大數(shù)據(jù)存儲(chǔ)解決方案

從企業(yè)數(shù)據(jù)向大數(shù)據(jù)的擴(kuò)展TraditionalApproachStructured,analytical,logicalSystemsofRecordNewApproach

Creative,holisticthought,intuitionSystemsOfEngagementMultimediaSystemsofInsight

EnterpriseIntegration

andContextAccumulationStructured

Repeatable

LinearUnstructured

Exploratory

DynamicDataWarehouseWebLogsSocialDataTextData:

emailsSensordata:

imagesRFIDInternalAppDataTransactionDataMainframeDataOLTPSystemDataHadoopand

StreamsTraditionalSourcesNewSourcesERP

data具備洞悉能力的系統(tǒng)SystemsofInsight對(duì)新式基礎(chǔ)架構(gòu)的需求在可靠和安全的環(huán)境中處理關(guān)鍵業(yè)務(wù)應(yīng)用存取和處理海量數(shù)據(jù)——包括結(jié)構(gòu)化和非結(jié)構(gòu)化數(shù)據(jù)速度及時(shí)響應(yīng)隨時(shí)可能出現(xiàn)的商業(yè)機(jī)會(huì)，這就需要靈活、實(shí)時(shí)性的基礎(chǔ)架構(gòu)ThedynamicsofSoRandSoE:通過(guò)負(fù)載及資源部署的優(yōu)化，來(lái)增強(qiáng)靈活性和效益通過(guò)采用包括基于開(kāi)放標(biāo)準(zhǔn)的技術(shù)等新技術(shù)來(lái)改善ITeconomicsSystemofRecord（SoR）SystemsofEngagement（SoE）對(duì)的決策對(duì)的地方對(duì)的時(shí)間點(diǎn)BigData&Analytics大數(shù)據(jù)分析的新型架構(gòu)解決方案IBMBigData&AnalyticsInfrastructureDataZoneApplicationZone4SmartMeteringGridOperations電網(wǎng)管理FieldService外勤現(xiàn)場(chǎng)服務(wù)ResourcePlanning資源規(guī)劃CustomerService/CustomerOperations實(shí)現(xiàn)真正的有效的法規(guī)遵從及時(shí)發(fā)現(xiàn)能源損耗問(wèn)題、以及偷電和欺詐行為提高客戶(hù)滿(mǎn)意度電量使用預(yù)測(cè)更為精確電網(wǎng)運(yùn)維優(yōu)化減少停電次數(shù)和時(shí)間案例:SmartMetering智慧電力計(jì)費(fèi)

大數(shù)據(jù)分析應(yīng)用可以帶來(lái)真正的業(yè)務(wù)價(jià)值法規(guī)遵從案例:用大數(shù)據(jù)分析來(lái)加強(qiáng)

SmartMetering數(shù)據(jù)分析的高可用性，以確保隨時(shí)了解用戶(hù)喜好跨應(yīng)用的TB級(jí)的數(shù)據(jù)需求–通用虛擬化存儲(chǔ)平臺(tái)實(shí)時(shí)收集、存儲(chǔ)并分析數(shù)據(jù)，最快可達(dá)50,000datapoints/sec歷史用電狀態(tài)數(shù)據(jù)的復(fù)雜查詢(xún)處理數(shù)據(jù)在加載到數(shù)據(jù)倉(cāng)庫(kù)前的清洗、驗(yàn)證，這些數(shù)據(jù)可能來(lái)自很多的用戶(hù)、收費(fèi)系統(tǒng)或斷電保護(hù)系統(tǒng)關(guān)系掌控

構(gòu)建和維護(hù)電網(wǎng)的唯一試圖對(duì)整個(gè)企業(yè)的結(jié)構(gòu)化和非結(jié)構(gòu)化數(shù)據(jù)t做全局導(dǎo)覽Navigation，從中發(fā)現(xiàn)Discover價(jià)值分析用戶(hù)用電情況，偵測(cè)偷電、改表等行為預(yù)測(cè)哪些用戶(hù)適合于哪些分時(shí)時(shí)段電價(jià)或需求/響應(yīng)服務(wù)分時(shí)時(shí)段電價(jià)的實(shí)時(shí)定價(jià)或

