大數(shù)據(jù)分析存儲(chǔ)解決方案 課件

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

Creative,holisticthought,intuitionSystemsOfEngagementMultimediaSystemsofInsight

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andContextAccumulationStructured

Repeatable

LinearUnstructured

Exploratory

DynamicDataWarehouseWebLogsSocialDataTextData:

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imagesRFIDInternalAppDataTransactionDataMainframeDataOLTPSystemDataHadoopand

StreamsTraditionalSourcesNewSourcesERP

data具備洞悉能力的系統(tǒng)SystemsofInsight從企業(yè)數(shù)據(jù)向大數(shù)據(jù)的擴(kuò)展TraditionalApproa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Sy大數(shù)據(jù)分析的新型架構(gòu)解決方案IBMBigData&AnalyticsInfrastructureDataZoneApplicationZone大數(shù)據(jù)分析的新型架構(gòu)解決方案IBMBigData&A4SmartMeteringGridOperations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滿意度電量使用預(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4SmartMeteringGridOperations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、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)值分析用戶用電情況，偵測(cè)偷電、改表等行為預(yù)測(cè)哪些用戶適合于哪些分時(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)案例:用大數(shù)據(jù)分析來(lái)加強(qiáng)SmartMetering數(shù)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&ImageAutomatedProcessCaseManagementAnalyticApplicationsWatsonCloudServicesISVSolutionsAlertsIBMBigData&AnalyticsReferNewInfrastructureLeveragesDataTypesDatain

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

StreamProcessingDataIntegrationMasterDataStreamsInformationGovernance,SecurityandBusinessContinuityBigInsightsStreamsWarehouseNewInfrastructureLeveragesD大數(shù)據(jù)分析存儲(chǔ)解決方案課件InfoSphereBigInsightsHadoop-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)InfoSphereBigInsightsNetezzaWhat: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?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NameNode(Metadatastore)NodesHDFSClusterOperatingSystemNodesElasticStorage-SNCClusterKernelLevelIBMValueforHadoop!HDFS把數(shù)據(jù)分散存儲(chǔ)在多個(gè)存儲(chǔ)節(jié)點(diǎn)Node上HDFS設(shè)計(jì)時(shí)就假設(shè)存儲(chǔ)節(jié)點(diǎn)有失效的可能，所以HDFS會(huì)把一份數(shù)據(jù)復(fù)制3份以上，分散存儲(chǔ)在多個(gè)節(jié)點(diǎn)上，從而實(shí)現(xiàn)系統(tǒng)整體上的可靠性HDFS文件系統(tǒng)是由服務(wù)器節(jié)點(diǎn)集群組成的，每臺(tái)服務(wù)器依照HDFS的特有block協(xié)議支持網(wǎng)絡(luò)化block數(shù)據(jù)HDFSNameNode有發(fā)生單點(diǎn)故障的危險(xiǎn)IBM在改善文件系統(tǒng)的性能同時(shí)消除了單點(diǎn)故障

——ElasticStorage-SNC(availableasbetacode)Hadoop說(shuō)明,MapReduce,HDFSNameNode(Metadatastore)NodesHadoopStackWhatdoesitlooklike?HadoopStackWhatdoesitlook典型Hadoop存儲(chǔ)的PainPoints在選擇HDFS的組件（如軟件、服務(wù)器、網(wǎng)絡(luò)和存儲(chǔ)等）時(shí)很難選對(duì)在從測(cè)試環(huán)境遷移到生產(chǎn)環(huán)境時(shí)，需要做的調(diào)優(yōu)和調(diào)整工作太繁復(fù)了長(zhǎng)期持續(xù)不斷的運(yùn)維保障過(guò)于繁重，比如老要更換失效組件（尤其是硬盤(pán)），這使得保證期望的SLA非常難CPU和存儲(chǔ)去耦本來(lái)用戶的CPU和內(nèi)存已經(jīng)滿足計(jì)算需求，但為了存儲(chǔ)容量需要安裝更多的硬盤(pán)不得不買(mǎi)更多的、不必要的CPU和內(nèi)存Storageoptionsavailablehavecleargaps本地存儲(chǔ)的利用率低(~25%)，每次需要擴(kuò)容的時(shí)候就要添加更多的服務(wù)器，而一旦硬盤(pán)失效后需要重建，服務(wù)器越多，失效的幾率越高，性能也就越差典型Hadoop存儲(chǔ)的PainPoints在選擇HDFS的IBMStorageforHadoop傳統(tǒng)的Hadoop集群使用的是服務(wù)器內(nèi)置硬盤(pán)存儲(chǔ)。如果用作測(cè)試或科學(xué)研究還好，可作為業(yè)務(wù)運(yùn)行的存儲(chǔ)就要采用企業(yè)存儲(chǔ)Hadoop集群要負(fù)責(zé)數(shù)據(jù)保護(hù)和復(fù)制重建（就是copy）失效的數(shù)據(jù)集到不同節(jié)點(diǎn)上——嚴(yán)重影響CPU性能，無(wú)法實(shí)現(xiàn)企業(yè)級(jí)的RASReplicatedata–問(wèn)題同上擴(kuò)展的時(shí)候同時(shí)增加處理器/網(wǎng)絡(luò)/存儲(chǔ)，無(wú)法做到物盡其用（nowaytoseparatethese3evenifexcesscapacityexistinginone(e.g.NeededmorestoragebuthadtoaddComputeandNetwork)）使用外部存儲(chǔ)可以將存儲(chǔ)負(fù)載和Hadoop計(jì)算節(jié)點(diǎn)分離，同時(shí)還獲得了企業(yè)存儲(chǔ)的好處。SellthevalueofXIV,V7000,SVC,etc.用戶一般會(huì)隨HadoopFileSystem部署；采用ElasticStorage可以有很多好處14IBMStorageforHadoop14數(shù)據(jù)加速ExperiencetheinstantresultsthatcomefromIBMFlashSystemDriveasmuchas45X

