1. InternalIntroduction to SAP HANA

2. In-Memory ComputingTechnology that allows the processing ofmassive quantities of real time datain the main memory of the serverto provide immediate results fromanalyses and transactions 3. Increasing DataVolumesCalculation SpeedType and # ofData SourcesLack of business transparencySales & Operations Planning based onsubsets of highly aggregated information,being several days or weeks outdated.Reactive business modelMissed opportunities andcompetitive disadvantage due tolack of speed and agility Utilities: daily- or hour-basedbilling and consumptionanalysis/simulation.In-Memory ComputingTechnology Constrained Business OutcomeSub-optimal execution speedLack of responsiveness due to datalatency and deployment bottlenecks Inability to update demand plan withgreater than monthly frequencyCurrent ScenarioInformationLatency 4. TeraBytes of DataIn-Memory100 GB/s datathrougputReal TimeFreedom fromthe data sourceImprove Business Performance IT rapidly delivering flexible solutionsenabling business Speed up billing and reconciliation cyclesfor complex goods manufacturers Planning and simulation on the fly basedon actual non-aggregated dataCompetitive AdvantageE.g. Utilities Industry: Sales growth and market advantagefrom demand/cost driven pricing thatoptimizes multiple variables consumption data, hourly energyprice, weather forecast, etc.In-Memory ComputingLeapfrogging Current Technology ConstraintsFlexible Real Time Analytics Real-time customer profitability Effective marketing campaign spendbased on large-volume data analysisFuture State 5. In-Memory Computing The Time is NOWOrchestrating Technology InnovationsThe elements of In-Memory computing are not new. However, dramatically improved hardware economics and technologyinnovations in software has now made it possible for SAP to deliver on its vision of the Real-Time Enterprise with In-Memory businessHW Technology InnovationsMulti-Core Architecture (8 x 8core CPUper blade)Massive parallel scaling with manyblades64bit address space 2TB in currentservers100GB/s data throughputDramatic decline inprice/performanceRow and Column StoreCompressionPartitioningNo Aggregate TablesReal-Time Data CaptureInsert Only on DeltaapplicationsSAP SW Technology Innovations 6. SAP Strategy for In-MemoryTECHNOLOGY INNOVATION BUSINESSVALUEReal-Time Analytics, Process Innovation, Lower TCOHEART OF FUTURE APPLICATIONSPackaged Business Solutions for Industry and Line of BusinessCUSTOMER CO-INNOVATIONDesign with customersEXPAND PARTNER ECOSYSTEMPartner-built applications, Hardware partnersGUIDING PRINCIPLESINNOVATION WITHOUT DISRUPTIONNew Capabilities For Current Landscape 7. In-Memory Computing Product SAP HANASAP High Performance Analytic ApplianceWhat is SAP HANA?SAP HANA is a preconfigured out of the box Appliance In-Memory software bundled with hardware deliveredfrom the hardware partner (HP, IBM, CISCO, Fujitsu) In-Memory Computing Engine Tools for data modeling, data and life cyclemanagement, security, operations, etc. Real-time Data replication via Sybase ReplicationServer Support for multiple interfaces Content packages (Extractors and Data Models)introduced over time Capabilities Enabled Analyze information in real-time at unprecedented speedson large volumes of non-aggregated data. Create flexible analytic models based on real-time andhistoric business data Foundation for new category of applications (e.g., planning,simulation) to significantly outperform current applicationsin category Minimizes data duplicationSAP HANASAPBusinessSuite3rd PartySAP BWreplicateETLSAP HANAmodelingBI ClientsSQLMDXBICS3rd Party 8. Technical OverviewCalculation models Extreme Performance and Flexibility with Calculations on the flySQLScriptPlanModelCalculation ModelCalculation EngineSQL MDXLogical Execution PlanDistributed Execution EngineRow Store Column StoreotherCompile & OptimizePhysical Execution PlanParseIn-Memory Computing EngineCalculation Model A calc model can be generated on the fly basedon input script or SQL/MDX A calc model can also define a parameterizedcalculation schema for highly optimized reuse A calc model supports scripted operationsData Storage Row Store - Metadata Column Store 10-20x Data Compression 9. SAP 2007/Page 9SAP BusinessObjects Data Services PlatformIntegrate heterogeneousdata into BWAExtract From Any Data Source into HANASyndicate From HANA to Any ConsumerRich TransformsIntegrated Data QualityText Analytics 10. SAP HANA Road Map:In-Memory IntroductionTodays System Landscape ERP System running on traditional database BW running on traditional database Data extracted from ERP and loaded into BW BWA accelerates analytic models Analytic data consumed in BI or pulled to data martsStep 1 In-Memory in parallel(Q4 2010) Operational data in traditional database is replicated intomemory for operational reporting Analytic models from production EDW can be brought intomemory for agile modeling and reporting Third party data (POS, CDR etc) can be brought into memoryfor agile modeling and reporting 11. SAP HANA Road Map:Renovation of DW and Innovation of ApplicationsStep 2 Primary Data Store for BW(Planned for Q3 2011) In-Memory Computing used as primary persistence for BW BW manages the analytic metadata and the EDW dataprovisioning processes Detailed operational data replicated from applications is thebasis for all processes SAP HANA 1.5 will be able to provide the functionality ofBWAStep 3 New Applications(Planned for Q3 2011) New applications extend the core business suite withnew capabilities New applications delegate data intense operationsentirely to the in-memory computing Operational data from new applications is immediatelyaccessible for analytics real real time 12. SAP HANA Road Map:Transformation of application platformsStep 4 Real Time Data Feed(2012/2013)Applications write data simultaneously to traditional databasesas well as the in-memory computingStep 5 Platform Consolidation All applications (ERP and BW) run on data residing in-memory Analytics and operations work on data in real time In-memory computing executes all transactions,transformations, and complex data processing 13. Real Time Enterprise: Value PropositionAddressing Key Business Drivers1. Real-Time Decision Making Fast and easy creation of ad-hoc views on business Access to real time analysis2. Accelerate Business Performance Increase speed of transactional information flow in areassuch as planning, forecasting, pricing, offers3. Unlock New Insights Remove constraints for analyzing large data volumes -trends, data mining, predictive analytics etc. Structured and unstructured data4. Improve Business Productivity Business designed and owned analytical models Business self-service reduce reliance on IT Use data from anywhere5. Improve IT efficiency Manage growing data volume and complexity efficiently Lower landscape costsThere is a significant interest from business to get agileanalytic solutions.In a down economy, companies focus on cash protection.The decision on what needs to be done to makeprocurement more efficient is being made in theprocurement department.CEO of a multinational transportation companyFlexibility to analyse business missed by LoB.First performance, and the other is flexibility on abusiness analyst level, who need to do deep diving tobetter understand and conclude. The second would bethat also front-end tools are not providing flexibility.Executive of a global retail companyTraditional data warehouse processes are too complexand consume too much time for business departments. The companies [] were frustrated with usualproblems [] difficulty to build new information views.These companies were willing to move data [] intoanother proprietary file format []. Analyst 14. Real Time Enterprise: Value PropositionThe Value BlocksValue Elements In-Memory Enablers Run performance-critical applications in-memory Combine analytical and transactional applications No need for planning levels or aggregation levels Multi-dimensional simulation models updated in one step Internal and external data securely combined Batch data loads eliminated Eliminate BW database Empower business self-service analytics reduceshadow IT Consolidate data warehouses and data marts In-memory business applications (eliminate database fortransactional systems) New business models based on real-timeinformation and execution Improved business agility Dramatically improveplanning, forecasting, price optimization and otherprocesses New business opportunities faster, more accuratebusiness decisions based on complex, large datavolumes Sense and respond faster Apply analytics tointernal and external data in real-time to triggeractions (e.g., market analytics) Business-driven What-If Ask ad-hocquestions against the data set without IT Right information at the right time Lower infrastructure costs server, storage,database Lower labor costs backup/restore,reporting, performance tuning High performance real-time analytics Support for trending, simulation (what-if) Business-driven data models Support for structured and un-structured data Analysis based on non-aggregated data setsProcessTransformationReal-TimeBusiness InsightsTransactionalandInfrastructure 15. HANA Information Modeler 16. HANA Information ModelerCreating Connectivity to a new system 17. HANA Information ModelerCreating Attribute View 18. HANA Information ModelerDefining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types) 19. HANA Information ModelerData Preview 20. HANA Information ModelerCreating Hierarchies 21. HANA Information ModelerCreating Analytic View 22. HANA Information ModelerCreating Analytic View 23. THANK YOUHead Quarters:9301 Southwest Freeway, Suite 475,Houston TX 77074 USAP: +1-832-849-1120F: +1-832-849-1119E: letstalk@principleinfotech.comOffshore office:3rd Floor, RPAS Chambers,Begumpet, TS - 500016 IndiaP: +91-40-64101333F: +1-832-849-1119E: letstalk@principleinfotech.com