提供及時(shí)的需求/響應(yīng)服務(wù)IBMBigData&AnalyticsReferenceArchitectureBigDataPlatformCapabilitiesInformationIngestReal-timeAnalyticsWarehouse&DataMartsAnalyticAppliancesAllDataSourcesAdvancedAnalytics/

NewInsightsNew/

EnhancedApplicationsCognitive認(rèn)知LearnDynamically?Prescriptive規(guī)范BestOutcomes?Predictive預(yù)測(cè)WhatCouldHappen?Descriptive

描述WhatHasHappened?ExplorationandDiscoveryWhatDoYouHave?StreamingDataTextDataApplicationsDataTimeSeriesGeoSpatialRelationalSocialNetworkVideo&ImageAutomatedProcessCaseManagementAnalyticApplicationsWatsonCloudServicesISVSolutionsAlertsNewInfrastructureLeveragesDataTypesDatain

MotionDataat

RestDatain

ManyFormsInformationIngestionandOperationalInformationDecision

ManagementBIandPredictiveAnalyticsNavigation

andDiscoveryIntelligence

AnalysisRawDataStructuredDataTextAnalyticsDataMiningEntityAnalyticsMachineLearningLandingArea,AnalyticsZoneandArchiveVideo/AudioNetwork/SensorEntityAnalyticsPredictiveReal-timeAnalyticsExploration,IntegratedWarehouse,andMartZonesDiscoveryDeepReflectionOperationalPredictive

StreamProcessingDataIntegrationMasterDataStreamsInformationGovernance,SecurityandBusinessContinuityBigInsightsStreamsWarehouseInfoSphereBigInsightsHadoop-based低延遲分析，針對(duì)多樣化的、海量靜態(tài)數(shù)據(jù)Data-At-RestNetezzaHighCapacityAppliance基于結(jié)構(gòu)化數(shù)據(jù)的可查詢(xún)歸檔Netezza1000基于結(jié)構(gòu)化數(shù)據(jù)的

