUnifiedDataArchitecture™alsoprovidesthreeoptionsforwheretododiscovery.Again,doingtheanalysisdependsontheeconomicsofcomputation,staffskillsets,andbigdatadiscoveryandvisualizationtools,aswellasaccesstoinformationthatmayhavebeenstoredinothersystems(orinanexternalsource).NeweconomicsanddiscoveryprocesschangesdriveustoadvocatetheTeradataAsterDiscoveryPlatformasthebigdatadiscov-eryplatform.
TERADATADATABASE
MarketingExecutives
OperationalSystems
CustomersPartners
Engineers
DataScientists
BusinessAnalysts
Mathand Stats
Data Mining
Business Intelligence
Applications
Languages
Marketing
USERS
INTEGRATED DISCOVERYPLATFORM
INTEGRATED DATA WAREHOUSE
ERP
SCM
CRM
Images
Audio and Visual
MachineLogs
Text
Web and Social
SOURCES
DATA PLATFORM
TERADATA DATABASE
TERADATA ASTER DATABASE
HORTONWORKSHADOOP
TERADATA UNIFIED DATA ARCHITECTURESystem Conceptual View
ACCESSMOVE MANAGE
ANALYTIC TOOLS & APPS
FrontlineWorkers
Figure 4. Teradata Unified Data Architecture™—physical view.
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BIG DATA: TERADATA UNIFIED DATA ARCHITECTURE™ IN ACTION
Onceinsightsarecreated,theyneedtobeusedtohavevalue.Again,therearenumerouschoicesofhowtocon-nectthedataandinsightgenerationenginesshownheretoaction-takingsystems,likeaCRMsystem,acontactcenter,awebsiterenderingengineinreal-time,depen-dentdatamartsdrivingotherapps,orevenanexecutivedashboardingsystem.TheadvocatedsolutionhereistheTeradataIntegratedDataWarehouse,wellknownforitsabilitytoscaletothousandsofBIanalysts(andmil-lionsofconsumers),runningavarietyofapplicationsaswellasBItoolsfortheendconsumersofbigdata(andtraditionaldata).Withthisunderstandingoftheadvo-catedpiecesofTeradataUnifiedDataArchitecture™let’sdrillaleveldeeperintothecomponents.
Teradata Aster
In2011,TeradataacquiredAsterDatawithitspatentedSQL-MapReduce®-baseddiscoveryplatform,useful
todatascientistsandbusinessanalystswhowanttogetfasterinsightsoncombinationsoftraditional(i.e.,relational)andnon-traditionaldata(essentiallyanytypeofdata),alsoknownas“multi-structureddata.”AtypicaluseoftheTeradataAsterDiscoveryPlatformistopullinformationfromavarietyofsources(e.g.,rawweblogsfromwebpagerenderingsystems,machinedatafromfactoryfloorsystems,andtraditionaldatafromTeradata)todoinvestigativeanalytics,tofindnewpatternsofcustomerbehavior,ornewaberra-tionsinmanufacturingdatathatmightindicatequalitycontrolissues.
TeradataAsterisbasedonapatentedSQL-MapReduce®implementationwhichmakesiteasyforBIanalystswhoknowSQLtowriteextensionsthatusefourclassesofbuilt-inanalyticalfunctions(seeFigure5)toveryquicklycapture,prepare,analyze,andvisualizebigdatasets.
TeradataAccess
HadoopAccess
RDBMSAccess
DataAdaptors
DataTransformers
Statistical
PatternMatching
Time Series
GraphAlgorithms
Geospatial
FlowVisualizer
HierarchyVisualizer
AffinityVisualizer
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CUSTOMBIG ANALYTIC APPS
PACKAGEDBIG ANALYTIC APPS
BITOOLS
DataAcquisition
Module
DataPreparation
ModuleAnalyticsModule
VisualizationModule
Figure 5. Teradata Aster analytics and access modules.
