1、How to use a semantic layer for data and analyticsA Practical GuideTable of Contents3 Introduction:What is a Semantic Layer?4 Where the Semantic Layer Fits5TrendsDrivingtheNeedforaSemanticLayer6TopChallengesaSemanticLayerSolves12IndustryUseCasesforaSemanticLayer17ConsiderationsforChoosingaSemanticLa

2、yer21AFrameworkforEvaluatingSemanticLayerSolutions23AtScaleCloudDataPerformanceBenchmarks25MakingaBusinessCaseforaSemanticLayer26LearnMore3A Practical Guide to Using a Semantic Layer Youmayhaveheardthetermsemanticlayerbefore;itsbeenaroundforsometime.Peopleinventedsemanticlayerstomoldrelationaldataba

3、sesandtheirSQLdialectsintoanapproachableInterfaceforbusinessusers.In1992,BusinessObjectspatentedthetermandformalizedtheirimplementationastheBusinessObjectsUniverseTM.Fromthatpointon,theconceptofmeasureanddimensionsasanabstractionofSQLhasbecomethepreferredlanguageforbusinessusers.Introduction:What is

4、 a Semantic Layer?PrimaryReasonstoAdoptaSemantic Layer Establishasinglesourceofbusiness-vettedmetricsandanalysisdimensions.Ensure data consumers can access governeddatawithtoolsoftheirchoice.Ensureperformantaccesstodataincludingsummarystatistics.Keepcostslowbymanagingcloudinfrastructuresmartlyandkee

5、pingmanagementandadministrationsimpleLetslearnmore!Untilrecently,however,thesemanticlayer was always closely tied to a businessintelligence(BI)platform.AslongasenterprisesremainedwithintheconfinesoftheirBIvendorofchoice,everythingworkedwell.Today,therearemorewaysthanevertoanalyzedata.Longgonearethed

6、ayswheretherewasoneBIplatformtoruleall.Tightlycouplingasemanticlayertoone analytics consumption style no longermakessense.Toexpandonthat,theexplosionofself-serviceBIhasfreedbusinessusersfromrelyingonIT-preparedanalytics,butattheexpenseofdataconsistencyandtrustinanalyticsoutput.Businessdefinitionsand

7、termshavebecomemutable,malleable,andsubjecttointerpretation.Whileitsgreatthatbusinessusersnowhaveself-serviceBItools,theyalsoneedtobeworkingoffofconsistent,high-qualitydata.Thecostofbaddataisenormous;AccordingtoIBM,poordataqualitycoststheU.S.economyastaggering$3.1trillionannually.Luckily,asemanticla

8、yerthatsdecoupled from the point of consumption can help ease these problems with data quality and empowerself-serviceanalytics.Awell-designedsemanticlayercanleadtobetterdata-drivendecisions.Itsacriticalpartofthemodernanalyticsstack.4A Practical Guide to Using a Semantic Layer Where the Semantic Lay

9、er Sits in Your Data StackAsyoucanseeinthisdiagram,the semantic layer sits between the point of analytics consumption and the data warehouseanddatalake.A semantic layer hides the physical complexityfromendusersandprovidesthemwithunderstandablebusinesstermsanduser-friendlydata,insteadofrawSQLanddatab

10、aseschemas.Thislevelofdatavirtualizationmakesdataaccesspossibleforanyanalyticsconsumer.5A Practical Guide to Using a Semantic Layer PowerfulclouddataplatformslikeSnowflake,GoogleBigQuery,AmazonRedshift,Databricks,andMicrosoftAzureSynapsehavebecomethestandardforenterpriseanalyticsstacks.Accordingtoth

11、eAtScale2020BigData&AnalyticsMaturitySurvey,61%ofrespondentscurrentlyoperateclouddataplatforms,and48%planondeployingthemsoon.TrendsDriving theNeedforaSemantic LayerAsthevolumeofdatainthecloudgrows,dataarchitectsareincreasinglybecomingmorecomfortablewithdatalivingindifferentlocationsandindifferentpla

12、tformarchitectures.However,thisgivesrisetoanewchallengeforIT:managingdataaccessandqualityacrossmultiplesilos.Asemanticlayerbecomesacriticalpieceinaclouddataplatformstrategy(orablendedcloudandon-premstrategy).BothdatascientistsandBIusersneedaccesstoclean,understandabledata.Todaysself-servicearchitect

