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SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
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SAP HANA Inside Track
SAP HANA Inside Track
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SAP HANA Inside Track
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SAP HANA Inside Track
SAP HANA Inside Track
SAP HANA Inside Track
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SAP HANA Inside Track

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This presentation shows the sessions of the SAP HANA Inside track, a community driven event (July 30 in Palo Alto) with many insights on the following topics: …

This presentation shows the sessions of the SAP HANA Inside track, a community driven event (July 30 in Palo Alto) with many insights on the following topics:
BW on SAP HANA,Hadoop, SAP BI and HANA, HANA in the Cloud, HANA and the SAP BI Landscape and Live HANA Demos

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  • 1. SAP HANA Inside TrackDavid Hull, SAP HullJuly 30, 2012 Public
  • 2. AgendaTime Session1:00pm-1:10pm Welcome SAP BW on HANA1:10pm-1:45pm Rohit Kamath, SAP Hadoop, SAP BI and HANA - Healthcare Industry Example Jeff Krone from Zettaset will discuss how automating the Hadoop process using1:45pm-2:30pm Zettaset Orchestrator can deliver new levels of operational efficiency to the healthcare industry, including faster patient on-boarding and tighter compliance with new Affordable Care Act mandates. HANA in the Cloud - Options and Alternatives2:30pm-3:15pm Yusuf Bashir, SAP HANA & the SAP BI Landscape3:30pm-4:15pm Hari Guleria, Independent Consultant Live HANA Demos: Advanced Text Search (with HTML5 UI); Smart Meter Analytics;4:15pm-5:00pm Business Objects Explorer running on 3TB HANA dataset Chris Hallenbeck, SAP© 2012 SAP AG. All rights reserved. 2
  • 3. Be Part of the Twitter Conversation #sitpal #HANA© 2012 SAP AG. All rights reserved. 3
  • 4. Thank you
  • 5. BW on SAP HANARohit KamathJuly 30, 2012
  • 6. DisclaimerThis presentation outlines our general product direction and should not be relied onin making a purchase decision. This presentation is not subject to your licenseagreement or any other agreement with SAP. SAP has no obligation to pursue anycourse of business outlined in this presentation or to develop or release anyfunctionality mentioned in this presentation. This presentation and SAPs strategyand possible future developments are subject to change and may be changed bySAP at any time for any reason without notice. This document is provided without awarranty of any kind, either express or implied, including but not limited to, theimplied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in thisdocument, except if such damages were caused by SAP intentionally or grosslynegligent.© 2012 SAP AG. All rights reserved. 8
  • 7. AgendaIntroduction  Breaking new grounds for in-memory technologyEnterprise Data Warehousing with SAP BW  DWH Application and DB Platform in symbiosis  Typical bottle necks caused by the RDBMS paradigm and the two tier approachSAP HANA In-memory Data Base  In-memory database technology for Dummies  SAP HANA 1.0 SPS3 – data base specific featuresSAP BW on SAP HANA database: value propositionModeling and Dataflow Aspects for an HANA based BW  BW‟s Layered Scalable Architecture (LSA) in times of HANA  HANA optimized modeling objects in BWSummary and Outlook  Six key points to take homeAppendix – Migration considerations© 2012 SAP AG. All rights reserved. 9
  • 8. Enterprise Data Warehousing with SAP DWH Application and DB Platform in symbiosis Typical bottle necks caused by the RDBMS paradigm and the two tier approach
  • 9. SAP NetWeaver Business WarehouseStrong EDW capabilities - OverviewIntegrated, scalable Enterprise Data Warehouse (EDW) platform EDW = DBMS + BW Fast, sustainable implementation through Business  Modeling Patterns Content  Business Content Openness and data quality through Reliable  Out-of-the box integration for data originating in SAP systems Data Acquisition  Integrated with SAP BusinessObjects Data Services (Data Integrator and Data Quality Management) Efficient data management through: Streamlined  Management of data consistency, data base abstraction, data base neutral Operations  Sophisticated Security, Authorization and Identity Handling  High availability Enable sophisticated lifecycle management at different levels: Lifecycle  System Management  Meta Data  Data (Nearline storage, archiving)© 2012 SAP AG. All rights reserved. 11