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AsterBigAnalyticsAppliance
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StrategicandOperationalIntelligence,
HighConcurrencyActive,10+
ApplicationsRealTime
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DSS,FastScan,Low
ConcurrencyActive,<10
Applications
Test/Development,
SmallDataWarehouse
ArchiveReporting,
DeepAnalytics,
DataRefinery,DataLabs
Storing,CapturingandRefiningData,Hortonworks
HDP1.1
BigDataAnalyticswith
EmbeddedSQL
MapReduceforNewData
TypesandSources
Software Teradata Database Horton Works Aster Database
Figure 6. Platform Offers: Four Teradata engine options, plus Hadoop and Teradata Aster.
Hadoop
In2013,TeradataannouncedtheTeradataPortfolioforHadoop,providinganotherdatacapture/storage/refiningplatform(leveragingourpartnersHortonworksandCloudera).Theinitialfocusisonwide,cheapcaptureof“dataexhaust”forpossiblestand-aloneuse,butmorelikelyasasourceorETLsystemtotheTeradataAsterDiscoveryPlatformorTeradataIntegratedDataWarehouse,augmentingthevolumeandtypesofdatathatcanbeincludedinanalysis.
Hardware Platforms
Fromahardwareperspective,addingTeradataAsterandHadoopaugmentedthenumberofoptionsfromwhichtochoose,asshowninFigure6.Customerscanevenbuyworkload-specificappliancesthatareconfiguredwithcombinationsofTeradataAsterandHadoopnodes.
Teradataalsoaddedafabric-basedhighthroughputcomputinginterconnectcalledTeradataBYNET™V5onInfiniBand,forveryhigh-speeddatamovementandfaulttoleranceamongthevariousplatforms.Thisenablesfastinterchangeofdatatothemostoptimizedplatformforspecificstorage,processing,oranalysisrequirements.
Data Connectors
ConnectingthesethreesystemsrequiredthatTeradatabuildbulkdataconnectorsaswellasspecializedconnec-torsforon-the-flydataaccess,e.g.,TeradataSQL-H™andTeradataAsterSQL-H™foraccessingdatainHadoop,andanAster-TeradataConnector.
Pre-built Analytic Models
Ourgoalistomakeinsightgenerationfast,despitetheincreasingamountsandtypesofdata.Todothat,we
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SAMPLE TERADATA ASTER SQL-MAPREDUCE® FUNCTIONS BY CATEGORY
A N A L Y T I C S M O D U L E
DATA ACQUISITION ANDPREPARATION MODULE
VISUALIZATION MODULE
PARTNERS MODULE
Sessionization, IPGEO Mapping,XML Relation, XML and ISON Parsers
PATH AND PATTERNTeradata Aster nPath™, Path Generator,
Path Summarizer, Attribution
STATISTICALHistogram, Linear Regression, Sampling,
F-measure, Naive Bayes Text Predict, GLM
TIME SERIESDiscrete Wavelet Transformation,
Symbolic Aggregation Approximation (SAX)
RELATIONSHIP ANALYSISSingle Source Shortest Path,Collaborative Filtering, nTree
TEXTText Classifier, Text Parser, Chinese Text
Segmentation, Text Tagging, Sentiment Extraction
Teradata Aster nPath™ Visualizer, cFilter Visualizer
Attensity Sentiment Categorization, Attensity Triples Finder, Zementis PMML, SAS and R In-Database Scoring
Figure 7. Teradata Aster pre-built analytics functions.
haverapidlygrownthenumberofTeradataAsterpre-builtfunctionstomorethan80(seeFigure7;formoredetailsoneachfunction,seetheTeradataAsterDiscoveryPortfoliowhitepapers7listedintheSuggestedRead-ingsection).Thesecanbeusedinisolation,orinvariouscombinations,toenable“multi-genreanalytics”andlookatdatathroughdifferentperspectivestoimproveaccu-racyofmodelsandalgorithmsforchurn,fraud,customersegmentation,andmore.
Inaddition,wehaveworkedwithpartnerslikeSAS®,IBMSPSS®, RevolutionAnalytics,Zementis,and FuzzyLogix™,8 leadingtextvendorslikeAttensity,andgeo-spatialvendorslikeESRI,toincreasethenumberofTeradata-basedanalyticmodulesthatoftenusein-memory(orin-database)capabilitiestospeedinsightdevelopmentonTeradata.ThetraditionalBIapproachofusingaTeradataDataLabonstructureddatahasbeenextendedtoworkinconjunctionwiththeTeradataAsterDiscoveryPlatformtoprovidearichcollectionofoppor-tunitiesforinsightgenerationandintegration.
