大数据分析存储解决方案

1、IBM存储(cn ch)解决方案 数据分析的存储IBM STG wenhuax第一页，共四十四页。从企业数据(shj)向大数据(shj)的扩展Traditional ApproachStructured, analytical, logicalSystems of RecordNew ApproachCreative, holistic thought, intuitionSystems Of EngagementMultimediaSystems of Insight Enterprise Integrationand Context AccumulationStructuredRepeat

2、ableLinearUnstructuredExploratoryDynamicData WarehouseWeb LogsSocial DataText Data:emailsSensor data:imagesRFIDInternal App DataTransaction DataMainframe DataOLTP System DataHadoop andStreamsTraditional SourcesNew SourcesERP data具备洞悉能力的系统Systems of Insight第二页，共四十四页。对新式(xnsh)基础架构的需求在可靠和安全的环境中处理关键业务应用

3、存取和处理海量数据包括结构化和非结构化数据速度及时响应随时可能(knng)出现的商业机会，这就需要灵活、实时性的基础架构The dynamics of SoR and SoE:通过负载及资源部署的优化，来增强灵活性和效益通过采用包括基于开放标准的技术等新技术来改善IT economicsSystem of Record （SoR）Systems of Engagement（SoE）对的决策对的地方(dfng)对的时间点Big Data& Analytics第三页，共四十四页。大数据分析的新型(xnxng)架构解决方案IBM Big Data & Analytics Infrastructure

4、Data Zone Application Zone 第四页，共四十四页。5Smart MeteringGrid Operations电网(dinwng)管理Field Service外勤现场(xinchng)服务Resource Planning资源(zyun)规划Customer Service / Customer Operations实现真正的有效的法规遵从及时发现能源损耗问题、以及偷电和欺诈行为提高客户满意度电量使用预测更为精确电网运维优化减少停电次数和时间案例: Smart Metering智慧电力计费 大数据分析应用可以带来真正的业务价值法规遵从第五页，共四十四页。案例(n l)

5、: 用大数据分析来加强 Smart Metering数据分析的高可用性，以确保随时(sush)了解用户喜好跨应用的TB级的数据需求 通用虚拟化存储(cn ch)平台实时收集、存储并分析数据，最快可达 50,000 data points/sec历史用电状态数据的复杂查询处理数据在加载到数据仓库前的清洗、验证，这些数据可能来自很多的用户、收费系统或断电保护系统关系掌控构建和维护电网的唯一试图对整个企业的结构化和非结构化数据t做全局导览Navigation，从中发现Discover价值分析用户用电情况，侦测偷电、改表等行为预测哪些用户适合于哪些分时时段电价或需求/响应服务分时时段电价的实时定价 或

6、 提供及时的需求/响应服务第六页，共四十四页。IBM Big Data & Analytics Reference ArchitectureBig Data Platform CapabilitiesInformation IngestReal-time AnalyticsWarehouse & Data MartsAnalytic AppliancesAll Data SourcesAdvanced Analytics/New InsightsNew/Enhanced ApplicationsCognitive认知Learn Dynamically?Prescriptive 规范Best O

7、utcomes?Predictive预测What Could Happen?Descriptive描述What Has Happened?Exploration and DiscoveryWhat Do You Have?Streaming DataText DataApplications DataTime SeriesGeo SpatialRelationalSocial NetworkVideo & ImageAutomated ProcessCase ManagementAnalytic ApplicationsWatsonCloud ServicesISV SolutionsAler

8、ts第七页，共四十四页。New Infrastructure Leverages Data TypesData inMotionData atRestData inMany FormsInformation Ingestion and Operational Information Decision ManagementBI and Predictive AnalyticsNavigation and DiscoveryIntelligenceAnalysis Raw Data Structured Data Text Analytics Data Mining Entity Analytic

9、s Machine LearningLanding Area, Analytics Zone and ArchiveVideo/AudioNetwork/SensorEntity AnalyticsPredictiveReal-time AnalyticsExploration,Integrated Warehouse, and Mart ZonesDiscoveryDeep ReflectionOperationalPredictive Stream Processing Data Integration Master Data StreamsInformation Governance,