BI+定制化分析DataSmartAnalyticsSystem基于結(jié)構(gòu)化數(shù)據(jù)的運(yùn)營(yíng)分析InformixTimeseriesTime-structuredanalyticsInfoSphereWarehouse基于結(jié)構(gòu)化數(shù)據(jù)的大容量數(shù)據(jù)分析InfoSphereStreams低延遲流數(shù)據(jù)分析Velocity,Variety&VolumeData-In-MotionMPPDataWarehouseStreamComputingInformationIntegrationHadoopInfoSphereInformationServer海量數(shù)據(jù)集成和轉(zhuǎn)化ApacheHadoop:跨服務(wù)器集群的大數(shù)據(jù)集分布式處理開(kāi)放系統(tǒng)框架，采用的是一種簡(jiǎn)單化編程模型IBMBigDataPlatform大數(shù)據(jù)平臺(tái)What:一種開(kāi)源軟件，將數(shù)據(jù)計(jì)算分布到整個(gè)集群的常見(jiàn)商用服務(wù)器和存儲(chǔ)上Why:傳統(tǒng)的計(jì)算架構(gòu)是一種沿縱向擴(kuò)展模式，通過(guò)更快的SAN、大容量?jī)?nèi)存和多級(jí)緩存將數(shù)據(jù)加載到CPU上，成本比較高。What:Hadoop把大數(shù)據(jù)集合拆分區(qū)劃為小數(shù)據(jù)集合，再把小數(shù)據(jù)集合分發(fā)到多臺(tái)普通服務(wù)器上，是一種橫向擴(kuò)展模式。Why:Scalable,Flexible,CostEffective,FaultTolerentComponents:MapReduce,HDFSWhatisHadoop?NameNode(Metadatastore)NodesHDFSClusterOperatingSystemNodesElasticStorage-SNCClusterKernelLevelIBMValueforHadoop!HDFS把數(shù)據(jù)分散散存儲(chǔ)在多多個(gè)存儲(chǔ)節(jié)節(jié)點(diǎn)Node上HDFS設(shè)計(jì)時(shí)就假假設(shè)存儲(chǔ)節(jié)節(jié)點(diǎn)有失效效的可能，，所以HDFS會(huì)把一份數(shù)數(shù)據(jù)復(fù)制3份以上，分分散存儲(chǔ)在在多個(gè)節(jié)點(diǎn)點(diǎn)上，從而而實(shí)現(xiàn)系統(tǒng)統(tǒng)整體上的的可靠性HDFS文件系統(tǒng)是是由服務(wù)器器節(jié)點(diǎn)集群群組成的，，每臺(tái)服務(wù)務(wù)器依照HDFS的特有block協(xié)議支持網(wǎng)網(wǎng)絡(luò)化block數(shù)據(jù)HDFSNameNode有發(fā)生單點(diǎn)點(diǎn)故障的危危險(xiǎn)IBM在改善文件件系統(tǒng)的性性能同時(shí)消消除了單點(diǎn)點(diǎn)故障——ElasticStorage-SNC(availableasbetacode)Hadoop說(shuō)明,MapReduce,HDFSHadoopStackWhatdoesitlooklike?典型Hadoop存儲(chǔ)的PainPoints在選擇HDFS的組件（如如軟件、服服務(wù)器、網(wǎng)網(wǎng)絡(luò)和存儲(chǔ)儲(chǔ)等）時(shí)很很難選對(duì)在從測(cè)試環(huán)環(huán)境遷移到到生產(chǎn)環(huán)境境時(shí)，需要要做的調(diào)優(yōu)優(yōu)和調(diào)整工工作太繁復(fù)復(fù)了長(zhǎng)期持續(xù)不不斷的運(yùn)維維保障過(guò)于于繁重，比比如老要更更換失效組組件（尤其其是硬盤(pán)）），這使得得保證期望望的SLA非常難CPU和存儲(chǔ)去耦耦本來(lái)用戶(hù)的的CPU和內(nèi)存已經(jīng)經(jīng)滿(mǎn)足計(jì)算算需求，但但為了存儲(chǔ)儲(chǔ)容量需要要安裝更多多的硬盤(pán)不不得不買(mǎi)更更多的、不不必要的CPU和內(nèi)存Storageoptionsavailablehavecleargaps本地存儲(chǔ)的的利用率低低(~25%)，每次需要要擴(kuò)容的時(shí)時(shí)候就要添添加更多的的服務(wù)器，，而一旦硬硬盤(pán)失效后后需要重建建，服務(wù)器器越多，失失效的幾率率越高，性性能也就越越差I(lǐng)BMStorageforHadoop傳統(tǒng)的Hadoop集群使用的的是服務(wù)器器內(nèi)置硬盤(pán)盤(pán)存儲(chǔ)。