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Management
Asyouaddmorecomponentstotheenterprisedataarchitecture—andinparticular,asyoumovefromdiscov-erytooperationalization—adangeristhatmanymorepeoplemightberequiredtomonitorandmanagethesystem.InourtraditionalTeradatasystems,wedrivelowertotalcostofownership(TCO)byautomating,andinmanycaseseliminating,manysystemsmanage-mentfunctions,buildinginself-monitoringandtuningcapabilities,andmakingDBAfunctionseasierthanthecompetition’s.WeholdtruetothatcorporategoalwiththeTeradataUnifiedDataArchitecture,™bringingmanyoftherichTeradatamanagementtoolsandworld-classsupportinfrastructuretotheTeradataAsterDiscoveryPlatformandtheTeradataPortfolioforHadoop.
KeytechnologiesincludeTeradataViewpoint,TeradataVitalInfrastructure,andTeradataStudio™tonameafew.TheTeradataUnityportfolioformanagingmultipleTeradatasystemsisbeingenhancedtosupportTeradataAsterandHadoopenvironmentswherethetechnologiesfit,cost-effectivelytakingmanagementoftheTeradataUnifiedDataArchitecture™tothenextlevel.Teradataalsohascomprehensivedatagovernanceoffers,extendingmetadataandmasterdataservicestoleveragethevalueandreliabilityoftraditionaldatamanagementacrossallthreeplatforms.Thisgivesorganizationsatrustedfoun-dationforadata-drivenculture.Bottomline:TeradatarecognizesthathandlingbigdatarequiresmanyofthesameproductionqualitytoolsthattraditionalTeradatahasprovidedforyears,andthisisabigpartofourTCOvaluetoourcustomers.
Training and Services
OurfinalpieceoftheTeradataUnifiedDataArchitecture™isourtrainingcurriculumalongwithProfessionalandCustomerServices.WehavehiredexpertswithnewskillsetsinHadoop,aswellastrainingaconsiderablenumberofourProfessionalServicespeopletobedatascientists.Inengagements,theysitside-by-sidewithourcustomers,trainingthemontheTeradataUnifiedDataArchitecture™technologiesandprovidingleave-behindtemplatesforprojectsanddatadiscoveryinsightmethodsthatcanbeeasilyreplicated.OurIndustryConsultantscollectthelatestandgreatestcustomercasestudiesandcanhelpevaluateprojectplansforbigdataeffortsusingourIndustryFrameworks.And“takingaction”onnewinsights—whichiswherethevaluelies—oftendependson
discussionsanddatasharingwithothergroups,orintegrationprojects.Forexample,Teradatacustomersareleveragingbigdatainsightsbyconnectingtoanyofahostofautomatedfront-endsystemsliketheContactCenter,thewebgroup,thefactoryfloor,ormobiledashboards.Planning,designing,deploying,andmaintainingtheenvironment—alltheseactivitieshavecorrespondingProfessionalServiceoffersthathavebeenextendedtocoverbigdataneeds.ThiscomprehensiveviewofbigdataandhowitisoperationalizedinyourbusinesssetsTeradataapartfromothertechnologyvendors—andgivesyou“onethroattochoke”ifyouhaveproblemsand“onehandtoshake”withsuccess.
HOW DOES TERADATA UNIFIED DATA ARCHITECTURE™ WORK?
TheprimaryvalueofTeradataUnifiedDataArchitecture™
istoconvertdata—bigandsmall,andallcombinations—intouseful,actionableinsights.Thisinvolvesanalyticalapproachesdesignedtouncoverpreviouslyunknownpatterns,ortheidentificationofkeyeventsthattriggercustomerbehaviorslikedecisionstobuyproductsorcancelcontracts.Inthissection,we’llbringtheTeradataUnifiedDataArchitecture™tolifebyshowing“ADayintheLife”oftwocustomerprojectsfromvariousindustries,highlightingvariousimplementationdetails.