13、uresoftenforceanalyticsconsumerstobecomedatawranglersanddataengineers.Infact,theaveragedatascientistspendsover45%oftheirtimepreparingdataratherthanmodelingit.Askingbusinessusersanddatascientiststodesigntheirownmetricsandanalysisdimensionseverytimetheystartanewprojectisbothamassivewasteoftimeandareci

14、peforchaosandinconsistency.Finally,asemanticlayercanserveasacentralgovernancegatewayacrosstheenterprise,whichiscrucialasthenumberofsilosanddataaccesspointsexplodes.AsemanticlayerservesasasinglepointofaccesssoITcansecuredataandcontrolaccessacrosstheorganization.ThesameBigData&AnalyticsMaturitySurveyr

15、eferencedaboveshowsthatnearly80%ofenterprisesranksecurityandgovernanceascriticaltotheirsuccessinthecloud.6A Practical Guide to Using a Semantic Layer ThetopfivechallengesasemanticlayercansolveThere are common problems that crop up without asemanticlayerfacilitatingdecision-makinginanorganization.Wec

16、angrouptheseproblemsintofiveareas:1.Differentanalyticstoolpreferences2.Lackofdataaccess3.Slowdataintegrationleadingtosiloedsolutions4.InconsistentBIreportsacrossdifferentbusinessunits5.LowdataconfidenceWhatfollowsisadeepdiveintoeachofthesechallengesand an explanation of how a semantic layer can help

17、 solveit.7A Practical Guide to Using a Semantic Layer #1Businessunitshavepreferencesfor different analytics toolsDresnerreportsthatmanenterprisesusethreeormoreBItools,witheachtoolhavingitsownsourceoftruth.Throwinpossibilitiesofinaccuratereportsfrombusinessanalystsormisleadingpredictionsfromdatascien

18、tists,anditseasy to see how multiple tools can lead to multipletruthsandthatsnotagoodthing!Andthepaceofchangeinclouddatawarehousing,BI,andAI/MLhasresultedinaconstantcycleofupgrades,replatforms,and re-factors across different organizations.Fromatime,cost,andbusinessimpactperspective,itshardtokeepupwi

19、ththesechanges.Asemanticlayerneatlysolvesthisproblembyprovidinganalytics-as-a-service(AaaS)toyourbusinessusersanddatascientists.Thisletsyougrantdataaccesstoyourendusersviatheirtoolsofchoicewhilemaintainingdatagovernanceandsemanticconsistency.Largerorganizationshaveatoughertimeimposingasingleanalytic

20、sstandardacrosstheboard.Thiscanbebecauseofthedisruptionofanacquisition,resistancetochange,orfactorsthatlimitmanagementsabilitytoenforceunifiedstandards.8A Practical Guide to Using a Semantic Layer #2Userscomplainabouta lack of access to dataTheresearchsupportsthis,tooGartnerreportsthat87%oforganizat

21、ionshavelowBIandanalyticsmaturity.Youmighthaveabundantdata,butyourdataconsumersstruggletomakesenseofitanditshamperingthespeedatwhichtheycanmakeaccuratedecisions.Asemanticlayereasesthispainbypoweringyourdatamodelwithcrucialcontexttoaiddecision-making.Dataisplentiful,butcoherentdataisanotherstory.Busi

22、nessanalystsanddatascientistscantrelyonjustanydata.Theyneedtounderstandthedatainlogfiles,relationaltables,andotherdatastoresthroughmetadata.Ifthatsmissing,itleadstotimewastedoninterpretationandeveninaccurateresultsthatcanhurtbusinessperformance.9A Practical Guide to Using a Semantic Layer #3 The slo

23、w pace of data integrationdrivesbusinessestoDIYTheresaclearlinkbetweendatadrivendecision-makingandbusinessperformance:MITreportsthatcompaniesinthetopthreespotsintheirindustrywhoapplydata-drivendecisionmakingrealized5%moreproductivityand6%moreprofitthantheirpeers.Thismovetothecloudandriseofbigdatahav

24、epoweredaBIrevolution,leadingtobusinessuserstakingreportinganddataengineeringintotheirownhands.Thisisapositiveshift.Butitalsohasitsdrawbacks,withmanydataplatformsanddatamartsproliferatingeverywhereandmakingdatagovernancedifficult.Suchasituation shows the need for a semantic layer to simplify and str