如如果用作測(cè)測(cè)試或科學(xué)學(xué)研究還好好，可作為為業(yè)務(wù)運(yùn)行行的存儲(chǔ)就就要采用企企業(yè)存儲(chǔ)Hadoop集群要負(fù)責(zé)責(zé)數(shù)據(jù)保護(hù)護(hù)和復(fù)制重建（就是是copy）失效的數(shù)數(shù)據(jù)集到不不同節(jié)點(diǎn)上上——嚴(yán)重影響CPU性能，無(wú)法法實(shí)現(xiàn)企業(yè)業(yè)級(jí)的RASReplicatedata–問(wèn)題同上擴(kuò)展的時(shí)候候同時(shí)增加加處理器/網(wǎng)絡(luò)/存儲(chǔ)，無(wú)法法做到物盡盡其用（nowaytoseparatethese3evenifexcesscapacityexistinginone(e.g.NeededmorestoragebuthadtoaddComputeandNetwork)）使用外部存儲(chǔ)儲(chǔ)可以將存儲(chǔ)儲(chǔ)負(fù)載和Hadoop計(jì)算節(jié)點(diǎn)分離離，同時(shí)還獲獲得了企業(yè)存存儲(chǔ)的好處。。SellthevalueofXIV,V7000,SVC,etc.用戶(hù)一般會(huì)隨隨HadoopFileSystem部署；采用ElasticStorage可以有很多好好處14數(shù)據(jù)加速ExperiencetheinstantresultsthatcomefromIBMFlashSystemDriveasmuchas45Xfasteranalyticsresultsoncertainworkloads數(shù)據(jù)負(fù)載的多多樣性和靈活活性XIVdeliverspredictableperformancethatscaleslinearlywithouthotspotsdeliveringinsightsfromanalyticsfasterwithtuning-freedatadistributionScale-out,parallelprocessingofElasticStoragesoftwareandintegrationwithFlashSystemdramaticallyacceleratesperformanceofAnalyticsclustersVirtualStorageCenterwithSVCautomaticallyoptimizesdatawarehouseperformanceandcostacrossFlashandDiskMainframeDataEnvironmentsIntegrationwithDB2&specialtyanalytics“engines”leveragingDS8870delivers4xreductioninbatchtimeswithnewHighPerformanceFlashEnclosuresHighspeedencryptiononeverydrivetypesecuresdata數(shù)據(jù)保護(hù)和保保留LTFSEEw/tapeprovidesreducedTCObyupto90%overdiskforlongtermretentionofdataatrestwithalargeopenformattaperepositoryReducetheamountofdatatobestoredbyupto25timeswithProtecTIERde-duplication12x更快IBMFlashSystemincreasedSPLUNK&SASapplicationefficiencytoperformbusinessanalytics20x改善inactionablesupplychainanalytics,4xreductioninbatchtimes,virtualizationforplug&play6x時(shí)間節(jié)省“GPFSallowsustomovethemetadatafromthedisktotheFlashSystemonline.Oncewedidthat,thebackupswerereduceddowntoaboutanhour.”2hrsbecomes2minutes失效切換時(shí)間間大幅縮短MappingCharacteristicstoIBMStorageProductsStorageInfrastructure需求適用于所有的的5種應(yīng)用場(chǎng)景OptimizedMulti-TemperatureWarehouse優(yōu)化的多級(jí)存存儲(chǔ)庫(kù)AllFlashFlashSystemHybridDS8000EasyTierXIV+SSDCachingStorwizeEasyTierFlashSystemSolution(VSC+FlashSystem)PureSystemsPureFlex(XIVorStorwizew/EasyTier)PureDataforTransactions(Storwize)PureDataforAnalytics(Netezza)Midrange&EntryTier0AccelerationSmarterStorageIntegratedSystemsEnterpriseOfferingsXIVzEnterpriseSolutionsforAnalyticswithDS8000PureDataSystemforOperationalAnalyticswithStorwizePureFlexSystemwithStorwizeDS8000SmartAnalyticsSystemswithDS3xxxOpen&ExtensibleStorwizefamilyFlashSystemfamilyIBMSmarterStorage的設(shè)計(jì)就是支支持大數(shù)據(jù)分分析高效和優(yōu)化數(shù)數(shù)據(jù)基礎(chǔ)架構(gòu)構(gòu)IBMFlashSystem：為大數(shù)據(jù)分分析應(yīng)用設(shè)計(jì)計(jì)的，讓?xiě)?yīng)用用和數(shù)據(jù)實(shí)現(xiàn)現(xiàn)極速I(mǎi)BMFlashSystem的極速性能讓實(shí)時(shí)業(yè)務(wù)決決策成為可能能適合于模塊化化數(shù)據(jù)存儲(chǔ)結(jié)結(jié)構(gòu)的Hadoop系統(tǒng)。