SCENARIO 1: RETAILMULTI-CHANNELINSIGHTSDRIVEBETTERMARKETINGANDCATEGORYMANAGEMENT
SeverallargeretailersuseTeradata’sUnifiedDataArchitecture™tohelptheirmarketingandcategorymanagersbetterunderstandconsumerbehaviorontheirwebsites,onmobiledevices,andinthestores.Inthiscompositecaseillustratingseveralcustomers’bestuses,theseretailersalreadyhadTeradatasystemsthatcontaineddetailsaboutmillionsofcustomers,sales,andproducts.Theirbigdatainterestwasinaddingwebandmobiledata,specificallytryingtounderstandtheimpactofmarketingcampaigns,andcouponingeffortstodrivelargermarketbasketsorimproveoverallmargins.
Data
Oneretailerbegantheirproofofconcept(POC)byloadingverydetailedwebserverlogrecordsintoaHortonworksHadoopcluster.Thisrawdatatransferwasfast;theyfoundtheycouldloadtwomonthsofweblog
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inonehour(about60timesfasterthantheirtraditionalapproach).Oneday’sworthofdataincludedabout30millionwebclickentries,eachinvolvinguptohundredsofattributesthatneededtobeparsed.
Sessionization
Hadoopwasfurtherusedtosessionizethedatafrommultiplelogsourcestocreateintegratedcustomervisits.Sincematerializingeachpagehitmightcausemul-tiplelogrecordstobewrittentoseveralservers,thereareoftenfrom5to30recordsthatneedtobesplicedtogethertoshowwhat’shappenedonanyparticularpage.Oncethisisdone,thenthetemporalsequenceofpagescanbereconstructedforanyparticularcus-tomer.Splicingthistogetherwithcustomerstoredataonmarketbasketsprovidedamoreusefuldatasetofeachcustomer’sbehavior.
Analytics
ThesesequenceswerethenaccessedusingHCatalogbyAsterforfurtheranalysisbasedonpathmatching(usingthepre-builtTeradataAsternPath™function).TheirengineersbuiltatoolsouserscouldeasilywriteSQL-MapReducequeriestodopathmatchingatahighersemanticlevel.Forexample:“findallcustomerswholookedatproductsinthecategory‘LawnFurniture’andclickedthreetimesinthatcategorybutneverpurchasedanything,”thendrilldownintowhatthosecustomerswerelookingat,specifically,togetdeeperinsightsaboutproductinterest.Theycouldalsocross-correlatepur-chasersversusnon-purchasersfromreferralsites(websiteswithadsdrivingcustomerstothewebsite),sothey
couldseewhichadplacementsdrovethemoreprofitablecustomerstotheirwebsite.Theycouldalsocomparethewebpagesthatpurchasersandnon-purchaserswerebrowsing,toseeagainthedifferences—allprovidinglevelsofinsightthecategorymanagerscouldnevergetbefore.Finally,byaccessingtheTeradatadatawarehouse,theycouldtiethiscustomeranalysistogetherwithpreviousstorevisits,purchases,lifetimevalue,responsetopromotionsofvarioustypes,andothercustomerscoresalreadyintheirexistingdatabasetobettercalibratefuturemarketingcampaigns.
AnotherTeradatacustomerfocusedonomni-channelbehaviors,specificallyemailcampaignsthatdrove(ordidnotdrive)conversionsusingcouponsinthestores.Theydrilledintofindingwhichpeoplebought/searched/clippedcouponsforitemA,andthenranAster’saffinityandclusteringanalyticstorevealdeeperinsightsaboutwhatcombinationsofitemsareattractingorrepellingcoupon-orientedshoppers.Clusteringcustomergroupsbybehaviorthenprovidedthecluestheyneeded,foraparticularshopper,tomatch“herhistoricalbaskets”and“othercouponshopperslikeher”tocomeupwithbetterpersonalizedrecommendationsandcouponsforfutureemails(seeFigure8).