25、eamline data accessanduse.Businesstodaymovesquickly,andwaitingforacentralizeddatateamtoproducereportsanddashboardsfordifferentdepartmentalusecasesisnotagoodoption.10A Practical Guide to Using a Semantic Layer Ofcourse,havingmultipleBItoolsacrosstheorganizationresultsindifferingresultsforsimilarqueri

26、es.EachBItoolcomeswithitsownmodelinglayer,andallofthemsupportcustomcalculations,soitseasyenoughtocreatewildlydivergentreportsoffofthesamedata.Thatsnotevenaccountingfortablejoinerrors,flawedtime-basedcalculations,orjustsimpleformulamistakes.Thisleadstoacommonconsequence:#4ReportsfromdifferentBItools

27、use similar termsbutshowdifferent results11A Practical Guide to Using a Semantic Layer #5Businessexecsexpressdoubt in the numbersHowever,thisisnttherealityformostbusinessestoday.Manycompaniescannot be sure of the reliability of their data.Thisintroducesdoubtanddelaysindecision-makingasignificantdraw

28、backconsideringthattrustindataisamajorcompetitiveadvantage.Usingonesourceoftruthnaturallyleadstomoretrustinthedata,soifyoufindyourbusinessusersemployingdifferentanalyticstoolstodotheiranalyses,youmaybesufferingfromaconfidencecrisisthatasemanticlayercouldsolve.Thereareseveralapproachestoimplementinga

29、semanticlayerinyourorganizations.Belowisatablewiththeprosandconsforeach:Experianreportsthatsixin10companiesbelievethathigh-qualitydataincreasesbusinessefficiency,44%believeitraisesconsumertrust,43%concludeitenhancescustomersatisfaction,42%believeitdrivesmoreinformeddecision-making,and41%reportthatgo

30、oddatacutscosts.12A Practical Guide to Using a Semantic Layer ACross-IndustryChallengeThepotentialforcreatingvaluewithdata,analytics,andenterpriseAIisacross-industryopportunity.Whileusecasesmayvaryacrossindustryandorganizationanddepartments,theiraresomefundamentalchallengesthatspanallindustries.Inth

31、issection,wellexplorecommonusecasesforasemanticlayerdrawnfromdifferentindustries.13A Practical Guide to Using a Semantic Layer HealthcareManypharmaceuticalandhealthcarecompaniesoperateinhighlycomplexandheavilyregulatedindustries.Asyoumightimagine,theirbusinessesdependondataforsuccess.Someteamschoose

32、tobuildtheirowndataandanalyticsplatformsormakeuseofpre-existingcomponents.Ineitherscenario,asemanticlayerhelpstodemocratizeaccesstodataacrossthecompany.Amongthemanybenefitsofthisapproachisallowinghealthcarecompaniestofocusdataandanalyticseffortsonactivitiesthatimpactprofitandloss.Forapharmaceuticalc

33、ompany,evenasinglepercentagepointofefficiencyimprovementscouldhaveatremendousimpactonmargins.Thegoalistotakeaforward-facing,predictiveapproachtodata,ratherthansimplylookingbackonreportsofwhathasalreadyhappened.Thisapproachalsodramaticallysimplifiestheirdataaccuracy,andreducesreplicationofdataacrossm

34、ultipledatastores.Inaddition,itprovidescommoncontrolsandasharedbacklogsothatbusinessandITteamscandefineworkinbigroomplanningsessionsandpullworkfromacommonbacklogforsprints.Finally,asemanticlayerprovidescrucialsecurityandgovernancecontrols,sothatsensitiveinformationremainsprotected(butmoreonthatlater

35、).14A Practical Guide to Using a Semantic Layer RetailandeCommerce RetailersandeCommerceprovidersrelyontheirdataandinfrastructuretocompete.Withaplethoraofoptionsavailabletoshoppersbothonlineandin-store,theretailerswiththebestdata-drivenstrategiescanprovidehighlytailoredrecommendationsandadapttochang

36、ingcustomerpreferences.Thisagilitystemsfromtheabilityofeveryoneontheteamtobeadataanalyst.Whendealingwithahighvolumeoftrafficandtheresultingmountainofdata,thedatateamstoppriorityisempoweringbusinessuserstoleveragewhicheverdatatoolstheylikebestwhileenablingthemtogetreliable,accurateanswersquickly.Adop