10、Security and Business Continuity BigInsightsStreamsWarehouse第八页，共四十四页。第九页，共四十四页。InfoSphere BigInsights Hadoop-based 低延迟(ynch)分析，针对多样化的、海量静态数据Data-At-RestNetezza High Capacity Appliance基于(jy)结构化数据的可查询归档Netezza 1000基于(jy)结构化数据的BI+定制化分析 DataSmart Analytics System基于结构化数据的运营分析Informix TimeseriesTime-stru

11、ctured analyticsInfoSphere Warehouse基于结构化数据的大容量数据分析InfoSphere Streams低延迟流数据分析Velocity, Variety & VolumeData-In-MotionMPP Data WarehouseStream ComputingInformation IntegrationHadoopInfoSphere Information Server海量数据集成和转化Apache Hadoop:跨服务器集群的大数据集分布式处理开放系统框架，采用的是一种简单化编程模型IBM Big Data Platform大数据平台第十页，共四

12、十四页。What: 一种开源(ki yun)软件，将数据计算分布到整个集群的常见商用服务器和存储上Why: 传统的计算架构是一种沿纵向扩展模式，通过更快的SAN、大容量内存和多级缓存将数据加载到CPU上，成本比较高。What: Hadoop 把大数据集合拆分区划为小数据集合，再把小数据集合分发到多台普通服务器上，是一种横向扩展模式。Why: Scalable, Flexible, Cost Effective, Fault TolerentComponents: Map Reduce, HDFSWhat is Hadoop?第十一页，共四十四页。9、 人的价值，在招收诱惑的一瞬间被决定。202

13、2/9/12022/9/1Thursday, September 1, 202210、低头要有勇气，抬头要有低气。2022/9/12022/9/12022/9/19/1/2022 10:27:23 PM11、人总是珍惜为得到。2022/9/12022/9/12022/9/1Sep-2201-Sep-2212、人乱于心，不宽余请。2022/9/12022/9/12022/9/1Thursday, September 1, 202213、生气是拿别人做错的事来惩罚(chngf)自己。2022/9/12022/9/12022/9/12022/9/19/1/202214、抱最大的希望，作最大的努力。0

14、1 九月 20222022/9/12022/9/12022/9/115、一个人炫耀什么，说明他内心缺少什么。九月 222022/9/12022/9/12022/9/19/1/202216、业余生活要有意义，不要越轨。2022/9/12022/9/101 September 202217、一个人即使已登上顶峰，也仍要自强不息。2022/9/12022/9/12022/9/12022/9/1第十二页，共四十四页。NameNode (Metadata store)NodesHDFS ClusterOperating SystemNodesElastic Storage -SNC ClusterKer

15、nel LevelIBM Value for Hadoop!HDFS 把数据分散存储在多个存储节点Node上HDFS 设计时就假设存储节点有失效的可能，所以HDFS会把一份数据复制3份以上，分散存储在多个节点上，从而实现(shxin)系统整体上的可靠性HDFS文件系统是由服务器节点集群组成的，每台服务器依照HDFS的特有block协议支持网络化block 数据HDFS Name Node 有发生单点故障的危险IBM 在改善文件系统的性能同时消除了单点故障 Elastic Storage -SNC (available as beta code)Hadoop 说明(shumng), Map Re

16、duce, HDFS第十三页，共四十四页。 Hadoop StackWhat does it look like?第十四页，共四十四页。典型(dinxng)Hadoop存储的Pain Points在选择HDFS的组件（如软件、服务器、网络和存储(cn ch)等）时很难选对在从测试环境迁移到生产环境时，需要做的调优和调整工作太繁复了长期持续不断的运维保障过于繁重，比如老要更换失效组件（尤其是硬盘），这使得保证期望的SLA非常难CPU 和存储去耦本来用户的CPU和内存已经(y jing)满足计算需求，但为了存储容量需要安装更多的硬盘不得不买更多的、不必要的CPU和内存Storage options