YetanothercompanyalsousedtheTeradataUnifiedDataArchitecturetoanalyzewhichemailsdrovepeopleto“un-subscribe”altogetherfromemails.Forexample,arethereparticularwordsorphrasesthatseemtocausethis(discoveredusingtextanalytics),oraretime-of-day,day-of-week(temporal),ornumberofrecentemailsdrivingun-subscriptionevents?
Product A: BOUGHT
Product B: LOOKED
Product C: BOUGHT
Product B
COUPON for Product E
A Customer’s“Historical Basket” + =Shopping Patterns
of Similar ShoppersPersonalized
Recommendations
Figure 8. Matching Customer Behavior with Clusters to Drive Recommendations.
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Value to Users?
Forthefirsttime,categorymanagerscouldeasily“see”exactlyhowcustomerswerebehavingonthewebsite,andwhatwerethe“hot”and“cold”partsofthewebsite.Notjustthe“score”ofacustomersegment,buttheactualbrowsingbehaviorofdiscreteindividualsandmicro-segments.Theycouldseedwelltimesonvariouspages,andunderstandbetterwhichpagescontributedtothepathwaytopurchases,asopposedtopathwaysthatendedindropouts(non-purchases),whichhelpeddriverethinkingofunproductivewebpages.Theycouldseewhichcouponoffersworkedanddidn’t.
Action
Thefasterinsightsusingdetailedwebbehaviorswereusedbythisretailertochangewebdesignlayoutsandcontent.Theyalsohelpproductcategorymanag-ersderivebetterinsightsaboutcustomersandbetterpersonalizedoffers.Theycouldalsorecalibratetheirspendingonsearchenginekeywordsonothersites,andfocusonunproductiveemailtextandbademailtiming.
The Business Value?
Theseareexpectedtoincreasemarketbasketsizeandimproveconversionratesofexistingcustomersbyfivetotenpercentanddriveanothereighttotenpercentmorebrowsersintothesalesfunnelthroughbettertargeting.Thebottomlineimpactistensofmillionsofdollars.
Project Time
Theseprojectswerecompletedtypicallyinthreetosixweekswithonetotwofull-timeanalysts,usuallyoneTeradatadatascientistandoneTeradataclientBIexpert,plusthesponsorandalittlehelpfromanETLengineer.
SCENARIO 2: BANKINGIMPROVINGTHECUSTOMERSUPPORTEXPERIENCE
Alllargebanksandcreditcardcompaniesreceivemil-lionsofcustomersupportrequestsperyear.Likemostconsumercompanies,theyhavetriedtoshuntinquiriestoself-servicemodeontheirwebsitetoreducethevolumeofphonecallscomingintothecustomercontactcenter.Thegoalofthesebigdataprojectswastoseeiftheycouldfigureoutwhypeoplewerecalling,especiallywhentheyhadbeenonthewebsitejustpriortocall-ing.Theyhadneverhadtheabilityto“see”thecustomer
datathiswaybecauseitwasonvarioussystems—webdataandanalyticsononesystem(withoutsourcingofsomeanalytics),contactcenterdataonanother,interac-tivevoiceresponse(IVR)dataonyetanother,callcenteragentnotesinyetanotherplace,andcustomertransac-tioninformationononemoresystem.
Data*
Fourtosixmonthsofdatawereloadedfromfivetosevensourcesystems.500Mto1.5Buncompressedweblogrecordsfrom10-30Mcustomersessionsonthewebtook40-70minutestoloadintoTeradataAsterDatabase.800Mto1.3billionIVRandcallrecordsfrom5-15Mcontactcenterinteractionstookanother40-60minutes,and50-75millioncallcentertextnotesandcomplaintdatabaserecordstooktypicallylessthan30minutes.
Sessionization
Usingtimestamps,asshowninFigure9,theIVRandcallrecordsandmemoshadtobematcheduptocreateacompleterecordforeachcall,thenmergedwithwebses-sionsandsorted.Theythenlookedforwebsessionsthatwereimmediatelyfollowedbycallstocustomersupport.
