摩根大通-2026+新兴技术趋势前瞻++2026+Emerging+Technology+Trends

2026

Emerging

Technology

Trends

JPMCInternalUseOnly

Introduction

OurGlobalTechnologyStrategy,InnovationandPartnershipsteamhelpsensurethatJPMorganChaseremainsconnectedtoinnovativeandemergingtrendsinboththeimmediateandlongterm.Eachyear,theteamputstogetheracollectionofemergingtechnologytrends,alongwithmarketandindustryperspectives.

Thisyear’sreportidentifieskeypredictionsanddistillsthemostmeaningfultechnol-ogytrendsbyprovidingaconciseoverviewofeach,offeringinsightsfromthebroad-ermarketandindustry.Thetrendsrepresentpivotalareasofinnovation,identifiedthroughcontinuousecosystemconnectivity,internalbenchmarks,detailedresearchandinsightfulconversationswithdomainexpertsacrossJPMorganChaseandexter-nally.

Thereporthighlightsasetofhigh-impactpredictionsandthemesthatarerapidlyreshapingthetechnologylandscape

Context-drivenarchitectureswillbeeverything:SuccessinenterpriseAInowde-pendsonenablingagentstosecurelyaccessthemostrelevantdataandtools,em-poweringdifferentiatedproductsandservices.Asautomationtransformsthesoft-waredevelopmentlifecycle,thefocusisshiftingfrommanualcodingtoarchitectingcontext-richapplications.InferencedemanddrivescontinuedAIbuildout:Thede-mandforAIinferenceandinfrastructureshowsnosignsofslowing,withongoinginnovationindatacenterdesign,siliconanddeliverynetworksformingthebackboneforfuturesoftwareandecosystemdevelopment.

InferencedemanddrivescontinuedAIbuildout:ThedemandforAIinferenceandinfrastructureshowsnosignsofslowing,withongoinginnovationindatacenterdesign,siliconanddeliverynetworksformingthebackboneforfuturesoftwareandecosystemdevelopment.

Theendofappswitching;Intentisthenewinterface:Thedominantinterfaceisshiftingfromappsandbrowserstoasingle,AI-nativeenvironment.Thisnewpar-adigmcollapseseveryworkflowintoonecontinuouslypersonalizedstream,whereintelligentinterfacesanticipate,executeandtransactacrossmodalities.

AI-poweredsimulationenhancestesting:Organizationsareincreasinglyrelyingonadvancedsimulationstotest,validateandoptimizeproducts,processesandsce-nariosbeforereal-worlddeployment.Thesevirtualenvironmentsenablecontinuous,scalableexperimentation,andrapiditeration.

Asthesetrendsmature,organizationsshouldalignadoptionwithdisciplinedgover-nanceasrisksandregulatoryexpectationscontinuetoevolve.

Aswelookahead,the2026EmergingTechnologyTrendsReportunderscoreshowtheconvergenceofthesetrendsisredefiningwhat’spossibleforglobalenterprises.

2Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

TableofContentsbyPrediction

PhysicalAI7

Privatemarketdatainsights9

Knowledgegraphsand11

semanticlayers

DataformatsforAI13

Contextengineering15

Reinforcementlearning17

environments

Contextengineeringfor19

end-to-endsoftwaredevelopment

EvaluationofAIprotocols AgenticSRE ObservingAI Datacentricsecuritypolicy AgenticIAM Humanriskmanagement

Context-drivenarchitectureswillbeeverything

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25

27

29

31

InferencedemanddrivescontinuedAIbuildout

AIinfrastructureinnovation35

CloudnativeAIinference37

QuantumComputing39

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Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Agenticbrowsers43

AInativeworkspaces45

Generativeuserexperiences47

Multi-modalsociallistening49

Agenticwearables51

Theendofappswitching;Intentisthenewinterface

Syntheticusers/usersimulations55

Proactivedefenseattacksimulation57

AIpoweredsimulationenhancestesting

44Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

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Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Context-drivenarchitectureswillbeeverything

ThesuccessofenterpriseAIinitiativesisreliantonenablingAIagentstoeffectivelyandsecurelyaccessthemostrelevantdataandtools,empoweringthemtodeliveruniqueanddifferentiatedproductsandservicestocustomersandclients.Asend-to-endautomationtrans-formsthesoftwaredevelopmentlifecycletosupportthevolumeofcodegeneratedbyAItools,developerswillfocuslessonmanualcod-ingandmoreonarchitectingcontext-richapplications,usingAItoolsandcontextengineeringtechniques.

6Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Context-drivenarchitectureswillbeeverything

PhysicalAI

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Integrationofartificialintelligencewithreal-worldenvironments,enablingsmartdevicesandrobotstoautonomouslyperceive,reason,andinteractthroughsensorsandedgedevices.

PhysicalAIrepresentstheconvergenceofartificialintelligenceandphysicalhardwaresystems,empoweringintelligentagentstoper-ceive,reasonandinteractwiththerealworldthroughsensors,actu-atorsandedgedevices.Thisintegrationenablesrobots,automatedmachinesandsmartsystemstoact,learnandadaptwithinphysicalenvironments,effectivelybridgingthedigitalandphysicalrealms.

PhysicalAImodelsaretrainedonphysicalinteractions,spatialrelationshipsandthelawsofphysicsthemselves.Usingadvancedsimulationenvironments,thesesystemscanexperiencemillionsofscenariosinvirtualreplicasoftherealworld,learninghowobjectsbehave,howforcesinteract,andhowtomanipulatephysicalspace.Throughtechniqueslikereinforcementlearning,sim-to-realtransfer,andsyntheticdatageneration,modelsdevelopanintuitiveunderstandingofphysics,geometryandcausalitythatenablesthemtooperateeffectivelyindynamic,unstructuredenvironments.Oncetrainedinsimulation,thesemodelsaredeployedtophysicalhardwarewheretheycontinuelearningfromreal-worldfeedback,constantlyrefiningtheirunderstandingofphysicalreality.

Theapplicationsspanmanufacturing,logistics,androbotics.Inwarehouses,roboticsystemstrainedonmillionsofsimulatedpick-and-placescenarioscanhandlediverseobjectsthey’veneverencountered,adaptinggripstrengthandapproachanglesbasedonvisualandtactilefeedback.Onmanufacturingfloors,AI-pow-eredqualityinspectionsystemslearntodetectdefectsacrossvaryinglightingconditionsandproductvariationsbytrainingonsyntheticdatasetsthatmirrorrealproductionenvironments.

Autonomousmobilerobotsnavigatecomplexfacilitiesbycom-biningpre-trainedspatialreasoningmodelswithreal-timesensorfusion,learningoptimalpathswhileavoidingdynamicobstacles.Roboticarmsinassemblylineslearnintricatetasksthroughdemon-strationandpracticeinsimulation,thentransferthatknowledgetophysicalproductionwithminimalreal-worldfine-tuning.

ThetrendtowardAI-drivenphysicalautomationisbeingpropelledbyseveralconvergingtechnologicalandeconomicfactors.Simu-lationbreakthroughshaveenabledadvancedphysicsenginesanddigitaltwinplatformstocreatephotorealistic,physics-accuratevirtualtrainingenvironmentsatscale,whileimprovedsim-to-realtransfertechniqueshavesignificantlyreducedtherealitygapbyallowingknowledgegainedinsimulatedenvironmentstotranslatemoreeffectivelytophysicalapplications.AI-poweredsyntheticdatagenerationtoolsnowproduceunlimitedlabeledtrainingdatarepresentingdiversephysicalscenarios,complementedbyhardwareadvancesthatbringmorepowerfuledgecomputingchipsandsensorscapableofreal-timeAIprocessingdirectlytothepointofaction.TheproliferationofIoTdevicesandsensorshastrig-geredadataexplosion,generatingvastamountsofphysicalworlddatathatenablescontinuouslearningandoptimization.Meanwhile,thedecreasingcostsofroboticscomponents,sensors,andcom-putepowerhavemadedeploymenteconomicallyviableacrossin-dustries.Finally,persistentlaborchallenges,particularlyworkforceshortagesinmanufacturingandlogistics,aredrivingheighteneddemandforintelligentautomationsystemscapableofadaptingtovariedandcomplextasks.

Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Marketandindustryperspectives

McKinseypredictsthephysicalAImarketisprojectedtoreach$370B+by2040drivenbyenterpriseadoptionacrossdiverseindustryapplicationssuchasfacili-tiesmanagement,physicalsecurity,manufacturingandlogistics.¹ThisisdrivenbyinvestmentsinplatformsthatdelivermeasurableROIthroughenergysavings,laborreductionandpredictivemaintenance.

PhysicalAIhasemergedwithavarietyofinitialusecaseapplicationsacrossmulti-pledomains.SmartbuildingandIoTplatformsaredeployingAI-poweredbuildingmanagementandenvironmentalcontrolsystemsthatoptimizefacilityoperations.SpatialintelligenceplatformsarebeingdevelopedbycompaniesbuildingAImodelsthatunderstandthree-dimensionalphysicalspacesandenableautonomousnavigationthroughcomplexenvironments.EmbodiedAIandroboticsapplicationsareintroducingphysicalrobotsandautonomousagentscapableofperforminginspection,delivery,andvariousfacilitytasks.SimulationandtrainingplatformsarecreatingsyntheticenvironmentsspecificallydesignedtotrainPhysicalAIsystemsbeforereal-worlddeployment.ComputervisionandperceptiontechnologiesareprovidingvisualAIcapabilitiesformonitoring,security,inspection,andsafetyapplicationsacrossindustries.Finally,edgeAIinfrastructureisdeliveringthehardwareandsoftwarenecessarytoenableAIprocessingdirectlyatendpointswithoutrelyingoncloudconnectivity,ensuringfasterresponsetimesandgreateroperationalindependence.

PhysicalAIpresentsseveralkeyimplicationsfortheenterpriseacrossoperationaldomains.Insmartbuildings,securitycamerasareleveragingAItodetectreal-timethreatsandsendimmediatealerts,whileaccesscontrolsystemsutilizebiometricsforenhancedsecurity,andbuildingmanagementsystemsautonomouslycontroltem-perature,lighting,andventilationbasedonoccupancypatternsandenvironmentalconditions.Autonomousoperationsaretransformingwarehouses,manufacturingfacilities,andlogisticsoperationsthroughthedeploymentofAI-poweredrobotsthathandlematerialmovement,conductqualityinspections,andmanagedeliverytasks.EnhancedcustomerexperiencesarebeingrealizedinretailenvironmentswherePhysicalAIenablessophisticatedinventorymanagement,checkoutautomation,andpersonalizedin-storeassistancethatadaptstoindividualshopperneeds.Additionally,predictivemaintenancecapabilitiesarerevolutionizingindustrialoper-ationsasequipmentoutfittedwithsensorsandAIcanpredictfailuresbeforetheyoccur,significantlyreducingcostlydowntimeandenablingproactiveintervention.

¹WillembodiedAIcreateroboticcoworkers?.McKinsey&Company.(2025,June30).

/industries/industrials-and-electronics/our-insights/will-em

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bodied-ai-create-robotic-coworkers.8

Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Context-drivenarchitectureswillbeeverything

Privatemarketdatainsights

exchangefortheAIeconomy

Expansionofreal-timeinsightsfromstructuredprivatemarketdatathroughseamless,consump-tion-basedexchanges,andenablingthetradingofdataasanassetclass.

Traditionalprivatemarketdataprovidersareknownforprovidingdataattributestiedtocompanyenti-ties(e.g.,funding,valuation,investors,jobpostings),brands(e.g.,adversemedia),transactions(e.g.,lo-cation,time),people,andbeyond.ThisprivatedataistypicallyingestedbycorporationsthroughanAPIfeedandfurtherusedtoenrichexistingfirmograph-icdata.Onechallengewiththisexistingapproachisthat(1)privatemarketsdataisoftennotstructuredusingauniversalentityframeworkand(2)dataisoftenstuckbehindwalledgardens,or(3)contractu-allylimitedtospecificusecasesandindividualuseraccess,therebybeingunderutilized.