  • 10. SAP NetWeaver Business WarehouseEDW Model and Dataflow DefinitionDefine a central EDW model that satisfiesthe need of decision makers across all Reporting Layerareas of a company and acts as a singlepoint of truth for any kind of information Operational Data Business Transformation Layer LSA Dataflow Modeler Store Data Propagation Layer CorporatDefine ETL processes to populate the e Memorypersistency layers of the EDW Model with Harmonisation Layercleansed and consolidated, consistent andharmonized data in an adequate periodicity, Data Acquisition Layerwill say periodically based on batch or near-real or real time processes Transformations / DTP Source System handling Realtime Data Acquisition (RDA)© 2012 SAP AG. All rights reserved. 12
  • 11. SAP NetWeaver Business WarehouseScheduling and Monitoring the DataflowOrganize, schedule and monitor thedataflow towards and within the EDW andprovide tools to repair or redo unexpectedfailures during load processes. External ETL Processes Metadata Management Process Chains Admin Cockpit generating Repair Chains checking Error DTPs© 2012 SAP AG. All rights reserved. 13
  • 12. SAP NetWeaver Business WarehouseEDW Persistency and Performance ManagementProvide Data Management capabilities inorder to massage the data persistencyaccording to the specific characteristics of thedata and information partitions such as actual,frequently asked data, volatile data that isgoing to be updated very likely, old, read onlydata – with nearly no demand for reporting,data that has to be hidden but kept for legalreasonsProvide a technology for high performance Acceleration ArchivingOLAP processing on top of all parts of thedata resulting out of adequate modeling Staginfeatures (like Star Schema), particular gpersistency layers in the model (granular vs.aggregated data resp. information) andsophisticated storage paradigms© 2012 SAP AG. All rights reserved. 14
  • 13. Typical Bottle Necks - Short Comings of current Approach Missing analytical capabilities on DB level lead to massive AppServer/DBServer traffic – DataStoreObject (DSO) (e.g. Activation) – Integrated Planning (e.g. Disaggregation) Distributed data management (RDBMS vs. BWA vs. NLS vs. Archive) – Missing data aging strategies in RDBMS Nature of RDBMS - tupel based data storage, indexing necessary for performance – Read/Load Performance on the RDBMS (e.g. Extended SAP Star Schema too complex) Other Examples – Exception Aggregation (e.g. Distinct Count only available as BWA Calculation Engine feature)© 2012 SAP AG. All rights reserved. 15
  • 14. SAP NetWeaver BW Accelerator 7.20Addressing the RDBMS read and calculation performance bottleneckEnhanced built-in analytical capabilities* F4-Value help Today MultiProvider calculation handling ABAP AS Exception aggregation App (min, max, count distinct) “BW Workspace” Analytic indexesAdvanced features* SAP BW BWA based InfoCube Accelerator Calculation RDBMS Use DataStore Objects to create indexes Engine Aggregation Engine Index This presentation and SAPs strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is© 2012 SAP AG. All rights reserved. provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. 16
  • 15. SAP HANA In-memory Database In-memory database technology for Dummies SAP HANA 1.0 SPS3 – data base specific features
  • 16. In-Memory Computing – a new Lifestyle 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© 2012 SAP AG. All rights reserved. 18
  • 17. In-Memory Computing – The Time is NOWOrchestrating Technology InnovationsThe elements of in-memory computing are not new. However, dramatically improved hardwareeconomics and technology innovations in software have now made it possible for SAP to deliver on itsvision of the Real-Time Enterprise with in-memory business applications. HW Technology Innovations SAP SW Technology Innovations Multi-Core Architecture (8 x 8core CPU Row and Column Store per blade) Massive parallel scaling with many blades Compression One blade ~$50.000 = 1 Enterprise Class Server Partitioning 64bit address space – 2TB in current servers No Aggregate Tables 100GB/s data throughput Dramatic decline in price/performance Insert Only on Delta© 2012 SAP AG. All rights reserved. 19