fasteranalyticsresultsoncertainworkloads數(shù)據(jù)負(fù)載的多樣性和靈活性XIVdeliverspredictableperformancethatscaleslinearlywithouthotspotsdeliveringinsightsfromanalyticsfasterwithtuning-freedatadistributionScale-out,parallelprocessingofElasticStoragesoftwareandintegrationwithFlashSystemdramaticallyacceleratesperformanceofAnalyticsclustersVirtualStorageCenterwithSVCautomaticallyoptimizesdatawarehouseperformanceandcostacrossFlashandDiskMainframeDataEnvironmentsIntegrationwithDB2&specialtyanalytics“engines”leveragingDS8870delivers4x

reductioninbatchtimeswithnewHighPerformanceFlashEnclosuresHighspeedencryptiononeverydrivetypesecuresdata數(shù)據(jù)保護(hù)和保留

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)間大幅縮短MappingCharacteristicstoIBMStorageProducts數(shù)據(jù)加速12x更快20x改善6x時(shí)間節(jié)省2hrsStorageInfrastructure需求適用于所有的5種應(yīng)用場(chǎng)景

OptimizedMulti-TemperatureWarehouse優(yōu)化的多級(jí)存儲(chǔ)庫(kù)

AllFlashFlashSystemHybridDS8000EasyTierXIV+SSDCachingStorwizeEasyTierFlashSystemSolution(VSC+FlashSystem)PureSystemsPureFlex(XIVorStorwizew/EasyTier)PureDataforTransactions(Storwize)PureDataforAnalytics(Netezza)StorageInfrastructure需求適用于所有Midrange&EntryTier0AccelerationSmarterStorageIntegratedSystemsEnterpriseOfferingsXIVzEnterpriseSolutionsforAnalyticswithDS8000PureDataSystemforOperationalAnalyticswithStorwizePureFlexSystemwithStorwizeDS8000SmartAnalyticsSystemswithDS3xxxOpen&ExtensibleStorwizefamilyFlashSystemfamilyIBMSmarterStorage的設(shè)計(jì)就是支持大數(shù)據(jù)分析

高效和優(yōu)化數(shù)據(jù)基礎(chǔ)架構(gòu)MidrangeSmarterStorageIntegrIBMFlashSystem：為大數(shù)據(jù)分析應(yīng)用設(shè)計(jì)的，讓?xiě)?yīng)用和數(shù)據(jù)實(shí)現(xiàn)極速I(mǎi)BMFlashSystem的

極速性能

讓實(shí)時(shí)業(yè)務(wù)決策成為可能適合于模塊化數(shù)據(jù)存儲(chǔ)結(jié)構(gòu)的Hadoop系統(tǒng)。某些或所有數(shù)據(jù)可以保存到Flash閃存上，其他可以保存到XIVIBMFlashSystem：為大數(shù)據(jù)分析應(yīng)用設(shè)計(jì)的，讓?xiě)?yīng)IBMXIV:OptimizeddataworkloaddiversityforBigData&AnalyticsIBMXIV的高性能無(wú)須人工干預(yù)配置，且適用于各種各樣的存儲(chǔ)負(fù)載IBMXIV的效率

高的異乎尋常，而且簡(jiǎn)單性業(yè)內(nèi)最高，內(nèi)置友好界面IBMXIV

的彈性是企業(yè)級(jí)的，完全保證了數(shù)據(jù)的可用性和業(yè)務(wù)連續(xù)性IBMXIV:OptimizeddataworkloXIV:為Analytics而生