Page-Hit Aster nPath™: Find customers of interest
E.g., Three pages on the Web,THEN
A call to Customer Care
Call/Memo
Page-Hit
Page-Hit
Page-Hit
Call/Memo
Customer B
Customer T
Figure 9. Creating an integrated view of web activities followed by care center activities.
Analytics
UsingTeradataAster,theyfoundthatabout2percentofthemillionsofwebsessionsdrovesubsequentcallstocustomercare,providingabaseofseveralhundredthousandcasesthattheycouldthenfurtherinvestigate.
*Teradatahasdonemultiplebigdataprojectsforthisusecase;numbersbelowareacompositeorrangeoffiguresrepresentativeof“typicalimplementations.”
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Figure 10. Backmapping from high-incidence calls to the care center to pages on the Web site.
TheycreatedTeradataAsternPath™visualslikeFigure10toshowthetopwebpagespeoplehadclickedonrightbeforecalling,andmappedthosetotheCSR’s“reasonforcall”codes.
Value to Users
Thecustomersawnewinsightsaboutpaths,specificallywhichwebsitepagesandIVRsequencesseemtocauseconfusionanddropoutsresultinginexcessivecallstocustomercare.
Action
ThefindingsledtotheredesignofseveralwebsitepagesandIVRbuttonpushsequencessoconsumerscanresolveissuesontheirowninself-servicemode.
Business Value?
Transfersbetweenchannelscostfromfivetofifteenmilliondollarsannually,andrepeatcallsforunresolvedissuescostalmostthesameamount.Thebigdataprojectprovidedincreasedlevelsofself-servicewhiledecreas-ingcostofoperations,bettercustomerexperienceandsatisfaction,andprovidedearlyidentificationofvaluablecustomerswithcareissueswhomightdefect.Aroughprojectionofsavingsiscosttakeoutof10percentorroughly$1.5M-$3Mfromthisbigdataproject.
Project Time
Theseprojectswerecompletedtypicallyinfivetoeightweekswiththreetofivefulltimeanalystsandahandfulofpart-timeemployeestohelpwithdatasourcing.
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HOW CAN MY COMPANY GET STARTED?
Thisisanevolutionnotarevolution,sothesamebestpracticesapplytotheprojectmanagementofbigdataprojects.It’saboutROI,appropriateleadershipandmen-toring,staffingandtraining,aswellaslinkagesbetweenbusinessandITtofocusonkeyaspectsoftheproject.
Thekeyareatofocusonistheeconomicsofopportunitygainedorlost.Thinklikeaneconomist.Whatbusinessproblemscouldberesolvedwithmore/new/differentdata?Whatistheeconomicvalueofincrementaldata?Whatisthecost?Ormoreimportantly,whatistheeco-nomicvalueofnewinsights?Askyourbusinesspeoplequestionslike“whatwouldyoudoifyouknew,”“whatimmediateactionscouldwetakeifweknew,”and“howmuchvaluewouldthatprovide?”Ifyouaremanagingacallcenter,whatcouldyoudobetterifyougotanauto-matichourlylistofthepeoplewhoaresoangryincallsthatthiscouldbeTHEkeyfactorinradicallyimprovingcustomerretention?ROIisaboutvalueversuscosts.Thecostsareusuallyeasytoobtain—costsofstorage,soft-ware,tools,consultingservices,andongoinglicenses.It’sthevaluethat’sdifficulttocompute—andsomethingthathastocomefromthebusinessgroups,notIT.
SurveyingTeradatacustomers,it’sinterestingtoseewholaunchedthebigdataprojectorganizationally.Thecham-pionatourfirstbigdatacustomercamefromthebusinessstrategygroup,notthelineofbusiness,andnottheITgroup.That’srare.Often,initiativescomefromITpeoplesupportingmarketinggroups—whichisunderstandablesincemanyTeradataimplementationsaremarketing-sup-portsystems.Inallthesuccessfulcasesthough,theyhadabusinesssponsor,pickedaproblemthatwasbigenoughtomaketheresultsinteresting/useful/immediatelyofvalue,butnotsolargeastooverwhelmthegroupdoingthework.Byandlarge,we’vebeendelightedwithhowquicklythePOCshavegone(oftenonlyfourtosixweeks)andit’sanopportunityforourpeopletohelptrainyours.Thepoweroftechnologyiseasilyproven—itmaywellbethatthebottleneckforbigdataprojectsusingTeradataUnifiedDataArchitecture™willbethelimitsofyouranalysts’imagi-nations,notthetechnology.
