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Introduction to HANA in-memory from SAP

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ugur candan

This document provides an introduction to SAP HANA, an in-memory database that allows for real-time processing of massive quantities of data. It discusses SAP HANA's vision of enabling a real-time enterprise by removing constraints like data latency. The roadmap outlines steps to use HANA as the primary data store for SAP Business Warehouse and to develop new applications that leverage HANA. Key benefits mentioned include real-time decision making, accelerated business performance, unlocking new insights from large data volumes, and improved business productivity and IT efficiency.

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Core Team:xxxIntroduction to HANAManoj KethaNA SBO Competency Center
AgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
In-Memory Computing Technology that allows the processing of massive quantities of real time data in the main memory of the server to provide immediate results from analyses and transactions
Vision: In-Memory ComputingTechnology Constrained Business OutcomeCurrent ScenarioSub-optimal execution speedLack of responsiveness due to data latency and deployment bottlenecksInability to update demand plan with greater than monthly frequencyLack of business transparencySales & Operations Planning based on subsets of highly aggregated information, being several days or weeks outdated.Increasing Data VolumesInformation LatencyCalculation SpeedType and # of Data SourcesReactive business modelMissed opportunities and competitive disadvantage due to lack of speed and agility Utilities: daily- or hour-based billing and consumption analysis/simulation. Vision: In-Memory ComputingLeapfrogging Current Technology ConstraintsFuture StateFlexible Real Time AnalyticsReal-time customer profitability
Effective marketing campaign spend based on large-volume data analysisImprove Business PerformanceIT rapidly delivering flexible solutions enabling business
Speed up billing and reconciliation cycles for complex goods manufacturers
Planning and simulation on the fly based on actual non-aggregated dataTeraBytes of DataIn-Memory100 GB/s data througput RealTimeFreedom from the data sourceCompetitive AdvantageE.g. Utilities Industry:Sales growth and market advantage from demand/cost driven pricing that optimizes multiple variables – consumption data, hourly energy price, weather forecast, etc.In-Memory Computing – The Time is NOWOrchestrating Technology InnovationsThe elements of In-Memory computing are not new. However, dramatically improved hardware economics and technology innovations in software has now made it possible for SAP to deliver on its vision of the Real-Time Enterprise with In-Memory business applicationsHW Technology InnovationsSAP SW Technology InnovationsRow and Column StoreMulti-Core Architecture (8 x 8core CPU per blade)Massive parallel scaling with many bladesCompressionPartitioning64bit address space – 2TB in current servers100GB/s data throughputDramatic decline in price/performanceNo Aggregate TablesReal-Time Data CaptureInsert Only on Delta
SAP Strategy for In-MemoryTECHNOLOGY INNOVATION  BUSINESS VALUEReal-Time Analytics, Process Innovation, Lower TCOHEART OF FUTURE APPLICATIONSPackaged Business Solutions for Industry and Line of BusinessCUSTOMER CO-INNOVATIONDesign with customersGUIDING PRINCIPLESINNOVATION WITHOUT DISRUPTIONNew Capabilities For Current LandscapeEXPAND PARTNER ECOSYSTEMPartner-built applications, Hardware partners
AgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
In-Memory Computing Product “SAP HANA”SAP High Performance Analytic ApplianceWhat is SAP HANA?SAP HANA is a preconfigured out of the box ApplianceIn-Memory software bundled with hardware delivered from the hardware partner (HP, IBM, CISCO, Fujitsu)
In-Memory Computing Engine
Tools for data modeling, data and life cycle management, security, operations, etc.
Real-time Data replication via Sybase Replication Server
Support for multiple interfaces
Content packages (Extractors and Data Models) introduced over timeCapabilities EnabledAnalyze information in real-time at unprecedented speeds on large volumes of non-aggregated data.
Create flexible analytic models based on real-time and historic business data
Foundation for new category of applications (e.g., planning, simulation) to significantly outperform current applications in category
Minimizes data duplicationBI Clients3rd PartyIn-MemorySQLMDXBICSSAP HANASAP HANAmodelingSAPBusinessSuitereplicateETL3rd PartySAP BW
Technical OverviewCalculation models – Extreme Performance and Flexibility with Calculations on the flyCalculation ModelA calc model can be generated on the fly based on input script or SQL/MDX
A calc model can also define a parameterized calculation schema for highly optimized reuse
A calc model supports scripted operationsSQLMDXSQL ScriptPlan ModelotherIn-Memory Computing EngineCompile & OptimizeParseCalculation ModelCalculation EngineData StorageRow Store - Metadata
Column Store – 10-20x Data CompressionLogical Execution PlanDistributed Execution EnginePhysical Execution PlanColumn StoreRow Store
SAP BusinessObjects Data Services PlatformRich TransformsIntegrate heterogeneous data into BWAIntegrated Data QualityText AnalyticsExtract From Any Data Source into HANASyndicate From HANA to Any Consumer© SAP 2007/Page 11
SAP HANA Road Map:In-Memory Introduction Today‘s System LandscapeERP 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 into memory for agile modeling and reporting
Third party data (POS, CDR etc) can be brought into memory for agile modeling and reportingStep 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 data provisioning processes