無(wú)與倫比的性能可擴(kuò)展的網(wǎng)格存儲(chǔ)架構(gòu)任意時(shí)間支持任意讀寫(xiě)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)分布無(wú)雙的磁盤(pán)重建時(shí)間企業(yè)級(jí)的可用性

無(wú)與倫比的簡(jiǎn)易性簡(jiǎn)單的規(guī)劃、供給和靈活性上線后零維護(hù)零調(diào)優(yōu)“XIV最吸引我們的地方就是其超強(qiáng)的性能…we正是由于XIV為我們的精細(xì)復(fù)雜的分析應(yīng)用提供了一致的高性能，使得我們能夠?yàn)槲覀兊挠脩魩?lái)更多的價(jià)值?！盭IV:為Analytics而生無(wú)與倫比的性能可擴(kuò)展SAS

和XIV網(wǎng)格架構(gòu)——完美的結(jié)合大規(guī)模并行計(jì)算

保持持續(xù)地最佳性能BalancedPerformance性能均衡

常年零調(diào)整UnprecedentedScalability史無(wú)前例的擴(kuò)展性

配合添加SAS節(jié)點(diǎn)和XIV模塊即可SAS和XIV網(wǎng)格架構(gòu)——完美的結(jié)合大規(guī)模并行計(jì)算IBMSVC:OptimizeddataworkloadflexibilityforBigData&AnalyticsIBMSVC通過(guò)如下功能在IBM大數(shù)據(jù)產(chǎn)品線上增加了靈活性：完整和數(shù)據(jù)虛擬化和數(shù)據(jù)移動(dòng)性高級(jí)集群和復(fù)制多路鏡像，readpreferredoptionRealTimeCompression實(shí)時(shí)壓縮EasyTierHotExtentcachingStorwizeV7000/UIBMSVCIBMSVC:Optimizeddataworklo設(shè)計(jì)原則Real-TimeCompression實(shí)時(shí)壓縮是設(shè)計(jì)來(lái)做：作用于

ActivePrimaryData專(zhuān)用的壓縮平臺(tái)PlatformhandlesALLheavyliftingassociatedwithcompression不會(huì)影響性能Wemodifyacompressedfilein-placeefficiently不會(huì)改變用戶應(yīng)用Usersnoradminsneedtochangeanything處理流程不變壓縮是在線完成，不是事后壓縮業(yè)界標(biāo)準(zhǔn)壓縮算法所采用的壓縮算法已經(jīng)使用了幾十年StorwizeV7000/UIBMSVC設(shè)計(jì)原則Real-TimeCompression實(shí)時(shí)壓縮是24流處理計(jì)算&IBMFlashSystems24流處理計(jì)算&IBMFlashSystemsData:是擁有還是保存?或是是分析和開(kāi)始行動(dòng)!DatainDataat25Data:是擁有還是保存?或是是分析和開(kāi)始行動(dòng)!DaInfoSphereStreams:大數(shù)據(jù)流分析為分析動(dòng)態(tài)數(shù)據(jù)而建多并發(fā)輸入數(shù)據(jù)流大規(guī)?？蓴U(kuò)展Massivescalability分析和處理的數(shù)據(jù)多樣化Structured,unstructured,video,audioAdvancedanalyticoperators自適應(yīng)實(shí)時(shí)分析WithDataWarehousesWithHadoopSystemsInfoSphereStreams:大數(shù)據(jù)流分析為分析動(dòng)Currentfactfinding當(dāng)前數(shù)據(jù)查詢分許流動(dòng)中的數(shù)據(jù)——在數(shù)據(jù)落盤(pán)前低延遲模式,pushmodel數(shù)據(jù)驅(qū)動(dòng)——真正的數(shù)據(jù)分析Historicalfactfinding歷史數(shù)據(jù)查詢查找和分析存儲(chǔ)在磁盤(pán)上的數(shù)據(jù)信息批處理模式,pullmodel查詢驅(qū)動(dòng):submitsqueriestostaticdataTraditionalComputingStreamComputing流數(shù)據(jù)計(jì)算代表著計(jì)算模式的變遷Real-timeAnalyticsCurrentfactfinding當(dāng)前數(shù)據(jù)查詢HistRealTimeAnalytics實(shí)時(shí)分析

想象一下你如何用防火栓喝水來(lái)自多個(gè)多樣輸入源的大量數(shù)據(jù)直接處理和過(guò)濾數(shù)據(jù)，而不必存儲(chǔ)僅保存有價(jià)值的數(shù)據(jù)僅關(guān)聯(lián)對(duì)數(shù)據(jù)最感興趣的用戶隨著數(shù)據(jù)信息的產(chǎn)生采取行動(dòng)RealTimeAnalytics實(shí)時(shí)分析

想象一下你如AdaptiveAnalytics自適應(yīng)分析

DatainMotionandDataatRest的集成1.DataIngest數(shù)據(jù)集成，數(shù)據(jù)挖掘，機(jī)器學(xué)習(xí)，