17、 available have clear gaps本地存储的利用率低 (25%)，每次需要扩容的时候就要添加更多的服务器，而一旦硬盘失效后需要重建，服务器越多，失效的几率越高，性能也就越差第十五页，共四十四页。IBM Storage for Hadoop传统的 Hadoop 集群使用的是服务器内置硬盘存储。如果用作测试或科学研究还好，可作为(zuwi)业务运行的存储就要采用企业存储Hadoop 集群要负责数据保护和复制重建（就是copy）失效的数据集到不同节点上 严重影响CPU性能，无法实现企业级的RASReplicate data 问题同上扩展的时候同时增加处理器/网络/存储，无法做到物尽

18、其用（ no way to separate these 3 even if excess capacity existing in one (e.g. Needed more storage but had to add Compute and Network)）使用外部存储可以将存储负载和Hadoop计算节点分离，同时还获得了企业存储的好处。Sell the value of XIV, V7000, SVC, etc.用户一般会随Hadoop File System部署；采用Elastic Storage 可以有很多好处16第十六页，共四十四页。数据加速(ji s)Experience t

19、he instant results that come from IBM FlashSystemDrive as much as 45X faster analytics results on certain workloads数据负载的多样性和灵活性XIV delivers predictable performance that scales linearly without hotspots delivering insights from analytics faster with tuning-free data distributionScale-out, parallel pr

20、ocessing of Elastic Storage software and integration with FlashSystem dramatically accelerates performance of Analytics clusters Virtual Storage Center with SVC automatically optimizes data warehouse performance and cost across Flash and DiskMainframe Data EnvironmentsIntegration with DB2 & specialt

21、y analytics “engines” leveraging DS8870 delivers 4x reduction in batch times with new High Performance Flash EnclosuresHigh speed encryption on every drive type secures data数据保护和保留 LTFS EE w/ tape provides reduced TCO by up to 90% over disk for long term retention of data at rest with a large open f

22、ormat tape repositoryReduce the amount of data to be stored by up to 25 times with ProtecTIER de-duplication 12x 更快IBM FlashSystem increased SPLUNK & SAS application efficiency to perform business analytics20 x 改善(gishn) in actionable supply chain analytics, 4x reduction in batch times, virtualizati

23、on for plug & play6x 时间(shjin)节省“GPFS allows us to move the metadata from the disk to the FlashSystem online. Once we did that, the backups were reduced down to about an hour.” 2 hrs becomes2 minutes失效切换时间大幅缩短Mapping Characteristics to IBM Storage Products 第十七页，共四十四页。Storage Infrastructure 需求(xqi)适用

24、于所有(suyu)的5种应用场景 Optimized Multi-TemperatureWarehouse优化的多级存储(cn ch)库 All FlashFlashSystemHybridDS8000 EasyTierXIV + SSD CachingStorwize EasyTierFlashSystem Solution (VSC + FlashSystem)PureSystemsPureFlex (XIV or Storwize w/EasyTier)PureData for Transactions (Storwize)PureData for Analytics (Netezza)

25、第十八页，共四十四页。Midrange & EntryTier 0 AccelerationSmarter StorageIntegrated SystemsEnterpriseOfferingsXIVzEnterprise Solutions for Analytics with DS8000PureData System forOperational Analytics with StorwizePureFlex Systemwith StorwizeDS8000Smart Analytics Systems with DS3xxxOpen & ExtensibleStorwizefami

26、lyFlashSystemfamilyIBM Smarter Storage 的设计就是支持(zhch)大数据分析高效和优化数据基础架构第十九页，共四十四页。IBM FlashSystem：为大数据分析应用设计(shj)的，让应用和数据实现极速IBM FlashSystem的 极速性能 让实时业务决策成为可能适合于模块化数据存储结构的Hadoop系统。某些或所有数据可以保存(bocn)到Flash闪存上，其他可以保存到XIV第二十页，共四十四页。IBM XIV: Optimized data workload diversity for Big Data & AnalyticsIBM XIV