More Related Content

Introduction to HANA in-memory from SAP

  • 1. Core Team:xxxIntroduction to HANAManoj KethaNA SBO Competency Center
  • 2. AgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
  • 3. In-Memory Computing Technology that allows the processing of massive quantities of real time data in the main memory of the server to provide immediate results from analyses and transactions
  • 4. Vision: In-Memory ComputingTechnology Constrained Business OutcomeCurrent ScenarioSub-optimal execution speedLack of responsiveness due to data latency and deployment bottlenecksInability to update demand plan with greater than monthly frequencyLack of business transparencySales & Operations Planning based on subsets of highly aggregated information, being several days or weeks outdated.Increasing Data VolumesInformation LatencyCalculation SpeedType and # of Data SourcesReactive business modelMissed opportunities and competitive disadvantage due to lack of speed and agility Utilities: daily- or hour-based billing and consumption analysis/simulation. Vision: In-Memory ComputingLeapfrogging Current Technology ConstraintsFuture StateFlexible Real Time AnalyticsReal-time customer profitability
  • 5. Effective marketing campaign spend based on large-volume data analysisImprove Business PerformanceIT rapidly delivering flexible solutions enabling business
  • 6. Speed up billing and reconciliation cycles for complex goods manufacturers
  • 7. Planning and simulation on the fly based on actual non-aggregated dataTeraBytes of DataIn-Memory100 GB/s data througput RealTimeFreedom from the data sourceCompetitive AdvantageE.g. Utilities Industry:Sales growth and market advantage from demand/cost driven pricing that optimizes multiple variables – consumption data, hourly energy price, weather forecast, etc.In-Memory Computing – The Time is NOWOrchestrating Technology InnovationsThe elements of In-Memory computing are not new. However, dramatically improved hardware economics and technology innovations in software has now made it possible for SAP to deliver on its vision of the Real-Time Enterprise with In-Memory business applicationsHW Technology InnovationsSAP SW Technology InnovationsRow and Column StoreMulti-Core Architecture (8 x 8core CPU per blade)Massive parallel scaling with many bladesCompressionPartitioning64bit address space – 2TB in current servers100GB/s data throughputDramatic decline in price/performanceNo Aggregate TablesReal-Time Data CaptureInsert Only on Delta
  • 8. SAP Strategy for In-MemoryTECHNOLOGY INNOVATION  BUSINESS VALUEReal-Time Analytics, Process Innovation, Lower TCOHEART OF FUTURE APPLICATIONSPackaged Business Solutions for Industry and Line of BusinessCUSTOMER CO-INNOVATIONDesign with customersGUIDING PRINCIPLESINNOVATION WITHOUT DISRUPTIONNew Capabilities For Current LandscapeEXPAND PARTNER ECOSYSTEMPartner-built applications, Hardware partners
  • 9. AgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
  • 10. In-Memory Computing Product “SAP HANA”SAP High Performance Analytic ApplianceWhat is SAP HANA?SAP HANA is a preconfigured out of the box ApplianceIn-Memory software bundled with hardware delivered from the hardware partner (HP, IBM, CISCO, Fujitsu)
  • 12. Tools for data modeling, data and life cycle management, security, operations, etc.
  • 13. Real-time Data replication via Sybase Replication Server
  • 14. Support for multiple interfaces
  • 15. Content packages (Extractors and Data Models) introduced over timeCapabilities EnabledAnalyze information in real-time at unprecedented speeds on large volumes of non-aggregated data.
  • 16. Create flexible analytic models based on real-time and historic business data
  • 17. Foundation for new category of applications (e.g., planning, simulation) to significantly outperform current applications in category
  • 18. Minimizes data duplicationBI Clients3rd PartyIn-MemorySQLMDXBICSSAP HANASAP HANAmodelingSAPBusinessSuitereplicateETL3rd PartySAP BW