  • 18. SAP HANA Database - TechnologyMultiple data storage methods: Column StoreClassical DB NewDB Column Store: NewDB Column Store: Dictionary compressed Run length compressed* 0 INTEL 0 INTEL 1 Siemens 0 A 1 Siemens 0 A Company Region Group 2 SAP 0 Europe 1 B 2 SAP 0 Germany 1 B [CHAR50] [CHAR30] [CHAR5] 3 IBM 1 USA 2 C 3 IBM 1 USA 2 C INTEL USA A 0 1 x „0“ 1 x „0“ 0 1 1 x „1“ Siemens Europe B 1 0 1 2 x „1“ 4 x „0“ 1 x „1“ Siemens Europe C 1 0 2 2 x „2“ 1 x „1“ 1 x „2“ SAP Europe A 2 0 0 1 x „3“ 3 x „0“ SAP Europe A 2 0 0 IBM USA A 3 1 0 * Note that there is a variety of compression methods and algorithms like run-length compression + (see Comparison of Compression Algorithms`)© 2012 SAP AG. All rights reserved. 20
  • 19. Scale – SW side distribute across cores DataData Distribution RAM locality – data gets spread out to all available cores MPP execution – blades share nothing when Hot Standby Blades for Failover crunching large data sets Failover - Individual blades may fail without causing problems© 2012 SAP AG. All rights reserved. 21
  • 20. What is New in SAP HANA ? – Overview I SAP HANA Database for SAP Business Warehouse SAP HANA as database for SAP BW 7.30 Single In Memory Persistence and Storage - all BW tables are In-Memory objects New In-memory optimized Data Store Objects (with SAP BW 7.3 SP5) New In-Memory DSO activation process New partitioning options New In-memory optimized InfoCubes (with SAP BW 7.3 SP5) Simplified design and indexing Faster data loads and simplified modeling In-Memory Planning engine Based on existing Integrated Planning (BW-IP) Push down of OLAP engine into SAP HANA from SAP BW‟s ABAP layer Tools to seamlessly migrate BW underlying database to SAP HANA Planning Engine In Memory operations like Disaggregation, copy, write-back Supporting BW – IP and Business ByDesign application use cases Includes linear equation solver© 2012 SAP AG. All rights reserved. 22
  • 21. What is new in SAP HANA SPS3? – Overview IIData AcquisitionNew HTTP/XML based data acquisition option with support for SAPApplication and SAP BW extractorsFurther integration of ELT features in SAP HANA with Data ServicesBack-up & Recovery and SecurityLog backups and Point-in-time recoverySSL connection encryption with certificates for client connectionsSAP Identity Management (IDM) integration for user provisioning into SAPHANAAdministration and monitoringIntegration into Solution Manager Performance Warehouse Alerting Infrastructure DBA CockpitEnhanced tracing capabilitiesImproved resource usage statistics© 2012 SAP AG. All rights reserved. 23
  • 22. SAP BW on SAP HANA database: valueproposition
  • 23. AVOID Bottlenecks – Data TransferClassical Approach Calculation APPLICATION LAYER DATABASE LAYER Calculation MOVE calculations into database Future Approach Only transfer RESULTS© 2012 SAP AG. All rights reserved. 25
  • 24. Application vs. Database Server - Technical OverviewApplications – Tight coupling between Application Server and SAP HANA  With large data volumes, reading information becomes Today a bottleneck ABAP AS  Next generation applications App will delegate data intense In-Memory empowered operations ABAP AS Next Generation  The runtime environment Next Generation Apps executes complex processes in memory Procedure Program code code  In memory computing returns RDBMS results by pointing apps to a Fast data location in shared memory compile transfer & deploy Data in Runtime memory SAP HANA© 2012 SAP AG. All rights reserved. 26
  • 25. SAP NetWeaver BW7.3 powered by SAP HANA – AddedValueAccelerated Performance  Excellent query performance as proven with BWA  Accelerated In-Memory planning capabilities  Performance boost for load processesSimplified administration and infrastructure  DB and BWA merging in one instance for lower TCO  Simplified administration via one set of admin tools e.g. for Data Recovery and High Availability  Column based storage with highly compression rates and significantly less data to be materialized  No special efforts to guarantee fast reporting on any DB object Scale  Simplified data modeling and reduced materialized layers  Integrated and embedded flexibility for Datamarts Speed Flexible© 2012 SAP AG. All rights reserved. 27