37、tingasemanticlayerfromAtScalehelpsteamsacceleratetimetoinsightfromdata,agnosticoftheirunderlyinginfrastructure.Second,dataproducersneedasetoftechnologiesinordertodotheirjobswell.Thiscouldincludeunderlyingdimensionalmodelsortrainingsetsforamachinelearningmodel.Finally,infrastructurepowerstheactivitie

38、sofbothusergroups(thisincludescomputeenginesandstoragesystemsfordata.)Manylargeretailershaveundertakenatransformationtocloud-basedinfrastructure,whichprovidesaperfecttestcasetouseAtScale.Thegoalistodriveenduseradoptionofcloudtechnologiesthroughtheimplementationofasemanticlayerthatdemocratizedataacce

39、ss.15A Practical Guide to Using a Semantic Layer Consumer PackagedGoodsFordatateamsatconsumerpackagedgoods(CPG)companies,itsanaturalfittoinstitutionalizetheideaofdataasaproduct.Inotherwords,theirteamtreatsdataasapathtounlockingvalueforthebusinessuser.SmartCPGcompaniesleverageconceptsfromengineeringa

40、ndproductmanagementinthesoftwareworldandapplythoseapproachestodata,withimpressiveresults.OnemajorCPGhassuccessfullymanagedtoreducedatasilosandenablebusinessuserstoconsumeitusingasemanticlayer.Theyhavedevelopedalogicalmodelforthebusinessthatservesasasortof“digitaltwin”forthephysicalbusiness.Thisseman

41、tic,logicalmodelmakesitpossibleforbusinessuserstoquerydataandgetanswersusingtermsthattheteamalreadyunderstands.Plus,byleveragingAtScalessemanticlayer,thiscompanycanseparatetheconsumptionofdatafromwherethatdataactuallylivesandhowitisstored.Thisway,datacanliveanywhereandinanyformatwithoutslowingpeople

42、down.Anybusinessusercanaskquestionsandfeelconfidentthattheyarereceivingcorrect,consistentanswers.16A Practical Guide to Using a Semantic Layer FinancialServicesLegacydatastructures,however,cancreateexcessivesilos.Asdatavolumesgrow,businessintelligence(BI),development,anddatabaseengineeringteamsspend

43、significanttimemanagingcachesandmanuallyjoiningdatafromvarioussources.Meanwhile,thebusinesshastobeartheriskassociatedwith penalties due to inaccurate or late reporting.InvestinginasemanticlayercanautomatethemanagementofdataengineeringpreviouslydonebybusyBIteams.Automation can take the manpower out o

44、f datapreparation,byaggregatingrawdatabasedonenduserbehavior.Allofthiscanbedonewhileenhancingexistingsecurityandgovernancecontrols,andmitigatingriskofinaccuratereporting.Asaresult,analystshaveperformantaccessviaasinglesourceoftruth,meetingregulatoryrequirements.Thisrestorestrustinanalyticsandallowse

45、ngineering,BIanddataengineeringteams to spend their time on more productiveactivities.Withasemanticlayer,financialservicesorganizationscansavemillionsintotalcostofownershipfromanalytics,whilesimultaneouslyavoidingtheriskofregulatorypenalties.Duetofederalreportingrequirements,hundredsofanalystsmaynee

46、dtodrilldown across thousands of business calculations to properlysignoffandfilereportsonaregularbasis.17A Practical Guide to Using a Semantic Layer Key Considerations forChoosingaSemantic LayerNowthatyouhaveasenseofhowasemanticlayercansolvecommondatachallengesacrossdifferentindustries,letstalkabout

47、howtogoaboutselectingandimplementingone.Determiningatechnologystrategytoimplementasemanticlayercanbedaunting,butthereareeightkeyconsiderationsto keep in mind as you pick the best approach for your organization.Followingthesegeneralconsiderations,wecomparefouralternativetechnologyapproachesthatorgani

48、zationsmayconsideroutsideofapurpose-builtsemanticlayerplatformlikeAtScale.18A Practical Guide to Using a Semantic Layer #1 NOT TIED TO A SINGLE CONSUMPTION STYLEAsanalyticshavespreadmorewithinorganizations,relyingononeBIorAI/MLplatformtomeeteveryonesneedsisbecominglessrealistic.Also,asemanticlayer t

49、ied to one set of consumption tools isbydesignnot“universal”andinalandscape of many tools and analytics user personas,itscrucialtochooseasemanticlayerdecoupledfromasingleconsumptionstyleoranalyticstool.#2 OFFERS TABULAR AND MULTIDIMENSIONAL VIEWSSemanticlayerscomeintwoflavors:tabularandmultidimensio