  • 19. Technical OverviewCalculation models – Extreme Performance and Flexibility with Calculations on the flyCalculation ModelA calc model can be generated on the fly based on input script or SQL/MDX
  • 20. A calc model can also define a parameterized calculation schema for highly optimized reuse
  • 21. A calc model supports scripted operationsSQLMDXSQL ScriptPlan ModelotherIn-Memory Computing EngineCompile & OptimizeParseCalculation ModelCalculation EngineData StorageRow Store - Metadata
  • 22. Column Store – 10-20x Data CompressionLogical Execution PlanDistributed Execution EnginePhysical Execution PlanColumn StoreRow Store
  • 23. SAP BusinessObjects Data Services PlatformRich TransformsIntegrate heterogeneous data into BWAIntegrated Data QualityText AnalyticsExtract From Any Data Source into HANASyndicate From HANA to Any Consumer© SAP 2007/Page 11
  • 24. SAP HANA Road Map:In-Memory Introduction Today‘s System LandscapeERP System running on traditional database
  • 25. BW running on traditional database
  • 26. Data extracted from ERP and loaded into BW
  • 28. 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
  • 29. Analytic models from production EDW can be brought into memory for agile modeling and reporting
  • 30. Third party data (POS, CDR etc) can be brought into memory for agile modeling and reportingStep 2 – Primary Data Store for BW(Planned for Q3 2011)In-Memory Computing used as primary persistence for BW
  • 31. BW manages the analytic metadata and the EDW data provisioning processes
  • 32. Detailed operational data replicated from applications is the basis for all processes
  • 33. SAP HANA 1.5 will be able to provide the functionality of BWAStep 3 – New Applications (Planned for Q3 2011)New applications extend the core business suite with new capabilities
  • 34. New applications delegate data intense operations entirely to the in-memory computing
  • 35. Operational data from new applications is immediately accessible for analytics – real real timeSAP HANA Road Map: Renovation of DW and Innovation of Applications
  • 36. SAP HANA Road Map: Transformation of application platformsStep 4 – Real Time Data Feed(2012/2013)Applications write data simultaneously to traditional databases as well as the in-memory computingStep 5 – Platform ConsolidationAll applications (ERP and BW) run on data residing in-memory
  • 37. Analytics and operations work on data in real time
  • 38. In-memory computing executes all transactions, transformations, and complex data processingAgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
  • 39. Real Time Enterprise: Value PropositionAddressing Key Business DriversReal-Time Decision MakingFast and easy creation of ad-hoc views on businessAccess to real time analysisAccelerate Business Performance Increase speed of transactional information flow in areas such as planning, forecasting, pricing, offers…Unlock New Insights Remove constraints for analyzing large data volumes - trends, data mining, predictive analytics etc.Structured and unstructured dataImprove Business ProductivityBusiness designed and owned analytical modelsBusiness self-service  reduce reliance on ITUse data from anywhereImprove IT efficiencyManage growing data volume and complexity efficientlyLower landscape costs There is a significant interest from business to get agile analytic solutions.„In a down economy, companies focus on cash protection. The decision on what needs to be done to make procurement more efficient is being made in the procurement department“.CEO of a multinational transportation companyFlexibility to analyse business missed by LoB.„First performance, and the other is flexibility on a business analyst level, who need to do deep diving to better understand and conclude. The second would be that also front-end tools are not providing flexibility“.Executive of a global retail companyTraditional data warehouse processes are too complex and consume too much time for business departments.„ The companies […] were frustrated with usual problems […] difficulty to build new information views. These companies were willing to move data […] into another proprietary file format […]. “Analyst