  • 26. SAP HANA Database - BWA AspectsProvide a BWA-like Query Performance directly on any data in the HANADatabase BW Query on Query on •InfoCube, Masterdata  BWA like query performance DSO, BW InfoSet •AnalyticIndex,  BWA index obsolete CompositeProvider BWA like performance  BW hierarchies on standard DSO tables  TopN filter „in-memory‟ DSO with Open SQL / SQL92 BW Analytics API optimized activation  Exception aggregation algorithm  Currency conversion  (more to come) … SAP HANA SQL Engine Calc Engine Aggregation Engine on In-Memory dataSome BWA-features behave just “as before” Snapshot Indexes for Virtual- and QueryProvider Analytic Indexes & CompositeProvider BW Workspaces © 2012 SAP AG. All rights reserved. 28
  • 27. Modeling and Dataflow Aspects for HANAbased BW BW‟s Layered Scalable Architecture (LSA) in times of HANA HANA optimized modeling objects in BW In-Memory Planning Consumption of HANA Models/Data in BW BW Staging from Sources in HANA
  • 28. The Layers of SAP„s Reference Architecture (LSA) Reporting, analysis-ready Reporting BI applications (Architected Data Mart Layer) Near-realtime, operational-like Reporting Layer Apply business logic Business Transformation Layer Operational Data Store digestible, LSA ready to consume, integrated, Data Propagation Layer Corporat unified data e Harmonisation Layer Memory source system like service level, comprehensive, complete, master the unknown, long term Data Acquisition Layer create harmonized view, guarantee quality, plausibility EDW Layer gate (Single Point of truth, reusable, granular, complete history) Extractor inbox, 1:1 Data sources from extraction, temporary© 2012 SAP AG. All rights reserved. 30
  • 29. Unchanged Data Warehouse Architecture –Real World Example Project Governance End-user access / Presentation Data Mart ODS ReportingMain Service : Make data available for reporting & planning toolsTransform : Application specific/(dis-)aggregate/lookupContent : Application specificHistory : Application specificStore : IC,DSO, Info Set, Virtual Provider, MultiProvider. Data Propagation Data Warehouse Corp. Main Service : Spot for apps/Delta to app/App recovery Memory Transform : Enriched || General Business logic Content : Data source || Business domain specific History : Determined by rebuild requirements of apps Store : DSO(can be logical partitioned) Business transform IT Governance Harmonization Main Service : Integrated, harmonized Transform : Harmonize quality assure (in flow|| lookup) Content : Defined fields History : Short or not at all || Long term Store : Info source || IO/DSO/Z-table Data Acquisition Main Service : Decouple, Fast load and distribute Transform : 1:1 Content : 1 data source, All fields History : 4 weeks Store : PSA, DSO-WO. Provide data Source 1 Source 2 Source 3 Source 4 Source 5© 2012 SAP AG. All rights reserved. 31
  • 30. Evolving In-Memory Footprint in SAP BWiew Data Data Persistency BW 7.0 BW 7.3Modeling and Runtime DB + BWA 7.0 DB + BWA 7.2 BW 7.3 on HANA Example in-memory Planning Engine 3 planning engine Enterprise Data Warehouse and Data Mart Modeling with SAP NetWeaver BW additional first calculation Analytic Engine calculations scenarios in BWA in-memory MultiProvider Example Consumption of filter + Data Manager handling and 4 HANA models in aggregation flexible joins BW BWA instead Example In-Memory BWA-only InfoCubes of 2 optimized InfoCubes aggregates InfoCubes Example reporting + BWA reporting DataStore Objects 1 activation for for DSOs DSOs in-memory HANA data for Data Provisioning BW Staging© 2012 SAP AG. All rights reserved. 32
  • 31. In-Memory DSO for SAP BWExample ILeverage HANA technology to implement In-Memory Optimized DSOswith a reduced amount of physical storage Accelerate data loads Allow faster remodeling of structural changesNo adoption of processes, MultiProviders, or Queries required!Or - to make it short - …Leaner & faster propagation layer!!!© 2012 SAP AG. All rights reserved. 33