50、nal.Thetabular(orrelational)modelbecamepopularinthe70sand80sandreliedon concepts like fact and dimensional tables.Toolsbasedonthismodelweredesignedtomakerelationaldatabasesordatawarehouseseasiertoquery.Multidimensionaldatalayersgoonestepfurtherbydefiningrelationshipsandaggregationrulesandaddingbusin

51、ess-friendlycontextwhilenegatingtheneedforSQL.Itsessentialtochooseasemanticlayertoolthatoffersbothviewstocoverabroaderrangeofusesandconsumptionstyles#3 SUPPORTS DATA PLATFORM VIRTUALIZATIONDatahaslivedinlotsofdifferenthomesovertheyears.Firstitwasthemainframe,thentherelationaldatabase,followedbytheda

52、tawarehouse,theMPPdatabase,thedatalake,andbackagaintothe(thistime,cloud-hosted)datawarehouse.Theseevolutionshavebroughtsignificantchangestohowdataisaccessedandused,andsavvyorganizationshedgeagainstdataobsolescencethroughvirtualization.Virtualizationeliminatesthecostofdatamigrationseverytimeanewtrend

53、gripstheindustry.Asemanticlayervendorshouldofferdatavirtualizationtoabstractawayplatformdifferencesandminimizelock-in.#4 EASY MODEL DEVELOPMENT AND SHARINGRawdataisnear-useless,butaddingadata model to it makes it consumable information.Theidealsemanticlayervendorshouldenableeasyauthoring,sharing,and

54、collaboratingondatamodels.It should also allow the reuse of common objectsandconformeddimensions,theabilitytomodeldatavisually,andacode-basedapproachthatscompatiblewithyourorganizationssoftwaredevelopmentlifecycle.19A Practical Guide to Using a Semantic Layer #5 ABILITY TO EXPRESS DIFFERENT BUSINESS

55、 CONCEPTS AND FUNCTIONSRelationaldataisflexibleandpowerfulbutoftendifficulttoexpresshigh-levelbusinessconstructswith.Theseconstructsincludetime-basedcalculations(e.g.,period-over-period),semi-additivemetrics,ancestor/predecessorfunctions,etc.ExpressingthesecomputationsinSQLischallenging,sochooseasem

56、anticlayerthatsupportsbusinessconstructsandcore analytics requirements around time intelligenceandhierarchicalroll-ups.#6 QUERY PERFORMANCE AND CACHINGQueryperformanceandcachingarecriticalconsiderationsintheselectionprocess.A semantic layer needs consistent and performanttobeofanyusetoitsusers,whoex

57、pectblazinglyfastperformancefromproprietarydatabases.Thisisnteasyconsideringthatmanyoftodaysqueriesoftenincludeheterogeneousdatabasejoinsthatfurthertaxqueryperformance.Toovercomethischallenge,chooseasemanticlayervendorthatincludesacomprehensiveperformancemanagementsystembeyondsimplecachingtechniques

58、.#7 SUPPORT FOR BI AND DATA SCIENCE WORKLOADSTheneedforclean,usabledatadoesntendwithjustthebusinessanalystasreferencedabove,datascientistsspendapproximately45%oftheirtimejustpreppingdataforuse.Acommondatalanguageandbusinesstermsaremorelikely to ensure business analysts and datascientistshavethesamec

59、ontextandproduceconsistentresultsandpredictions.Chooseasemanticlayerthatsupportsvariousworkloads,includingbusinessintelligenceanddatascience.#8 SECURITY&GOVERNANCEBecausethesemanticlayersitsbetweentheorganizationsdataandtheanalyticstoolsthataccessthatdata,theplatformmustintegratewithyourorganization

60、ssecurityinfrastructure.Thiscanhappenintwoways:authenticationandauthorization.First,thesemanticlayermustintegratewithanyexistingsinglesign-oninfrastructuretoauthenticateusers,whetherthroughActiveDirectory,LDAP,OAuth,oranyotherauthenticationplatform.Second,the semantic layer must include the ability

61、tomasksensitivecolumns,limitdatarowsbasedonuseraccessrules,and,crucially,impersonateuserswhenqueryingunderlyingsources.Chooseasemanticlayerthatincorporatesthesetwocriticalsecurityandgovernanceprotocols.20A Practical Guide to Using a Semantic Layer DATA WAREHOUSE/DATA MARTSBUSINESS INTELLIGENCE PLATF