Therapidgrowthofbothunstructureddataaswellasagenticcapabilities,hasgivenrisetoPrivateMarketDataInsightsExchanges.Theselivedataex-changesarebuiltonproprietaryentityframeworkswhichhelpfacilitatethebuyingandsellingoftrans-

actionaldatatoabroaderpopulationofend-usersduetolesscostbarriers.Inaddition,someexchang-esleverageawaterfallenrichmentmethod,whereanindividualdataattribute(e.g.,revenue,funding,e-mailcontact)isindexedacrossmultiplesourcedataprovidersandthenscoredtomaximizedataqualityandcoverage.Further,theseexchangeshavepotentialtopowerLLMsandenterpriseappli-cationsonaconsumptionbasedmodel.

Thisconsumptionbasedmodelwillinvolvetradingdataasanassetclass,wheretheseexchangeswilloperatesimilarlytotradingsecurities.AIagentswillcommunicatewithotherAIagentstoexchangedatainsights,andfurtherthroughmodelcontextprotocol(MCP)capabilitieshavepotentialtogener-ateexecutivelevelinsightorientedtasksinamuchmorestreamlinedmannerthanhumans.

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Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Marketandindustryperspectives

TheestimatedtotaladdressablemarketforPrivateMarketsDataisexpectedtogrowata14.5%CAGRfrom$8Bin2024to$18Bby2030(BlackRock).²Thisisdrivenbyoverallincreaseddemandforaccesstoprivatemarkets,desiretoenhanceclientof-ferings(e.g.,deeperinsightsintoalternativeinvestments)andtogainunderstandingofperformanceanddriversofreturns.

TheemergenceofPrivateMarketDataInsightsExchangeswilldrivethefollowingoutcomes:

ImprovementinStructuredDatasets:Theriseofunstructureddatasets,makesdatarationalizationdifficult.Withtheuseofproprietaryentityframeworks,thiscanbeim-provedbyleveraginguniqueidentifierstoaggregatedisparatedatasources

ImprovementinVerifiableData:Noveltechniquessuchaswaterfallenrichmenthelpstriagingofdatasetsfordatacompletenessandverifiability

BroaderAccesstoPrivateMarketData:Emergingdatainsightsexchangeswillgainmaterialadoptionbynativelyofferinghundredsofprimarydatasources,whichwillfurtherbeleveragedviaAPIdatafeedsintohomegrowndatabasesinsomescenari-osthiswillreplaceentireusageoftraditionalmarketdataproviders

ActionableInsights&Recommendations:Abilitytoautomatetheanalysisofrawdata,andsegmentdatawiththeassistanceofAItogeneratecriticalinsightsandmessagingtodesiredaudiences,suchasinternalexecutivesorexternalsalestargets

AcceleratedAdoptionofAIResearchAgents:LeveragingAItoassistindailytasks(e.g.,researchandquerying,automatede-mailreachoutcampaignsforGTMteams)andstayaheadofemergingmarketsignals(e.g.,headcountchanges,sociallisten-ing,productreviews,adversemedia)

Consumption-BasedModel/DataAsAnAssetClass:Traditionalmarketdatapro-vidersmaybenefitfromarevenuesharemodelwiththeemergenceofprivatemarketdatainsightsexchange;howevertraditionalprovidersmaybenegativelyimpactedbythecannibalizationoftheircoreuserbase.

²WillembodiedAIcreateroboticcoworkers?.McKinsey&Company.(2025,June30).

/industries/industrials-and-electronics/our-insights/will-em

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bodied-ai-create-robotic-coworkers10

Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Context-drivenarchitectureswillbeeverything

Knowledgegraphs

&semanticlayers

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Knowledgegraphsarereshapingenterprisedatastrategiesbymak-inginformationmoreaccessible,contextualandactionable.

EnterpriseAIisundergoingadecisiveshiftfromstateless,prompt-driveninteractionstocontext-rich,governedsystems.Withcontextengineeringemergingasanewpracticetoprovidetherightcontextforamodeltooptimallycompleteatask,knowledgegraphsarepoisedtobeoneofthefoundationaltechnologiesfordeliveringmeaningfulcontexttoAIsystems.Byprovidingpersistentmemoryandsharedbusinesssemantics,AIagentswillgenerategroundedoutputs,withacommonsemanticfoundationlettingteamsreusedataandlogicacrossusecases,improvingthereturnonexistingdatainvestmentsandreducinghallucinations.Theyalsoserveasaseman-ticsubstrateusedatruntime,providingmodelswithacontrolplaneforbetterreasoning,contextinjectionandpolicyenforcement.