  • 32. Today„s DataStore Object Delta DataStore Objects (DSOs) are Upload Query fundamental building blocks for a Data Warehouse architecture There are 4 operations on a DSO:  Upload (of new data) Active Data Change Log  Activation (Calculation of the current image) Activation process in ABAP  Querying (the current image) Generates heavy load on the database  Delta upload (for delta feeds) Roundtrips to the applications server for delta In todays RDBMS-based calculation implementation, the activation and querying operations are extremely Activation performance-critical. Queue These can be highly optimized in the SAP HANA database DataStore Object Parallel upload© 2012 SAP AG. All rights reserved. 34
  • 33. DataStore Objects in SAP NetWeaver BW 7.30Creation of consistent delta informationDelta calculation performed on theapplication server, too complex topush it down to the DBMS as SQL /Stored Procedure Calculate Lookup Update Delta Data PackagesRoundtrips to application server Sorted Full Table Scanneeded for delta calculationActivation algorithm creates heavyload on the DBMS© 2012 SAP AG. All rights reserved. 35
  • 34. DataStore Objects in SAP NetWeaver BW 7.30Creation of consistent delta informationDelta calculation performed on theapplication server, too complex to push itdown to the DBMS as SQL / StoredProcedure Calculate Lookup Update Delta Data PackagesRoundtrips to application server needed Sorted Full Table Scanfor delta calculationActivation algorithm creates heavy loadon the DBMS© 2012 SAP AG. All rights reserved. 36
  • 35. SAP BW - Data Store ObjectsMain Principles 5455 I +30 5455 I -30 5455 I 30 5455 I +20 5455 I 20 Former Load 5455 I 30 Actual Load 5455 I 20 BW DataStore Objects are threefold  Activation Table  Active Table  Change Log Table© 2012 SAP AG. All rights reserved. 37
  • 36. In-Memory Optimized DataStore ObjectsUsing In-Memory Computing TechnologyReplaced ABAP modules for requestactivation and rollback by HANA DBimplementationNo data processing in ABAP afterloading a request into the activationqueueUsing in-memory optimized datastructures for faster access HANA DB ImplementationNo roundtrips to application serverneededOptimization is transparent for theuser© 2012 SAP AG. All rights reserved. 38
  • 37. In-Memory Optimized DataStore ObjectsOverview and Design Delta calculation completely integrated in InMemDB – no data processing in ABAP View View History Index (column based)Main Index (column based) Delta Index (column based) Activation triggered Using in-memory optimized data by BW, performed structures for faster access by InMemDB No roundtrips to application server needed© 2012 SAP AG. All rights reserved. 39
  • 38. In-Memory Optimized DataStore ObjectsMapping Between Application Server and HANA DB Calculation Column based table History Index Valid from View Valid to Former Load 5455 I 30 ......5455 I Before Image 5455 I -30 Actual Load 5455 I 20 30.........dt1..............dt2... After Image 5455 I 20 Main Index ......5455 I 20 ..... valid from  Table replaced by dt2 calc view (uses history index to Delta Index create a change log Standard column based table view of the data)  no primary key, performance Temporal table  View calculates advantage 20% technical key on the  Uniqueness checked by SQL fly statement (DBMS exit)  Multiple updates for a particular key are consolidated into one© 2012 SAP AG. All rights reserved. 40
  • 39. In-Memory Optimized DataStore ObjectsPerformance Figures Activation Runtime - Lab Results 4500 4500 4000 Using in-memory computing technology 3500 … one of the most time consuming 3000 staging operations – the request activation – was speed up Runtime in seconds 2500 tremendously by factor 5 - 10 ... storage of redundant data was 2000 prevented 1500 1000 473 500 300 20 41 0 3 Delta: 0.1 M, Active: 1 M Delta: 1 M, Active: 10 M Delta: 10 M, Active: 100 M BW 7.30 - RDMBS based In-Memory optimized© 2012 SAP AG. All rights reserved. 41
  • 40. Summary: In-Memory Optimized DataStore ObjectsAccelerated data loadsIn-Memory optimized DSOs Delta calculation completely integrated in HANA User interface User interface Using in-memory optimized data Layer Layer structures for faster access Presentation Presentation No roundtrips to application server SAP NW BW SAP NW BW needed DSO Speeding up data activation by Application Objects Application DSO Layer Objects factor 5 – 10 Layer Activation Avoids storage of redundant data SAP NW BW SAP NW After the upgrade to BW on HANA BW all DSOs remain unchanged Database Database Activation Layer Tool support for converting Layer Data standard DSOs into IN-Memory SAP Data xDB DSOs planned HANA – No changes of Dataflows required This presentation outlines our general product direction and should not be relied on in making a purchase decision. This pres entation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAPs strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, ei ther express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.© 2012 SAP AG. All rights reserved. 42