62、ORMSEXAMPLE VENDORSEXAMPLE VENDORSPROSPROSCONSCONSAdatabaseofinformationfromavarietyofdatasourcesTraditionalBIplatformsthatbundledatamodeling,querymanagementandvisualizationSinglesourceoftruthNoextratechnologyneededWidestarrayoftool/queryaccessTightintegrationBusinessuserfriendlyEasy to secureNotfri

63、endlyforbusiness users(tables,columns)Semantic layerspecifictoBItoolonly(notReusable)Slowtointegratenew data sourcesDependence on ITVendor lock in+-+-Snowflake,AmazonRedshift,GoogleBigQuery,AzureSynapseSQLAnalytics.Tableu,PowerBI,IBM,Cognos,SAPBusinessObjects,LookerOLAP PLATFORMSDATA VIRTUALIZATION

64、PLATFORMSEXAMPLE VENDORSEXAMPLE VENDORSPROSPROSCONSCONSAconventionalcube-basedOLAPapproachtoacceleratinganalyticsqueriesbasedonpre-defineddimensionsandmetrics.Platformsthatabstractawaythephysical source and location in a tabularformatAcceleratedqueryperformanceProvidesflexibilityinhow/wheredatais st

65、oredCost efficientGoodintegrationwithMicrosoftBIstack(PowerBIandExcel)Semantic layer can be used acrossavarietyof toolsComplex data pipelines and difficult to make changesSemantic layer is not exposed to otherBIplatformsNotfriendlyforbusiness users(tables,columns)Data is extracted and disconnected f

66、rom EDWDependence on ITData models needto be built beforeaccessingdataQueryperformanceisnotguaranteedand/orneedmanualtuning+-+-MicrosoftSSASorPowerBIPremium,Kyligence,KyvosDenodo,Dremio21A Practical Guide to Using a Semantic Layer A Framework forEvaluatingSemantic Layer SolutionsThefollowingchecklis

67、tincludesdetailedcapabilitiesforyoutoevaluatepotentialsemanticlayersolutions.Theremaybeotherconsiderationsuniquetoyourorganization,butthiscanserveasastartasyouplantrialsandproofofvalueprojects.22A Practical Guide to Using a Semantic Layer TOTALUseCasesFeatureFeature CategoryScore(1-5,5=best)(1-5,5=b

68、est)Weigh(1-5,5=best)Weighted scoreSupportsanalyticalworkloadsSupportsdatascienceworkloadsSupportslegacy,on-premisedatawarehousesSupportson-premiseandclouddatalakesSupportsSaasdatasources(Salesforce,Workday)SupportstoolsthatspeakSQLviaJDBSorODBSSupportstoolsthatspeakMDXorDAXandliveExcelconnectionsSu

69、pportscustomapplicationsviaRESTorPhytoninterfacesSupportsTimeintelligence(periodoverperiod,periodtodate)SupportszeroclientinstallfordataconsumersSupportsMDX,DAX,preandpostquerycalculationsSupportswebbaseddevelopment(versusclientapplication)Supportsaggregationfunctions(SUN,AVG,MAX,MIN)Supportsdialect

70、specificoptimizationsSupportssinglesignonforalldataconsumersSupportsuserimpersonationanddelegatedauthorizationSupportsandrespectsnativedataplatformsecurityconstructsSupportsrowlevelsecurityforusersandgroupsSupportsrowlevelsecurityforusersandgroupsSupportscolumnhidingandmaskingforusersandgroupsSuppor

71、tsmultiple,simultaneouseditorsforvirtualviewdevelopmentSupportsnon-additivemetrics(DistinctCount,First,Last)SupportsreusableobjectsandmodelcomponentsharingSupportsliveExcelpivottablesandExcelCUBEfunctionsSupportsdevelopmentlifestyle(dev/test/prod)SupportsautomatedqueryperformancemanagementConnectivi

72、ty(northbound&southbound)DevelopmentEnvironmentCalculations and Analytical Functions(OLAP)QueryPerformance&CatchingSecurity&Governance23A Practical Guide to Using a Semantic Layer Thefollowingtablessummarizetheseresults.Youcandownloadthefullbenchmarkstudieshere.(embedthislinkhttps:/ Common Cloud Dat