Attheheartofthisarchitecture,knowledgegraphsprovidememoryaboutentitiesandevents.Ontologieslayerontheformalbusinessvo-cabulary–definitions,constraints,rulesandrelationships–thattellsystemswhatacustomerorbusinessunitmeans,howtheentitiesrelateandwhichactionsarepermitted.Semanticlayersthenoper-ationalizethesedefinitionsasgoverned,reusableviewsandmetricsthatbothhumansandAIagentscanconsume,computeandinterpretconsistentlywithsharedentitycontext.Duetotheirabilitytoscale,knowledgegraphsandontologiesareinvaluableforlargeorganiza-tionsseekingtoorganizeandleveragetheirproprietarydata.

Thisyear,severalshiftswillmakethisstackstandardpractice.Re-trievalforLLMswillincreasinglycombinevectorsearchwithgraphreasoning,oftencalledGraphRAG,togroundanswersinenterprisefactswithtraceablecitations.AI-assistedontologytoolingwilldraftandmaintainsemanticmodelsfromschemas,logsanddocuments,keepinghumansintheloopforqualityandcompliance.ThesemanticlayerthatonceservedBIwillconvergewithAIneeds,unifyingmet-rics,accessandpolicyenforcementfordashboards,applicationsandagentsalike.Event-centricgraphswillbecomemoreprevalent,allowingagentstoreasonoversequencesliketransactionsandinter-actionsinreal-time.Interoperabilitywillimproveascommonpatternsandstandardsreducelock-inandmakemulti-agentecosystemsvi-able.

Whilestrideshavebeenmadetomodernizethisdecadesoldtechnol-ogyandimproveditsviabilityforAIapplications,challengesremaintoadoptthiswidely.Theindustrywillcontinuetoseeprogressasnewplayersinnovateandlegacysystemsareupdated,movingtowardsolutionsthatofferricherreasoningandaccuratecontext.Enterpris-esarealreadyrecognizingthestrategicvalueofknowledgegraphsandsemanticlayers,whichwillbecomeincreasinglyimportantfordatatobeAIready.

Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Marketandindustryperspectives

Theknowledgegraphmarkethasbeenaroundforyears.Earlyadoptersinthisspaceeitherfocusedonontologyorknowledgegraphs.However,wehaveseenconver-gencewithininthemarketasthespacehasgrown.

Graphcompanieshavegainedadoptionwithlargeenterprisesduetoscalabilityandfastmulti-hopqueryspeed.However,thesecompaniesstoredataascode,freezingthecodeatapointintimeratherthanhavingthedataenginefigureoutconnectionsandconclusionsitself.

Afewcompaniesrecentlyhavefocusedmoreontheontologylayer,equippingplat-formswithontologymodelsandvirtualizationenginesthataredataandapplicationagnostic,meaningtheycanconnecttoanumberofdatasourcesandtools,includingAgentSDKs.

Largerenterprisesfocusonend-to-endknowledgegraphsuitesthatsitontopofanenterprise’sdatalakehouse,enablingontologies,applicationsandagenticframe-worksinoneplatformforAIusecases.

NewerstartupsinthemarketareaimingtoprovideLLMsmemoryandknowledge.Oneapproachisthroughahybriddatastorearchitecturecombininggraph,vector,andkey-valuestores.AnotheristhroughbuildingatemporalknowledgegraphforAIagentsbuildingandupdatingitsgraphfromnotonlystructuredbusinessdata,butalsofromuserinteractions(chat,unstructuredtext),trackingwhendatabecomesinvalidorchangesovertime.

SnowflakespearheadingtheOpenSemanticInterchange(OSI)initiative,standardsforhowentities,metricsandpoliciesaredescribedandexchangedacrosstools,al-readyindicateshowtheindustryisconvergingtowardsgoverned,semanticfirstAIarchitectures.

12Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Context-drivenarchitectureswillbeeverything

DataformatsforAI

Large-scale,unstructuredmultimodaldatadrivingtheevolutionofdatainfrastructuretobettersupportagenticworkloads.