某些或或所有數(shù)據(jù)可可以保存到Flash閃存上，其他他可以保存到到XIVIBMXIV:OptimizeddataworkloaddiversityforBigData&AnalyticsIBMXIV的高性能無(wú)須人工干預(yù)預(yù)配置，且適適用于各種各各樣的存儲(chǔ)負(fù)負(fù)載IBMXIV的效率高的異乎尋常常，而且簡(jiǎn)單單性業(yè)內(nèi)最高高，內(nèi)置友好好界面IBMXIV的彈性是企業(yè)級(jí)的，，完全保證了了數(shù)據(jù)的可用用性和業(yè)務(wù)連連續(xù)性XIV:為Analytics而生無(wú)與倫比的性能可擴(kuò)展的網(wǎng)格格存儲(chǔ)架構(gòu)任意時(shí)間支持持任意讀寫(xiě)負(fù)負(fù)載板上的閃存Flash無(wú)與倫比的可靠性精致的數(shù)據(jù)分分布無(wú)雙的磁盤(pán)重重建時(shí)間企業(yè)級(jí)的可用用性無(wú)與倫比的簡(jiǎn)易性簡(jiǎn)單的規(guī)劃、、供給和靈活活性上線后零維護(hù)護(hù)零調(diào)優(yōu)“XIV最吸引我們的的地方就是其其超強(qiáng)的性能能…we正是由于XIV為我們的精細(xì)細(xì)復(fù)雜的分析析應(yīng)用提供了了一致的高性性能，使得得我們能夠?yàn)闉槲覀兊挠脩?hù)戶(hù)帶來(lái)更多的的價(jià)值?！盨AS和XIV網(wǎng)格架架構(gòu)——完美的的結(jié)合合大規(guī)模模并行行計(jì)算算保持持持續(xù)地地最佳佳性能能BalancedPerformance性能均均衡常年零零調(diào)整整UnprecedentedScalability史無(wú)前前例的的擴(kuò)展展性配合添添加SAS節(jié)點(diǎn)和和XIV模塊即即可IBMSVC:OptimizeddataworkloadflexibilityforBigData&AnalyticsIBMSVC通過(guò)如如下功功能在在IBM大數(shù)據(jù)據(jù)產(chǎn)品品線上上增加加了靈活性性：完整和和數(shù)據(jù)據(jù)虛擬擬化和和數(shù)據(jù)據(jù)移動(dòng)動(dòng)性高級(jí)集集群和和復(fù)制制多路鏡鏡像，，readpreferredoptionRealTimeCompression實(shí)時(shí)壓壓縮EasyTierHotExtentcachingStorwizeV7000/UIBMSVC設(shè)計(jì)原原則Real-TimeCompression實(shí)時(shí)壓壓縮是是設(shè)計(jì)計(jì)來(lái)做做：作用于于ActivePrimaryData專(zhuān)用的的壓縮縮平臺(tái)臺(tái)PlatformhandlesALLheavyliftingassociatedwithcompression不會(huì)影影響性性能Wemodifyacompressedfilein-placeefficiently不會(huì)改改變用用戶(hù)應(yīng)應(yīng)用Usersnoradminsneedtochangeanything處理流流程不不變壓縮是是在線線完成成，不不是事事后壓壓縮業(yè)界標(biāo)標(biāo)準(zhǔn)壓壓縮算算法所采用用的壓壓縮算算法已已經(jīng)使使用了了幾十十年StorwizeV7000/UIBMSVC24流處理理計(jì)算算&IBMFlashSystemsData:是擁有有還是是保存存?或是是是分析析和開(kāi)開(kāi)始行行動(dòng)!DatainDataat25InfoSphereStreams:大數(shù)據(jù)據(jù)流分分析為分析析動(dòng)態(tài)態(tài)數(shù)據(jù)據(jù)而建建多并發(fā)發(fā)輸入入數(shù)據(jù)據(jù)流大規(guī)模?？