  • 40. Real Time Enterprise: Value PropositionThe Value BlocksValue ElementsIn-Memory EnablersRun performance-critical applications in-memory
  • 41. Combine analytical and transactional applications
  • 42. No need for planning levels or aggregation levels
  • 44. Internal and external data securely combined
  • 45. Batch data loads eliminated
  • 46. New business models  based on real-time information and execution
  • 47. Improved business agility  Dramatically improve planning, forecasting, price optimization and other processes
  • 48. New business opportunities  faster, more accurate business decisions based on complex, large data volumes
  • 50. Support for trending, simulation (“what-if”)
  • 52. Support for structured and un-structured data
  • 53. Analysis based on non-aggregated data sets
  • 54. Sense and respond faster  Apply analytics to internal and external data in real-time to trigger actions (e.g., market analytics)
  • 55. Business-driven “What-If”  Ask ad-hoc questions against the data set without IT
  • 56. Right information at the right time
  • 58. Empower business self-service analytics – reduce shadow IT
  • 60. In-memory business applications (eliminate database for transactional systems)
  • 61. Lower infrastructure costs  server, storage, database
  • 62. Lower labor costs  backup/restore, reporting, performance tuningAgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
  • 64. HANA Information ModelerCreating Connectivity to a new system
  • 66. HANA Information ModelerDefining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types)
  • 71. AgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
  • 72. Connectivity from BO Enterprise ToolsCrystal Reports Enterprise - (ODBC, JDBC, Universe)IDT (Information Design Tool) - JDBCExplorer – Connection configuration in CMCAdvanced Analysis for Office (Q1 2011 release)Web Intelligence – UniverseXcelsius - Universe
  • 73. AgendaIntroduction to HANA: Vision and StrategySolution Overview & RoadmapBusiness ValueHANA Modeling StudioConnecting from BOEReal time Examples
  • 74. Learning ResourcesRKT Materialhttps://websmp208.sap-ag.de/rkt-hanaNavigation:Consulting  SAP High-Performance Analytic Appliance 1.0  Application Consultant sTechnology Consultants

Editor's Notes

  1. Business users of all levels are empowered to conduct immediate ad hoc data analyses and transaction processing using massive amounts of real time data for expanded business insight.It frees up IT resources and lowers the cost of operations.
  2. Defining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types)Right click  Data PreviewRight click  Activate: This action will activate the Attribute View with selected fields as key figures and associated measures.
  3. We can also view distinct values in each of these fields and perform a quick analysis (data disbursement in graphical format) Analyzing the data present in an attribute: (By selecting Dimensions, Measures and applying filters) Also, we can change the type of chart we want to use depending on the type of data.
  4. Creating Attribute Hierarchies: From the Attribute properties window  Click on Hierarchies Tab  Create New hierarchy  We can create two types here (Level Hierarchy and Parent Child hierarchy. Drag and Drop the attributes from the list available as shown:
  5. We can create Analytic views from either a table imported into HANA or from Attribute Views that were createdOrBy duplicating existing views and further edit for a different purpose
  6. The model of Attributes and Analytic View will appear as below after establishing the relationships:Activate the view by right clicking in the studioNow the Analytic View is ready to be accessed by the Explorer.