A quick set of questions to get started:
1. Whatisyourcurrentpainpointoropportunitythatwillgiveyouacompetitiveedgeusingbigdata?
2. Whounderstandsandhasthepowertodriveaproject?
3. Whatisthelikelybenefitinquantifiabletermsforyou?ROI?DoyouneedhelpwithaBusinessValueDiscovery?
4. DoesTeradataUnifiedDataArchitecture™fittheproblem?
5. DoesitmakesensetoproceedtoaPOConasampledataset,withTeradata’shelp,soyoucangetyourfeetwet,getsomeearlyexperiencewiththetools,andseewhatthetechnologycandoforyou?
6. IfyoualreadyhaveTeradata,canyouplananArchitectureAssessmentsoyourITgroupislinkedinandyouachievebuy-inforoperationalization?
7. Isthetimingright?Isyourcultureready?
Three things to watch for, and avoid, based on our experience:
First,the“shinynewtoy”phenomenon.Whileit’slaud-ableforpeopletowanttolearnnewtechnologies,it’simportanttomakesuretheyaredoingsoinabusinesscontext.Therearealwaysshinynewthingscompetingforyourattention.Eightypercentofthem(and90percentoftheassociatedvendors)flameout,somakesurethatyouplaceyourbetscarefully.Foranentertainingper-spectiveonthisbyDanGraham,oneofTeradata’smostinsightfulcurmudgeons,see‘ThrowingYourHatintheRing’or‘BigElephantEatsDataWarehouse’blogposts.9
Second,the“scienceprojectinthebasement”phenom-enon.Incaseswherebigdataprojectshaverunintoproblems,theyusuallyoccurneartheend,whentheystruggletotaketheresultsintoproductizationwithouttheappropriateplanningandconsiderationofmanyoftheoperationalizationaspectsofrealprojects.It’softenimportanttoinsulatetherisktakersinyourcompanywhowanttobechangeagentssotheycanworkquicklyinahigh-energyenvironment.However,it’sequallyimportanttomakesurethatyouhaveagoodgame-planforfoldingtheirinsightsbackintotheotherinsightsyou’vecreatedwithTeradata,andthatyoucantakeaction.Wecallthisthe“InsightIntegration”stepandthe“TakingAction”step.Usually,the“TakingAction”aspectsofdataandinsightsdependonTeradataanditscon-nectionstofront-linesystemslikewebsites,dashboards,andmobileapps.Thisisimportant,becauseit’sveryunlikelythatHadoop(givenitsbatchorientation)oreven
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TeradataAsterisgoingtobeabletohandlethevolumeofrequestsfromoperational(asopposedtostrategic)BIworkloads,anareathatTeradatacalls“ActiveDataWarehousing™.”(YoucanfindmuchmoreinformationonTeradata.comaboutTeradata’sprowessinmixedwork-loadsaswellascasestudiesofcustomersconnectinghundredsofthousands,evenmillionsofcustomersonwebormobiledevicesdirectlytothesamesystemthathandlestheirtraditionalBIworkloads.)
Finally,monitorforthe“cultureclash.”There’softenacultureclashbetweenthepeoplewhodidthe“newproject”andthosewhohavetorunthebusiness—andthat’stheareawhereyouneedtospendsomeextraenergy.Itreallyisaboutpeople,process,andtechnology.Whilethebigdatatechnologyisprovenandthearchi-tectureisrobust,peopleandprocessissuesareoftenthestumblingblockstoputtingTeradataUnifiedDataArchitecture™intoaction.
SUMMARY
Bigdataisanevolution,notarevolution—oftechnology,analyticalcapabilities,andskillsforpeopleresponsibleforusingdatatocreateinsightsanddiscoveriesthatleadtoacompetitiveedge.OurexpertisewithTeradataUnifiedDataArchitecture™helpscompaniesnavigatetheoptions,understandtheeconomics,andlearnfrombestpracticestoputbigdataprogramsinplace.FormoreinformationvisitusatTeradata.com.