73、a WarehousesModernclouddataplatformshavebeenagamechangerforenterpriseanalyticsprograms.CostefficientcloudstorageandpowerfulSQL-basedqueryenginesmakesitfeasibletomakepetabytesofdataavailableforanalysis.Whendatateamsareproactiveinmakingawiderangeofapplicationdataavailablein“analysis-ready”format,organ

74、izationsarepositionedtodrivetowardafasterrateofdata-driveninsightcreation.HoweverdataavailabilityandSQLaccesstodoesnotautomatically mean data can be deliveredat“speedofthought”orwithintrulyinteractivedatadashboards.Therawscaleofmodernclouddataoftenresultinmulti-secondorlongerquerytimes.Performancela

75、gsleadtoworkaroundsanddata extracts which break semanticconsistency.Thekeytomaintainingasemanticlayerstrategyistodeliverconsistentlyfastperformanceandmanagingcosts.AtScalewasdesignedtodeliverspeedofthoughtperformancewhileeliminatingtheneedtoextractdatalikeconventionalOLAPapproaches.Todemonstratethis

76、,weran20queriesbothwithandwithoutAtScale,usingthestandardTPC-DSbenchmarkv2.11.0fromtheTransactionProcessingCouncil(TPC)forourtests.AtScalesAccelerationStructuresshowedmajorbenefitsinacceleratingqueryperformance-bothrawqueryperformanceandwhenmanagingmultipeconcurrentusers.Wealsopresentmeasuresof simp

77、lification based on the reductioninSQLsyntax.24A Practical Guide to Using a Semantic Layer TESTRedshiftimprovement Factor with AtScaleQueryPerformance12.5xFasterUserConcurrency61x FasterCompute Cost2.6xCheaperComplexity76%LesscomplexSQLquerlesTESTAzure Synapse Analytics SQLimprovement Factor with At

78、ScaleQueryPerformance3x FasterUserConcurrency9xFasterCompute Cost2x CheaperComplexity76%LesscomplexSQLquerlesTESTGoogle BigQueryimprovement Factor with AtScaleQueryPerformance7.7xFasterUserConcurrency20 xFasterCompute Cost10 xCheaperComplexity76%LesscomplexSQLquerlesTESTSnowflakeimprovement Factor w

79、ith AtScaleQueryPerformance4x FasterUserConcurrency14x FasterCompute Cost3.7xCheaperComplexity76%LesscomplexSQLquerles25A Practical Guide to Using a Semantic Layer MakingtheBusinessCasefora Semantic LayerOverthepastseveralyears,organizationshavefocusedtheiranalyticsinvestmentsonembracingmajorcloudda

80、taplatforms.Whilemoderndataplatformssimplifyoperationsandsolvemanyproblems,theydonotaddressthefundamentialproblemsofgettingactionabledatatotheconsumersthatwantit.Buildingabusinesscaseforinvestinginasemanticlayerstrategyshouldfocusonthreeimportantvaluedrivers:1.Increasethevalueofdatabymakingiteasiert

81、ocombinemoredatasets,largedatasets,andwiderwindowsofdatainawaytheyareaccessibletomoredataconsumers.2.Reducethecostofdeliveringanalysis-readydatabyoptimizingcloudspend,simplifyingdataengineeringanddataops,andmakingdataconsumersmoreproductive.3.Increasingthenumberofdataconsumersbymakingiteasiertointer

82、actwithdataassets.Improvingdataliteracyinanorganizationopensuptheflowofdatadriveninsights.26A Practical Guide to Using a Semantic Layer Take the NextStepREQUESTADEMOCONTACTUSLEARNMOREReadtheTechnologyPlatformOverviewforAtScalesSemantic Layers SolutionBuyersGuide:https:/ advicefromfellowdata&analytic

83、sleaders on how to scalesmarterdata-drivendecision-makingStaycurrentonanalyticsstrategieswithshortarticles and webinarsontopicsinanalyticsstrategiesAbout AtScaleAtScaleenablessmarterdecision-makingbyacceleratingtheflowofdata-driveninsights.Thecompanyssemanticlayerplatformsimplifies,accelerates,andex

84、tendsbusinessintelligenceanddatasciencecapabilitiesforenterprisecustomersacrossallindustries.WithAtScale,customersareempoweredtodemocratizedata,implementself-serviceBIandbuildamoreagileanalyticsinfrastructureforbetter,moreimpactfuldecisionmaking.Formoreinformation,andfollowusonLinkedIn,TwitterorFacebook.