統(tǒng)計(jì)建模實(shí)時(shí)和歷史數(shù)據(jù)洞察力的可視化3.AdaptiveAnalyticsModel數(shù)據(jù)收取，

在線分析準(zhǔn)備，模式校驗(yàn)Data2.Bootstrap/EnrichControlflowInfoSphereBigInsights,Database&WarehouseInfoSphereStreamsAdaptiveAnalytics自適應(yīng)分析

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AdaptiveReal-TimeAnalytics自適應(yīng)實(shí)時(shí)分析來(lái)自多個(gè)多樣輸入源的大量數(shù)據(jù)過(guò)去、現(xiàn)在和未來(lái)全方位綜合性視圖實(shí)時(shí)分析，低延時(shí)結(jié)果Fullcontextfordeepanalysis深度分析的完整的上下文跨datainmotionanddataatrest的常用數(shù)據(jù)分析自適應(yīng)-隨機(jī)而變當(dāng)發(fā)現(xiàn)非預(yù)期行為時(shí)，自適應(yīng)當(dāng)識(shí)別出新數(shù)據(jù)意義時(shí)深度分析之開(kāi)始沒(méi)有意識(shí)到的數(shù)據(jù)意義，隨后才可能意識(shí)到自適應(yīng)——在開(kāi)始沒(méi)有意識(shí)到的，隨后可以找出數(shù)據(jù)模式

AdaptiveReal-TimeAnalyticsStockmarketImpactofweatheronsecuritiespricesAnalyzemarketdataatultra-lowlatenciesMomentumCalculatorFraudpreventionDetectingmulti-partyfraudRealtimefraudpreventione-ScienceSpaceweatherpredictionDetectionoftransienteventsSynchrotronatomicresearchGenomicResearchTransportationIntelligenttrafficmanagementAutomotiveTelematicsEnergy&UtilitiesTransactivecontrolPhasorMonitoringUnitDownholesensormonitoringNaturalSystemsWildfiremanagementWatermanagementOtherManufacturingTextAnalysisERPforCommoditiesReal-timemultimodalsurveillanceSituationalawarenessCybersecuritydetectionLawEnforcement,

Defense&CyberSecurityHealth&LifeSciencesICUmonitoringEpidemicearlywarningsystemRemotehealthcaremonitoringTelephonyCDRprocessingSocialanalysisChurnpredictionGeomapping如何使用InfoSphereStreams?StockmarketFraudpreventione-加快數(shù)據(jù)流入分析系統(tǒng)的速度向交易方向加速。。。一個(gè)高效和靈活的基礎(chǔ)架構(gòu)顯然可以加快流速，并平衡不同數(shù)據(jù)分析的需求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)間加快數(shù)據(jù)流入分析系統(tǒng)的速度向交易方向加速。。。一個(gè)高效和靈活大數(shù)據(jù)分析的新式基礎(chǔ)架構(gòu)解決方案IBMBigData&AnalyticsInfrastructureDataZoneApplicationZone大數(shù)據(jù)分析的新式基礎(chǔ)架構(gòu)解決方案IBMBigData&Experiencereal-timeanalyticalinsightswithupto50xbetterperformancethanenterprisedisksystemsusingIBMFlashCore?technologyPreserveandprotectinfrastructurecontinuitywhilescalingtoover2petabyteofeffectiveall-flashcapacityunderasingleintegrateinterfaceDeliveragilityanddataeconomicswith4xgreatercapacityinlessrackspacethancompetitiveall-flashproductsSynchronizedandComplimentarytoOverarchingStorageMessaging-Acceleratetimetoinsightsthrough"datawithoutborders."IBMinnovationfreesdatawithagileandsimpletousestoragesolutionsdeliveringsuperiordataeconomicsIBMFlashSystemCoreLaunchMessagingDriveacompleteparadigmshiftinEnterpriseStoragewiththeallnewIBMFlashSystemFamilyExperiencereal-timeanalyticaIBMFlashSystemFamily

2015ThemeTimetoinsight.Timetovalue.Timetomarket.IBMFlashSystem,it’sabouttime.FlashRealized!IBMFlashSystemFamily

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FoundationalPillarsIBMFlashCore?TechnologyistheDNAoftheFlashSystemFamilyScalablePerformanceEnduringEconomicsAgileIntegrationIBMFlashSystemV9000