蓴U(kuò)擴(kuò)展Massivescalability分析和和處理理的數(shù)數(shù)據(jù)多多樣化化Structured,unstructured,video,audioAdvancedanalyticoperators自適應(yīng)應(yīng)實(shí)時(shí)時(shí)分析析WithDataWarehousesWithHadoopSystemsCurrentfactfinding當(dāng)前數(shù)數(shù)據(jù)查查詢(xún)分許流流動(dòng)中中的數(shù)數(shù)據(jù)——在數(shù)據(jù)據(jù)落盤(pán)盤(pán)前低延遲遲模式式,pushmodel數(shù)據(jù)驅(qū)驅(qū)動(dòng)——真正的的數(shù)據(jù)據(jù)分析析Historicalfactfinding歷史數(shù)數(shù)據(jù)查查詢(xún)查找和和分析析存儲(chǔ)儲(chǔ)在磁磁盤(pán)上上的數(shù)數(shù)據(jù)信信息批處理理模式式,pullmodel查詢(xún)驅(qū)驅(qū)動(dòng):submitsqueriestostaticdataTraditionalComputingStreamComputing流數(shù)據(jù)據(jù)計(jì)算算代表表著計(jì)計(jì)算模模式的的變遷遷Real-timeAnalyticsRealTimeAnalytics實(shí)時(shí)分分析想象一一下你你如何何用防防火栓栓喝水水來(lái)自多多個(gè)多多樣輸輸入源源的大大量數(shù)數(shù)據(jù)直接處處理和和過(guò)濾濾數(shù)據(jù)據(jù)，而而不必必存儲(chǔ)儲(chǔ)僅保存存有價(jià)價(jià)值的的數(shù)據(jù)據(jù)僅關(guān)聯(lián)聯(lián)對(duì)數(shù)數(shù)據(jù)最最感興興趣的的用戶(hù)戶(hù)隨著數(shù)數(shù)據(jù)信信息的的產(chǎn)生生采取取行動(dòng)動(dòng)AdaptiveAnalytics自適應(yīng)應(yīng)分析析DatainMotionandDataatRest的集成成1.DataIngest數(shù)據(jù)集集成，，數(shù)據(jù)挖挖掘，，機(jī)器學(xué)學(xué)習(xí)，，統(tǒng)計(jì)建建模實(shí)時(shí)和和歷史史數(shù)據(jù)據(jù)洞察察力的的可視視化3.AdaptiveAnalyticsModel數(shù)據(jù)收收取，，在線分分析準(zhǔn)準(zhǔn)備，，模式式校驗(yàn)驗(yàn)Data2.Bootstrap/EnrichControlflowInfoSphereBigInsights,Database&WarehouseInfoSphereStreamsAdaptiveReal-TimeAnalytics自適適應(yīng)應(yīng)實(shí)實(shí)時(shí)時(shí)分分析析來(lái)自自多多個(gè)個(gè)多多樣樣輸輸入入源源的的大大量量數(shù)數(shù)據(jù)據(jù)過(guò)去去、、現(xiàn)現(xiàn)在在和和未未來(lái)來(lái)全全方方位位綜綜合合性性視視圖圖實(shí)時(shí)時(shí)分分析析，，低低延延時(shí)時(shí)結(jié)結(jié)果果Fullcontextfordeepanalysis深度度分分析析的的完完整整的的上上下下文文跨datainmotionanddataatrest的常常用用數(shù)數(shù)據(jù)據(jù)分分析析自適適應(yīng)應(yīng)-隨機(jī)機(jī)而而變變當(dāng)發(fā)發(fā)現(xiàn)現(xiàn)非非預(yù)預(yù)期期行行為為時(shí)時(shí)，，自自適適應(yīng)應(yīng)當(dāng)識(shí)識(shí)別別出出新新數(shù)數(shù)據(jù)據(jù)意意義義時(shí)時(shí)深深度度分分析析之之開(kāi)始始沒(méi)沒(méi)有有意意識(shí)識(shí)到到的的數(shù)數(shù)據(jù)據(jù)意意義義，，隨隨后后才才可可能能意意識(shí)識(shí)到到自適適應(yīng)應(yīng)———在開(kāi)開(kāi)始始沒(méi)沒(méi)有有意意識(shí)識(shí)到到的的，，隨隨后后可可以以找找出出數(shù)數(shù)據(jù)據(jù)模模式式StockmarketImpactofweatheronsecuritiespricesAnalyzemarketdataatultra-lowlatenciesMomentumCalculatorFraudpreventionDetectingmulti-partyfraudRealtimefraudpreventione-ScienceSpaceweatherpredictionDetectionoftransienteventsSynchrotronatomicresearchGenomicResearchTransportationIntelligenttrafficmanagementAutomotiveTelematicsEnergy&UtilitiesTransactivecontrolPhasorMonitoringUnitDownholesensormonitoringNaturalSystemsWildfiremanagementWatermanagementOtherManufacturingTextAnalysisERPforCommoditiesReal-timemultimodalsurveillanceSituationalawarenessCybersecuritydetectionLawEnforcement,