Foroveradecade,fileformatslikeParquet(2013,outofCloudera),Avro(2009,outofHadoop),andORC(2013,outofHortonworks)haveservedasthebackboneofanalyticalprocessing.Thesefor-matssitontopofobjectstorage(e.g.,S3)andaredesignedtooptimizedataforanalytics.

Sittingabovefileformatsaretableformats,whichhavebeenahighlycompetitivebattlegroundinrecentyears.Open-sourceIcebergandDeltahavesparkedintenseindustrydebate,withIcebergultimatelybecomingthemostwidelyadopted,withon-goingeffortstomakebothformatsinteroperable.Tableformatsaddametadatalayeratopfileformats,organizingrawfilesintodatabase-liketables.Thisletsusersstorealltheirdata—evenstructureddatatypicallydesignedforwarehouses—inopen,cost-effectiveformats,whilemaintainingwarehousereliabilityandperformancewithoutproprietarycopies.Userscanleverageanycomputeenginedirectlyonthedata,removingtheneedfordatamovementorduplication.

Whilecompleteownership,flexibilityandinteroperabilitywithinthisstackhaveprovidedsignificantbenefitstoenterprises,lim-itationsremain.TechnologiessuchasParquetandIceberghavebeenpredominantlycateredforstructured/tabulardata,servingbatchbusinessintelligenceworkloads(e.g.,SQL).

WiththeemergenceofGenAIandtheforthcomingwaveofagen-ticapplications—whichareproficientatprocessinghighlyun-structuredandmultimodaldata,includingdocuments,imagesandvideo—newAI-nativedataformats,bothatthefileandtablelevel,arebeingdevelopedtoaddresstheevolvingrequirementsofnext-generationAIworkloadsandfillthevoidofwhereParquetandIcebergfallshort.

OpendataformatslikeLanceprovidebothatableandfileformatspecificallydesignedforefficientsearchandretrievalofhighlycomplexmultimodaldataatmassivescale.OpenfileformatslikeNimble,developedbyMeta,addressParquet’slimitationsinAItrainingbyenablingfasterreadsandmoreefficientmemorylay-outs.VortexhasalsoemergedasaParquetalternative,optimizedforAI-nativeworkloads.WhiletheseformatsaredesignedforAIworkloads,theyalsosupporttraditionalSQLanddataengineer-ing(e.g.,Spark)processing.

Collectively,theseformatsseektoovercomethelimitationsofParquetandIceberginsupportingAIandagenticworkloads,positioninguserstomoreeffectivelyleveragehigh-dimensionalmultimodaldataatscaleforAIandagenticapplications.

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Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Marketandindustryperspectives

AccordingtoGartner,unstructureddatanowaccountsfor80to90%ofallnewenterprisedataandisgrowingthreetimesfasterthanstructureddata.3Thisshiftisdrivingmajorchangesacrossthedataecosystem.

Leadingdataplatforms,alongwithhyperscalersthathavewidelyadoptedParquetandIceberg,arerespondingtothisemergingecosystemthreat.

Toaddresstherisingcomplexityofmanagingandleveragingunstructureddata,AI-focusedcompaniesarebeginningtoshiftformats.Netflix,whereIcebergoriginallydeveloped,adoptedtheLancedataformattopoweritsmultimodaldatalake,whichincludesvideo,audio,images,text,andembeddings.Further,GenAInativecompanieshaveadoptedLanceinternallytopowerdiverseworkloads.

Giventheindustry-wideinvestmentinIceberg,andrecognizingtheshiftinglandscape,theApacheIcebergCommunityiscurrentlyreviewingtheFileFormatAPIProposal,whichseekstoestablishaunified,pluggableinterfaceforintegratingnewfileformatswithIceberg—muchlikeitscurrentsupportforParquet,Avro,andORC.

3Gartner.(2025,September17).MarketGuideforDataSecurityPostureManagement.JoergFritsch,BrianLowans,andAndrewBales.

/en/documents/6964866

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Thisreportisforinformational/discussionpurposesonlyandisnotintendedtobenorshoulditbereliedonfordecisionmaking

Context-drivenarchitecturesw