27、的高性能无须人工干预配置，且适用于各种各样的存储负载IBM XIV 的效率 高的异乎寻常，而且简单性业内(y ni)最高，内置友好界面IBM XIV 的弹性是企业级的，完全保证了数据的可用性和业务连续性第二十一页，共四十四页。XIV: 为 Analytics 而生 无与伦比(w y ln b)的性能可扩展的网格(wn )存储架构任意时间支持任意读写负载板上的闪存Flash 无与伦比(w y ln b)的可靠性精致的数据分布无双的磁盘重建时间企业级的可用性 无与伦比的简易性简单的规划、供给和灵活性上线后零维护零调优“XIV最吸引我们的地方就是其超强的性能 we正是由于XIV为我们的精细复杂的分析

28、应用提供了一致的高性能， 使得我们能够为我们的用户带来更多的价值。”第二十二页，共四十四页。SAS 和 XIV 网格架构(ji u) 完美的结合大规模并行计算 保持持续地最佳性能(xngnng)Balanced Performance性能均衡 常年零调整Unprecedented Scalability史无前例的扩展性 配合添加SAS节点和XIV模块即可第二十三页，共四十四页。IBM SVC: Optimized data workload flexibility for Big Data & AnalyticsIBM SVC 通过(tnggu)如下功能在IBM 大数据产品线上增加了灵活性：完

29、整和数据虚拟化和数据移动性高级集群和复制多路镜像，read preferred optionReal Time Compression实时压缩Easy Tier Hot Extent cachingStorwize V7000/UIBM SVC第二十四页，共四十四页。设计(shj)原则Real-Time Compression实时压缩是设计来做：作用(zuyng)于 Active Primary Data专用的压缩平台Platform handles ALL heavy lifting associated with compression不会影响性能We modify a compresse

30、d file in-place efficiently不会改变用户应用Users nor admins need to change anything处理流程不变压缩是在线完成，不是事后压缩业界标准压缩算法所采用的压缩算法已经使用了几十年Storwize V7000/UIBM SVC第二十五页，共四十四页。26流处理(chl)计算 & IBM Flash Systems第二十六页，共四十四页。Data: 是拥有还是保存? 或是是分析(fnx)和开始行动!Data inData at27第二十七页，共四十四页。InfoSphere Streams: 大数据流分析(fnx)为分析动态数据而建多并发

31、输入数据流大规模可扩展Massive scalability分析和处理的数据多样化Structured, unstructured, video, audioAdvanced analytic operators自适应(shyng)实时分析With Data WarehousesWith Hadoop Systems第二十八页，共四十四页。Current fact finding当前数据查询(chxn)分许流动中的数据在数据落盘前低延迟模式, push model数据驱动真正的数据分析Historical fact finding历史数据查询查找和分析存储在磁盘上的数据(shj)信息批处理模式

32、, pull model查询驱动: submits queries to static data Traditional ComputingStream Computing流数据计算(j sun)代表着计算(j sun)模式的变迁Real-time Analytics第二十九页，共四十四页。Real Time Analytics实时分析想象一下(yxi)你如何用防火栓喝水来自多个(du )多样输入源的大量数据直接处理和过滤数据，而不必存储仅保存有价值的数据仅关联对数据最感兴趣的用户随着数据信息的产生采取行动第三十页，共四十四页。Adaptive Analytics自适应(shyng)分析Dat

33、a in Motion and Data at Rest的集成1. Data Ingest数据集成，数据挖掘，机器学习(xux)， 统计建模实时(sh sh)和历史数据洞察力的可视化3. Adaptive Analytics Model数据收取，在线分析准备，模式校验Data2. Bootstrap/EnrichControl flowInfoSphereBigInsights, Database & WarehouseInfoSphereStreams第三十一页，共四十四页。 Adaptive Real-Time Analytics自适应(shyng)实时分析来自多个多样输入源的大量数据过去

34、、现在和未来全方位综合性视图实时分析，低延时结果Full context for deep analysis深度分析的完整的上下文跨data in motion and data at rest的常用数据分析自适应-随机而变当发现非预期行为时，自适应当识别出新数据意义时深度分析之开始没有意识(y sh)到的数据意义，随后才可能意识(y sh)到自适应在开始没有意识到的，随后可以找出数据模式第三十二页，共四十四页。Stock marketImpact of weather on securities pricesAnalyze market data at ultra-low latencies