Defense&CyberSecurityHealth&LifeSciencesICUmonitoringEpidemicearlywarningsystemRemotehealthcaremonitoringTelephonyCDRprocessingSocialanalysisChurnpredictionGeomapping如何使用用InfoSphereStreams?加快數(shù)據(jù)據(jù)流入分分析系統(tǒng)統(tǒng)的速度度向交易方方向加速速。。。。一個(gè)高效效和靈活活的基礎(chǔ)礎(chǔ)架構(gòu)顯顯然可以以加快流流速，并并平衡不不同數(shù)據(jù)據(jù)分析的的需求CoresSCMStorageNetworkCoresSCMStorageNetworkCoresSCMStorageNetworkCoresSCMStorageNetwork++預(yù)測(cè)分析

數(shù)據(jù)倉(cāng)庫(kù)文本分析HadoopWorkloads優(yōu)化敏感性分析加快流速價(jià)值時(shí)間“觸發(fā)事件”數(shù)據(jù)完備交易Insight預(yù)見(jiàn)獲取數(shù)據(jù)時(shí)間分析數(shù)據(jù)時(shí)間行動(dòng)時(shí)間大數(shù)據(jù)分分析的新新式基礎(chǔ)礎(chǔ)架構(gòu)解解決方案案IBMBigData&AnalyticsInfrastructureDataZoneApplicationZoneExperiencereal-timeanalyticalinsightswithupto50xbetterperformancethanenterprisedisksystemsusingIBMFlashCore??technologyPreserveandprotectinfrastructurecontinuitywhilescalingtoover2petabyteofeffectiveall-flashcapacityunderasingleintegrateinterfaceDeliveragilityanddataeconomicswith4xgreatercapacityinlessrackspacethancompetitiveall-flashproductsSynchronizedandComplimentarytoOverarchingStorageMessaging-Acceleratetimetoinsightsthrough"datawithoutborders."IBMinnovationfreesdatawithagileandsimpletousestoragesolutionsdeliveringsuperiordataeconomicsIBMFlashSystemCoreLaunchMessagingDriveacompleteparadigmshiftinEnterpriseStoragewiththeallnewIBMFlashSystemFamilyIBMFlashSystemFamily2015ThemeTimetoinsight.Timetovalue.Timetomarket.IBMFlashSystem,it’sabouttime.FlashRealized!IBMFlashSystemV9000FoundationalPillarsIBMFlashCore?TechnologyistheDNAoftheFlashSystemFamilyScalablePerformanceEnduringEconomicsAgileIntegrationIntroducingtheNewIBMFlashSystemFamilyOfferingsIBMFlashSystem900ExtremePerformance:Delivers100microsecondresponsetimesMacroEfficiency:Lowestlatencyofferingwith>40%greatercapacityatalowercostpercapacityEnterpriseReliability:IBMenhancedMicronMLCflashtechnologywithFlashWearGuaranteePoweredbyIBMFlashCore?TechnologyIBMFlashSystemV9000ScalablePerformance:Growcapacityandperformancewithupto2.2PBscalingcapabilityEnduringEconomics:NextgenerationflashmediawithlowercostpercapacityAgileIntegration:FullyintegratedsystemmanagementtosimplifymanagementandimproveworkforceproductivityunderasinglenamespaceFlashSystem900IntroducingIBMFlashSystem900,thenextgenerationinourlowes