FoundatiIntroducingtheNewIBMFlashSystemFamilyOfferingsIBMFlashSystem900ExtremePerformance:Delivers100microsecondresponsetimesMacroEfficiency:Lowestlatencyofferingwith>40%greatercapacityatalowercostpercapacityEnterpriseReliability:IBMenhancedMicronMLCflashtechnologywithFlashWearGuaranteePoweredbyIBMFlashCore?TechnologyIBMFlashSystemV9000ScalablePerformance:Growcapacityandperformancewithupto2.2PBscalingcapabilityEnduringEconomics:NextgenerationflashmediawithlowercostpercapacityAgileIntegration:FullyintegratedsystemmanagementtosimplifymanagementandimproveworkforceproductivityunderasinglenamespaceIntroducingtheNewIBMFlashSFlashSystem900IntroducingIBMFlashSystem900,thenextgenerationinourlowestlatencyofferingIBMMicroLatency?withupto1.1millionIOPS40%greatercapacityata10%lowercostpercapacityIBMFlashCore?technology,oursecretsauceTechnicalcollaborationwithMicronTechnology,ourflashchipsupplierIBMenhancedflashtechnologyMLCNANDflashofferingwithFlashWearGuaranteeVAAIUNMAPandVASAsupportwithIBMSISforimprovedcloudstorageperformanceandefficiencyMinimumlatency

Write90μsRead155μsMaximumIOPS4KBRead(100%,random)1,100,00Read/write(70%/30%,random)800,000Write(100%,random)600,000Maximumbandwidth256KBRead(100%,sequential)10GB/sWrite(100%,sequential)4.5GB/sPerformanceat-a-glanceIBMMicroLatencymoduletype1.2TB2.9TB5.7TBModulesquantity4681012681012681012RAID5capacity(TB)9.61211.617.423.229.022.834.245.657.0RawCapacity(TB)7.110.714.217.821.426.335.143.952.752.770.387.9105.5FlashSystem900IntroducingIBMIBMintroducesafullyintegrated,fullymanaged,fullfunctionall-flashstoragesystemFlashSystemV9000Scalableall-flasharchitecturewithfullsetofadvanceddatafeaturesPerformsatupto2.5MIOPSwithIBMMicroLatency,scalableto19.2GB/sScalesto456TBusableandupto2.28PBeffectivecapacityinonly34UUpto57TBusableandupto285TBeffectivecapacityinonly6UNewlicensingstructuretosimplifyorderingandplanningforExternalDataVirtualization,FlashCopy,MetroMirror,andReal-timeCompressionScalablePerformanceAgileIntegrationEnduringEconomicsPoweredbyFlashCore?TechnologyIBMintroducesafullyintegra從企業(yè)數(shù)據(jù)向大數(shù)據(jù)的擴(kuò)展TraditionalApproachStructured,analytical,logicalSystemsofRecordNewApproach

Creative,holisticthought,intuitionSystemsOfEngagementMultimediaSystemsofInsight

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data具備洞悉能力的系統(tǒng)SystemsofInsight從企業(yè)數(shù)據(jù)向大數(shù)據(jù)的擴(kuò)展TraditionalApproa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Sy大數(shù)據(jù)分析的新型架構(gòu)解決方案IBMBigData&AnalyticsInfrastructureDataZoneApplicationZone大數(shù)據(jù)分析的新型架構(gòu)解決方案IBMBigData&A43SmartMeteringGridOperations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滿意度電量使用預(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4SmartMeteringGridOperations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、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)值分析用戶用電情況，偵測(cè)偷電、改表等行為預(yù)測(cè)哪些用戶適合于哪些分時(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)案例:用大數(shù)據(jù)分析來(lái)加強(qiáng)SmartMetering數(shù)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&ImageAutomatedProcessCaseManagementAnalyticApplicationsWatsonCloudServicesISVSolutionsAlertsIBMBigData&AnalyticsReferNewInfrastructureLeveragesDataTypesDatain

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ManyFormsInformationIngestionandOperationalInformationDecision

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

StreamProcessingDataIntegrationMasterDataStreamsInformationGovernance,SecurityandBusinessContinuityBigInsightsStreamsWarehouseNewInfrastructureLeveragesD大數(shù)據(jù)分析存儲(chǔ)解決方案課件InfoSphereBigInsightsHadoop-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)InfoSphereBigInsightsNetezzaWhat: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?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NameNode(Metadatastore)NodesHDFSClusterOperatingSystemNodesElasticStorage-SNCClusterKernelLevelIBMValueforHadoop!HDFS把數(shù)據(jù)分散存儲(chǔ)在多個(gè)存儲(chǔ)節(jié)點(diǎn)Node上HDFS設(shè)計(jì)時(shí)就假設(shè)存儲(chǔ)節(jié)點(diǎn)有失效的可能，所以HDFS會(huì)把一份數(shù)據(jù)復(fù)制3份以上，分散存儲(chǔ)在多個(gè)節(jié)點(diǎn)上，從而實(shí)現(xiàn)系統(tǒng)整體上的可靠性HDFS文件系統(tǒng)是由服務(wù)器節(jié)點(diǎn)集群組成的，每臺(tái)服務(wù)器依照HDFS的特有block協(xié)議支持網(wǎng)絡(luò)化block數(shù)據(jù)HDFSNameNode有發(fā)生單點(diǎn)故障的危險(xiǎn)IBM在改善文件系統(tǒng)的性能同時(shí)消除了單點(diǎn)故障