  • 41. In-Memory InfoCubes for SAP BWExample IILeverage HANA technology to implement In-Memory Optimized InfoCubeswith “flat” structures and no Dimension tables and E tables in order to Accelerate data loads Simplify Data Modeling Allow faster remodeling of structural changesNo adoption of processes, MultiProvider, Queries requiredOr - to make it short - …Leaner & faster reporting layer!!!© 2012 SAP AG. All rights reserved. 43
  • 42. HANA optimized InfoCube Design in BWPhysical schema of BW InfoCube HANA can work with “flat” structurestailored and doesn‟t need E- and F-fact tables!towards traditional RDBMS M M MD MD D D D E F F Facts Migration / New Facts D M MMD MD D DBenefits:Fast data loads (no DIMIDs)  up to 80% time reductionDimensions not physically present  simpler modeling and faster structural changesAll processes, all Queries and MultiProviders can remain unchanged© 2012 SAP AG. All rights reserved. 44
  • 43. Standard InfoCube with Migration Option MD MD D E F Facts D MD MD© 2012 SAP AG. All rights reserved. 45
  • 44. In-Memory Optimized InfoCube M M D D F Facts M M D D© 2012 SAP AG. All rights reserved. 46
  • 45. BW Integrated Planning – based on SAP In-MemoryComputing Example IIILeverage SAP HANA Database Technology to bring data intense operations to the data optimize disaggregation features in Integrated PlanningOr - to make it short - …Smooth and fast planning tools …© 2012 SAP AG. All rights reserved. 47
  • 46. In-Memory PlanningThe Technological Change Classic Database In-Memory Database User interface Presentation Presentation Layer  Authorization  Locking Orchestration Orchestration  Hierarchies Application  input enablement Layer  list based planning Calculation  planning functions Calculation  MP handling Database  Conversions Layer  aggregation Data Data© 2012 SAP AG. All rights reserved. 48
  • 47. SAP NetWeaver BW 7.30In-Memory Planning - Simple Disaggregation Example user changes a plan valueTraditional Approach HANA-Based Approach1. Determine the delta  +50 1. Determine the delta  +502. Disaggregate (in appl. server) 2. Send 1 value to DB  per week (52) + instruction to disaggregate and  per branch (500) how  26000 combinations / values 3. Disaggregate (in DB engine)3. Send 26000 values to DB to save  per week (52)  per branch (500)  create + save 26000 values© 2012 SAP AG. All rights reserved. 49
  • 48. Consumption of HANA Models/Data in BWExample IVLeverage BW infrastructure to report on models created in HANA OLAP Engine to access HANA data BW Metadata Repository reflects HANA artifacts BW client support all kind of data within the HANA database Integration to BW InfoProvider (via CompositeProvider/Workspaces) Support Authorization Concept for meta-/data accessOr - to make it short - …Smooth and simple integration, “no” modeling© 2012 SAP AG. All rights reserved. 50
  • 49. Consumption of HANA Models - OverviewMixed Scenarios BW&HANA Schemas BW Query Query CompositeProvider Transient InfoCube Provider HAN A AnalyticView BW Schema HANA Schema(s)© 2012 SAP AG. All rights reserved. 51
  • 50. Publishing HANA modelsSelect Analytical View and generate VirtualProvider© 2012 SAP AG. All rights reserved. 52
  • 51. Analysis for Microsoft Excel - IAnalytical Indices/TransientProviders visible as DataSources© 2012 SAP AG. All rights reserved. 53
  • 52. Analysis for Microsoft Excel - IlQuery Result Example in Spread Sheet with Navigation Pane© 2012 SAP AG. All rights reserved. 54
  • 53. Summary and OutlookSix key points to take home