35、Momentum CalculatorFraud preventionDetecting multi-party fraudReal time fraud preventione-ScienceSpace weather predictionDetection of transient eventsSynchrotron atomic researchGenomic ResearchTransportationIntelligent traffic managementAutomotive TelematicsEnergy & UtilitiesTransactive controlPhaso

36、r Monitoring UnitDown hole sensor monitoringNatural SystemsWildfire managementWater managementOtherManufacturingText AnalysisERP for CommoditiesReal-time multimodal surveillanceSituational awarenessCyber security detectionLaw Enforcement, Defense & Cyber SecurityHealth & Life SciencesICU monitoringE

37、pidemic early warning systemRemote healthcare monitoringTelephonyCDR processingSocial analysisChurn predictionGeomapping如何(rh)使用InfoSphere Streams?第三十三页，共四十四页。加快数据流入分析(fnx)系统的速度向交易方向(fngxing)加速。一个高效(o xio)和灵活的基础架构显然可以加快流速，并平衡不同数据分析的需求CoresSCMStorageNetworkCoresSCMStorageNetworkCoresSCMStorageNetwork

38、CoresSCMStorageNetwork+预测分析数据仓库文本分析Hadoop Workloads优化敏感性分析加快流速价值时间“触发事件”数据完备交易Insight预见获取数据时间分析数据时间行动时间第三十四页，共四十四页。大数据分析的新式(xnsh)基础架构解决方案IBM Big Data & Analytics InfrastructureData Zone Application Zone 并行处理可扩展性低延时资源数据 优化第三十五页，共四十四页。Experience real-time analytical insights with up to 50 x better per

39、formance than enterprise disk systems using IBM FlashCore technologyPreserve and protect infrastructure continuity while scaling to over 2 petabyte of effective all-flash capacity under a single integrate interfaceDeliver agility and data economics with 4x greater capacity in less rack space than co

40、mpetitive all-flash productsSynchronized and Complimentary to Overarching Storage Messaging - Accelerate time to insights through data without borders. IBM innovation frees data with agile and simple to use storage solutions delivering superior data economics IBM FlashSystem Core Launch MessagingDri

41、ve a complete paradigm shift in Enterprise Storage with the all new IBM FlashSystem Family第三十六页，共四十四页。IBM FlashSystem Family2022 ThemeTime to insight. Time to value. Time to market.IBM FlashSystem, its about time.Flash Realized!第三十七页，共四十四页。IBM FlashSystem V9000Foundational PillarsIBM FlashCore Techn

42、ology is the DNA of the FlashSystem FamilyScalable PerformanceEnduring EconomicsAgile Integration第三十八页，共四十四页。Introducing the New IBM FlashSystem Family OfferingsIBM FlashSystem 900Extreme Performance: Delivers 100 microsecond response times Macro Efficiency: Lowest latency offering with 40% greater

43、capacity at a lower cost per capacityEnterprise Reliability: IBM enhanced Micron MLC flash technology with Flash Wear GuaranteePowered by IBM FlashCore TechnologyIBM FlashSystem V9000Scalable Performance: Grow capacity and performance with up to 2.2PB scaling capabilityEnduring Economics: Next gener

44、ation flash media with lower cost per capacity Agile Integration: Fully integrated system management to simplify management and improve workforce productivity under a single name space第三十九页，共四十四页。FlashSystem 900Introducing IBM FlashSystem 900, the next generation in our lowest latency offering IBM M

45、icroLatency with up to 1.1 million IOPS40% greater capacity at a 10% lower cost per capacityIBM FlashCore technology, our secret sauce Technical collaboration with Micron Technology, our flash chip supplierIBM enhanced flash technologyMLC NAND flash offering with Flash Wear Guarantee VAAI UNMAP and

46、VASA support with IBMSIS for improved cloud storage performance and efficiencyMinimum latencyWrite90 sRead155 s Maximum IOPS 4 KBRead (100%, random)1,100,00Read/write (70%/30%, random)800,000Write (100%, random)600,000Maximum bandwidth 256 KBRead (100%, sequential)10 GB/s Write (100%, sequential)4.5 GB/s Performance at-a-glanceIBM MicroLatency module type1.2 TB2.9 TB5.7 TBModules quanti