——ElasticStorage-SNC(availableasbetacode)Hadoop說(shuō)明,MapReduce,HDFSNameNode(Metadatastore)NodesHadoopStackWhatdoesitlooklike?HadoopStackWhatdoesitlook典型Hadoop存儲(chǔ)的PainPoints在選擇HDFS的組件（如軟件、服務(wù)器、網(wǎng)絡(luò)和存儲(chǔ)等）時(shí)很難選對(duì)在從測(cè)試環(huán)境遷移到生產(chǎn)環(huán)境時(shí)，需要做的調(diào)優(yōu)和調(diào)整工作太繁復(fù)了長(zhǎng)期持續(xù)不斷的運(yùn)維保障過(guò)于繁重，比如老要更換失效組件（尤其是硬盤(pán)），這使得保證期望的SLA非常難CPU和存儲(chǔ)去耦本來(lái)用戶的CPU和內(nèi)存已經(jīng)滿足計(jì)算需求，但為了存儲(chǔ)容量需要安裝更多的硬盤(pán)不得不買(mǎi)更多的、不必要的CPU和內(nèi)存Storageoptionsavailablehavecleargaps本地存儲(chǔ)的利用率低(~25%)，每次需要擴(kuò)容的時(shí)候就要添加更多的服務(wù)器，而一旦硬盤(pán)失效后需要重建，服務(wù)器越多，失效的幾率越高，性能也就越差典型Hadoop存儲(chǔ)的PainPoints在選擇HDFS的IBMStorageforHadoop傳統(tǒng)的Hadoop集群使用的是服務(wù)器內(nèi)置硬盤(pán)存儲(chǔ)。如果用作測(cè)試或科學(xué)研究還好，可作為業(yè)務(wù)運(yùn)行的存儲(chǔ)就要采用企業(yè)存儲(chǔ)Hadoop集群要負(fù)責(zé)數(shù)據(jù)保護(hù)和復(fù)制重建（就是copy）失效的數(shù)據(jù)集到不同節(jié)點(diǎn)上——嚴(yán)重影響CPU性能，無(wú)法實(shí)現(xiàn)企業(yè)級(jí)的RASReplicatedata–問(wèn)題同上擴(kuò)展的時(shí)候同時(shí)增加處理器/網(wǎng)絡(luò)/存儲(chǔ)，無(wú)法做到物盡其用（nowaytoseparatethese3evenifexcesscapacityexistinginone(e.g.NeededmorestoragebuthadtoaddComputeandNetwork)）使用外部存儲(chǔ)可以將存儲(chǔ)負(fù)載和Hadoop計(jì)算節(jié)點(diǎn)分離，同時(shí)還獲得了企業(yè)存儲(chǔ)的好處。SellthevalueofXIV,V7000,SVC,etc.用戶一般會(huì)隨HadoopFileSystem部署；采用ElasticStorage可以有很多好處53IBMStorageforHadoop14數(shù)據(jù)加速ExperiencetheinstantresultsthatcomefromIBMFlashSystemDriveasmuchas45X

fasteranalyticsresultsoncertainworkloads數(shù)據(jù)負(fù)載的多樣性和靈活性XIVdeliverspredictableperformancethatscaleslinearlywithouthotspotsdeliveringinsightsfromanalyticsfasterwithtuning-freedatadistributionScale-out,parallelprocessingofElasticStoragesoftwareandintegrationwithFlashSystemdramaticallyacceleratesperformanceofAnalyticsclustersVirtualStorageCenterwithSVCautomaticallyoptimizesdatawarehouseperformanceandcostacrossFlashandDiskMainframeDataEnvironmentsIntegrationwithDB2&specialtyanalytics“engines”leveragingDS8870delivers4x

reductioninbatchtimeswithnewHighPerformanceFlashEnclosuresHighspeedencryptiononeverydrivetypesecuresdata數(shù)據(jù)保護(hù)和保留

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)間大幅縮短MappingCharacteristicstoIBMStorageProducts數(shù)據(jù)加速12x更快20x改善6x時(shí)間節(jié)省2hrsStorageInfrastructure需求適用于所有的5種應(yīng)用場(chǎng)景

OptimizedMulti-TemperatureWarehouse優(yōu)化的多級(jí)存儲(chǔ)庫(kù)