Suggested Readings
1. LisaArthur,Big Data Marketing: Engage Your Customers More Effectively and Drive Value,Wiley,2013.
2. LisaKart,NickHeudecker,FrankBuytendijk.“SurveyAnalysis:BigDataAdoptionin2013ShowsSubstanceBehindtheHype,”GartnerReportGG0255160,September12,2013.
3. ThomasH.DavenportandJeanneG.Harris,Competing on Analytics, The New Science of Winning,HarvardBusinessSchoolPublishing,2007.
4.ErikBrynjolfsson,LorinM.Hitt,HeekyungHellenKim—MITSloan,“StrengthinNumbers:HowDoesData-DrivenDecisionmakingAffectFirmPerformance?”,SocialScienceResearchNetwork,April2011.http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1819486
5. Intel,“UsingaMultipleDataWarehouseStrategytoImproveBIAnalytics”,IT@IntelWhitePaper,March2013.http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/using-a-multiple-data-warehouse-strategy-to-improve-bi-analytics.pdf
6. RichardWinter.“BigData—WhatDoesItReallyCost,”June2013.http://www.asterdata.com/big-data-cost
7. TodrillintomoreinformationabouttheTeradataAsterDiscoveryPlatform,read:
~ http://www.asterdata.com/resources/whitepapers.php
~ http://www.asterdata.com/resources/analyst.php
8.TeradataWebsite.“TeradataandFuzzyLogixDeliverGame-ChangingIn-DatabaseAnalytics,”TeradataBrochure,July2013,AvailableonTeradata.com.
9. DanGrahamBlogPost,http://blogs.teradata.com/data-points/throwing-your-hat-in-the-ring
10.TeradataWebsite.“TeradataUnifiedDataArchitecture™”,TDWIE-Book,May2013.Providesan11pagecompilationofinterviewsofnotedanalystsMarkMadsenandDavidLoshin,aswellasinterviewswithTeradata’sImadBiroutyandSteveWooledge.AvailableonTeradata.com.
11.TeradataWebsite.“TeradataUnifiedDataArchitecture™:AVisionaryFramework”,EB6705,April2013.Providesa6pagequickoverviewoftheTeradataUnifiedDataArchitecture™andthreecustomerusecases—twointelecommandoneinbanking.AvailableonTeradata.com.
12.TeradataWebsite.“TurningDataintoProfitwithTeradataUnifiedDataArchitecture.™”Isa2pagesummaryfromQ1-2013.http://www.teradata.com/white-papers/Turning-data-into-profit-with-Teradata-Unified-Data-Architecture
TeradataStudio,SQL-H,TeradataAsternPath,ActiveDataWarehousing,andtheTeradataUnifiedDataArchitecturearetrademarks,andTeradata,Aster,BYNET,SQL-MapReduce,andtheTeradatalogoareregisteredtrademarksofTeradataCorporationand/oritsaffiliatesintheU.S.andworldwide.NetApp,theNetApplogo,andGofurther,fasteraretrade-
marksorregisteredtrademarksofNetApp,Inc.intheUnitedStatesand/orothercountries.IntelisaregisteredtrademarkofIntelCorporationoritssubsidiariesintheUnitedStatesandothercountries.SASandallotherSASInstituteInc.productorservicenamesareregisteredtrademarksortrademarksofSASInstituteInc.,intheUSAandothercountries.
®indicatesUSAregistration.SPSSisaregisteredtrademarkofIBMSPSS.AttensityisatrademarkofAttensityCorporation.FuzzyLogixisatrademarkofFuzzyLogix,LLC.Teradatacontinuallyimprovesproductsasnewtechnologiesandcomponentsbecomeavailable.Teradata,therefore,reservestherighttochangespecificationswithoutpriornotice.Allfeatures,functions,andoperationsdescribedhereinmaynotbemarketedinallpartsoftheworld.ConsultyourTeradatarepresentativeorTeradata.comformoreinformation.
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