  • 54. Six key points to take homeStart spreading the news The evolution of in-memory technology at SAP moves on Latest stage: SAP HANA Database as a full fledged in-memory database SAP BW as one of the first applications fully enabled to leverage the key strength of the new HANA In-memory database – Accelerated performance o No special efforts to guarantee fast BWA like reporting on any DB object o Accelerated In-Memory planning capabilities o Performance boost for ETL processes (DSO Activation 5-10 times faster, InfoCube load 5 times faster ) – Simplified administration and infrastructure o DB and BWA merging in one instance for lower TCO o Column based storage with highly compression rates and significantly less data to be materialized and managed o Simplified data modeling and reduced materialized layers Dedicated optimizations available for different BW modeling objects LSA reference architecture will stay as the recommended model in BW with slight changes © 2012 SAP AG. All rights reserved. 56
  • 55. Thank youContact information:Rohit Kamathrohit.kamath@sap.com
  • 56. Hadoop, SAP BI, and HANAJeffrey Krone,ZettasetJune 30, 2012
  • 57. What we will Cover (Agenda) Why Hadoop What Customers are expecting Best Practice for Integrating Hadoop with SAP BI Healthcare Business case Take Away© 2012 SAP AG. All rights reserved. 61
  • 58. Why Hadoop? Big data makes organizations smarter and more productive by enabling people to harness diverse data types previously unavailable, and to find previously unseen opportunities April 17, 2012 Gartner© 2012 SAP AG. All rights reserved. 62
  • 59. Are companies really using Hadoop? “80% of Fortune 500 companies have a Hadoop cluster, less then 20% have it in production” - Aberdeen Group Managing Big Data is a Complex Task Analytics, BI Disaster Backup Monitoring Failover Continuation Requirements Recovery Integrate Services Management Compliance Management Scheduling Alerts S Installation Automation Into SAP BI Gap Access Provisioning Security Configuration Scalability Utility Control Security Hadoop Distribution Core No-SQL Source : SAP America© 2012 SAP AG. All rights reserved. 63
  • 60. What Customers are expectingMask complexities of Hadoop with an enterprise-consumable product to manage big dataEliminates dependencies on professional services,reduces IT resource requirements, and dramaticallylowers TCOSingle vendor capable of integrating Hadoop onnon-commodity hardware such as SSD, flash,supercomputers, etc.The Zettaset Platform• meets all above expectations• is easy to deploy, resilient, highly scalable, flexible• offers significant cost savings compared to other Big Data Platforms.© 2012 SAP AG. All rights reserved. 64
  • 61. Business Case - Background October 2012 compliance requirement Fortune 500 Health Care Company Requires detailed Physician‟s Billing Analytics Current data in10‟s of TBs and is growing very fast with a high degree of inclusion of semi-structured and unstructured data. Currently, the Health Care company is utilizing two legacy databases: Legacy database 1: is utilized for importing the initial data, scrubbing it, and transferring the cleaned up data to the 2nd legacy database. Legacy database 2: is utilized as the backend for Business Objects. In addition, the database enforces the business rules and transforms the data into the appropriate format for the BO Reports.© 2012 SAP AG. All rights reserved. 65
  • 62. Technology Issues Performance issues with Legacy Databases Report generation, business rules processing, etc. Scalability Issues The Health Care provider has reached the capacity limit of their current system‟s technical capabilities. Thereby making it difficult to onboard new customers efficiently. Database Schema updates are burdensome Each minor change to one legacy database requires multiple manual table updates to both legacy databases. No Automated Failover or Backup mechanism Inability to import and analyze Unstructured Data© 2012 SAP AG. All rights reserved. 66
  • 63. Business Drivers for Conversion Customer wants to prepare for the new health care “Compliance Mandates” Evidence-based medicine in health care reform - NCBI Obama plan for health reform includes evidence-based care – Healthcare IT News The Implications of the Health Care Reform – James Brown, MD. Expand analytical capacity and performance without compromise. Broaden current product portfolio to their customer base. Derive insight into new markets based on short term business intelligence and long-term big data analytics. Ability to store, process, and analyze structured, semi-structured and unstructured data.© 2012 SAP AG. All rights reserved. 67