AllFlashFlashSystemHybridDS8000EasyTierXIV+SSDCachingStorwizeEasyTierFlashSystemSolution(VSC+FlashSystem)PureSystemsPureFlex(XIVorStorwizew/EasyTier)PureDataforTransactions(Storwize)PureDataforAnalytics(Netezza)StorageInfrastructure需求適用于所有Midrange&EntryTier0AccelerationSmarterStorageIntegratedSystemsEnterpriseOfferingsXIVzEnterpriseSolutionsforAnalyticswithDS8000PureDataSystemforOperationalAnalyticswithStorwizePureFlexSystemwithStorwizeDS8000SmartAnalyticsSystemswithDS3xxxOpen&ExtensibleStorwizefamilyFlashSystemfamilyIBMSmarterStorage的設(shè)計(jì)就是支持大數(shù)據(jù)分析

高效和優(yōu)化數(shù)據(jù)基礎(chǔ)架構(gòu)MidrangeSmarterStorageIntegrIBMFlashSystem：為大數(shù)據(jù)分析應(yīng)用設(shè)計(jì)的，讓?xiě)?yīng)用和數(shù)據(jù)實(shí)現(xiàn)極速I(mǎi)BMFlashSystem的

極速性能

讓實(shí)時(shí)業(yè)務(wù)決策成為可能適合于模塊化數(shù)據(jù)存儲(chǔ)結(jié)構(gòu)的Hadoop系統(tǒng)。某些或所有數(shù)據(jù)可以保存到Flash閃存上，其他可以保存到XIVIBMFlashSystem：為大數(shù)據(jù)分析應(yīng)用設(shè)計(jì)的，讓?xiě)?yīng)IBMXIV:OptimizeddataworkloaddiversityforBigData&AnalyticsIBMXIV的高性能無(wú)須人工干預(yù)配置，且適用于各種各樣的存儲(chǔ)負(fù)載IBMXIV的效率

高的異乎尋常，而且簡(jiǎn)單性業(yè)內(nèi)最高，內(nèi)置友好界面IBMXIV

的彈性是企業(yè)級(jí)的，完全保證了數(shù)據(jù)的可用性和業(yè)務(wù)連續(xù)性IBMXIV:OptimizeddataworkloXIV:為Analytics而生

無(wú)與倫比的性能可擴(kuò)展的網(wǎng)格存儲(chǔ)架構(gòu)任意時(shí)間支持任意讀寫(xiě)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)分布無(wú)雙的磁盤(pán)重建時(shí)間企業(yè)級(jí)的可用性

無(wú)與倫比的簡(jiǎn)易性簡(jiǎn)單的規(guī)劃、供給和靈活性上線后零維護(hù)零調(diào)優(yōu)“XIV最吸引我們的地方就是其超強(qiáng)的性能…we正是由于XIV為我們的精細(xì)復(fù)雜的分析應(yīng)用提供了一致的高性能，使得我們能夠?yàn)槲覀兊挠脩魩?lái)更多的價(jià)值。”XIV:為Analytics而生無(wú)與倫比的性能可擴(kuò)展SAS

和XIV網(wǎng)格架構(gòu)——完美的結(jié)合大規(guī)模并行計(jì)算

保持持續(xù)地最佳性能BalancedPerformance性能均衡

常年零調(diào)整UnprecedentedScalability史無(wú)前例的擴(kuò)展性

配合添加SAS節(jié)點(diǎn)和XIV模塊即可SAS和XIV網(wǎng)格架構(gòu)——完美的結(jié)合大規(guī)模并行計(jì)算IBMSVC:OptimizeddataworkloadflexibilityforBigData&AnalyticsIBMSVC通過(guò)如下功能在IBM大數(shù)據(jù)產(chǎn)品線上增加了靈活性：完整和數(shù)據(jù)虛擬化和數(shù)據(jù)移動(dòng)性高級(jí)集群和復(fù)制多路鏡像，readpreferredoptionRealTimeCompression實(shí)時(shí)壓縮EasyTierHotExtentcachingStorwizeV7000/UIBMSVCIBMSVC:Optimizeddataworklo設(shè)計(jì)原則Real-TimeCompression實(shí)時(shí)壓縮是設(shè)計(jì)來(lái)做：作用于

ActivePrimaryData專(zhuān)用的壓縮平臺(tái)PlatformhandlesALLheavyliftingassociatedwithcompression不會(huì)影響性能Wemodifyacompressedfilein-placeefficiently不會(huì)改變用戶應(yīng)用Usersnoradminsneedtochangeanything處理流程不變壓縮是在線完成，不是事后壓縮業(yè)界標(biāo)準(zhǔn)壓縮算法所采用的壓縮算法已經(jīng)使用了幾十年StorwizeV7000/UIBMSVC設(shè)計(jì)原則Real-TimeCompression實(shí)時(shí)壓縮是63流處理計(jì)算&IBMFlashSystems24流處理計(jì)算&IBMFlashSystemsData:是擁有還是保存?或是是分析和開(kāi)始行動(dòng)!DatainDataat64Data:是擁有還是保存?或是是分析和開(kāi)始行動(dòng)!DaInfoSphereStreams:大數(shù)據(jù)流分析為分析動(dòng)態(tài)數(shù)據(jù)而建多并發(fā)輸入數(shù)據(jù)流大規(guī)?？蓴U(kuò)展Massivescalability分析和處理的數(shù)據(jù)多樣化Structured,unstructured,video,audioAdvancedanalyticoperators自適應(yīng)實(shí)時(shí)分析WithDataWarehousesWithHadoopSystemsInfoSphereStreams:大數(shù)據(jù)流分析為分析動(dòng)Currentfactfinding當(dāng)前數(shù)據(jù)查詢分許流動(dòng)中的數(shù)據(jù)——在數(shù)據(jù)落盤(pán)前低延遲模式,pushmodel數(shù)據(jù)驅(qū)動(dòng)——真正的數(shù)據(jù)分析Historicalfactfinding歷史數(shù)據(jù)查詢查找和分析存儲(chǔ)在磁盤(pán)上的數(shù)據(jù)信息批處理模式,pullmodel查詢驅(qū)動(dòng):submitsqueriestostaticdataTraditionalComputingStreamComputing流數(shù)據(jù)計(jì)算代表著計(jì)算模式的變遷Real-timeAnalyticsCurrentfactfinding當(dāng)前數(shù)據(jù)查詢HistRealTimeAnalytics實(shí)時(shí)分析

想象一下你如何用防火栓喝水來(lái)自多個(gè)多樣輸入源的大量數(shù)據(jù)直接處理和過(guò)濾數(shù)據(jù)，而不必存儲(chǔ)僅保存有價(jià)值的數(shù)據(jù)僅關(guān)聯(lián)對(duì)數(shù)據(jù)最感興趣的用戶隨著數(shù)據(jù)信息的產(chǎn)生采取行動(dòng)RealTimeAnalytics實(shí)時(shí)分析

想象一下你如AdaptiveAnalytics自適應(yīng)分析

DatainMotionandDataatRest的集成1.DataIngest數(shù)據(jù)集成，數(shù)據(jù)挖掘，機(jī)器學(xué)習(xí)，

統(tǒng)計(jì)建模實(shí)時(shí)和歷史數(shù)據(jù)洞察力的可視化3.AdaptiveAnalyticsModel數(shù)據(jù)收取，

在線分析準(zhǔn)備，模式校驗(yàn)Data2.Bootstrap/EnrichControlflowInfoSphereBigInsights,Database&WarehouseInfoSphereStreamsAdaptiveAnalytics自適應(yīng)分析

Datai

AdaptiveReal-TimeAnalytics自適應(yīng)實(shí)時(shí)分析來(lái)自多個(gè)多樣輸入源的大量數(shù)據(jù)過(guò)去、現(xiàn)在和未來(lái)全方位綜合性視圖實(shí)時(shí)分析，低延時(shí)結(jié)果Fullcontextfordeepanalysis深度分析的完整的上下文跨datainmotionanddataatrest的常用數(shù)據(jù)分析自適應(yīng)-隨機(jī)而變當(dāng)發(fā)現(xiàn)非預(yù)期行為時(shí)，自適應(yīng)當(dāng)識(shí)別出新數(shù)據(jù)意義時(shí)深度分析之開(kāi)始沒(méi)有意識(shí)到的數(shù)據(jù)意義，隨后才可能意識(shí)到自適應(yīng)——在開(kāi)始沒(méi)有意識(shí)到的，隨后可以找出數(shù)據(jù)模式

AdaptiveReal-TimeAnalyticsStockmarketImpactofweatheronsecuritiespricesAnalyzemarketdataatultra-lowlatenciesMomentumCalculatorFraudpreventionDetectingmulti-partyfraudRealtimefraudpreventione-ScienceSpaceweatherpredictionDetectionoftransienteventsSynchrotronatomicresearchGenomicResearchTransportationIntelligenttrafficmanagementAutomotiveTelematicsEnergy&UtilitiesTransactivecontrolPhasorMonitoringUnitDownholesensormonitoringNaturalSystemsWildfiremanagementWatermanagementOtherManufacturingTextAnalysisERPforCommoditiesReal-timemultimodalsurveillanceSituationalawarenessCybersecuritydetectionLawEnforcement,

Defense&CyberSecurityHealth&LifeSciencesICUmonitoringEpidemicearlywarningsystemRemotehealthcaremonitoringTelephonyCDRprocessingSocialanalysisChurnpredictionGeomapping如何使用InfoSphereStreams?StockmarketFraudpreventione-加快數(shù)據(jù)流入分析系統(tǒng)的速度向交易方向加速。。。一個(gè)高效和靈活的基礎(chǔ)架構(gòu)顯然可以加快流速，并平衡不同數(shù)據(jù)分析的需求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)