  • 64. Best Practice steps – for a Hadoop Integration Business Technology Business Data Business Information Expectations Designing designing Integration Rules Harmonization Business Needs Data Source KPI definitions Data Types HANA Harmonize for Types Enterprise Expectations Business Rules Extraction Model Business Analytics Data Owners Rules Compliance Transformations Transformation Structured Data Volumes Business Data sources Harmonization Reduction Transformations Semi-structured Data Reduction Data Definitions Security Loading Business Unstructured Master data Definitions Integration Data Quality© 2012 SAP AG. All rights reserved. 68
  • 65. SAP HANA / Hadoop IntegrationHADOOP (ZTS ORCHESTRATOR)Accommodate both structured and un-structured dataPre-process and load the structured billing data via HadoopCombine structured and un-structured data within ZTSOrchestrator and transfer it to SAP HANA via a Hadoop / HANAConnector (SAP Provided)Leverage ZTS Orchestrator as a long term data repository andaggregator of all types of data.© 2012 SAP AG. All rights reserved. 69
  • 66. SAP HANA / Hadoop Integration - 1© 2012 SAP AG. All rights reserved. 70
  • 67. SAP HANA / Hadoop Integration - 2 SAP HANA  SAP HANA enforces the business rules via stored procedures and its columnar database utilizing their in-memory capabilities.  SAP HANA enables the Health Care Company to take a deep dive and perform sophisticated analytics on their data providing their customers new insights into their data.  SAP HANA / Business Objects enables real time reporting and analysis for their customers.  SAP HANA is utilized for the 1) Enforcement of the business rules, 2) Analytics, and 3) For generating Business Objects reports.© 2012 SAP AG. All rights reserved. 71
  • 68. SAP HANA / Hadoop Integration - 3Source: SAP America© 2012 SAP AG. All rights reserved. 72
  • 69. SAP HANA / Hadoop Integration - 4 SAP HANA New Reports / Analysis  Analyze Customer Billing Behavior – predictive analysis related to forthcoming billings based on historical trends  Analyze physician ratings and determine how they correlate to patient treatment and revenue.  Derive optimal treatments for patients based on doctor notations (i.e. analyze treatment by doctors to resolve specific issues for Good Patient Practices)  Streamline Customer‟s billing process and identify inefficiencies by analyzing unstructured notes related to billing / insurance transactions for patients.  Utilize SAP HANA to analyze unstructured text (i.e. patient notes, billing notes) and derive actionable intelligence.© 2012 SAP AG. All rights reserved. 73
  • 70. DEMODemo of Sample Healthcare Analytics© 2012 SAP AG. All rights reserved. 74
  • 71. Business Reports (SAP HANA)© 2012 SAP AG. All rights reserved. 75
  • 72. Business Reports (SAP HANA)© 2012 SAP AG. All rights reserved. 76
  • 73. Take Away The (SAP HANA / ZTS) system enables Health Care Customers to: Accommodate both structured and unstructured data via the ZTS Orchestrator. Combine Structured and Unstructured data within ZTS and transfer it to SAP HANA to handle enforcement of business rules, transformations and Unstructured Text Analytics. Utilize the HANA In-memory capabilities and breadth of SAP Analytic applications to perform sophisticated analytics (e.g. unstructured text analysis) providing Health Care Customers with new capabilities of performance and decision management. Substantially enhance performance, scalability, and ability to perform true-real- time reporting and analysis for Customers. Serve as the long term Historical Data Store (ZTS)© 2012 SAP AG. All rights reserved. 77
  • 74. Your Turn [© 2012 SAP AG. All rights reserved. 78
  • 75. Additional ResourcesDatasheets:Updated datasheet with specs, including ShadoopShadoop datasheetIntel:A Distributed Parallelized Platform for Handling Large Data” – Jeffrey KroneMarket Watch:SAP Continues to Expand Capabilities and Scale of SAP HANA® Platform and EaseDeveloper Adoption”Zions Bancorporation articles:CIO.com - Bank Adopts Security Data Warehouse to Fight Persistent Security ThreatsBanktech.com - Banks Push Hadoop Envelope to Open Big Datas Secrets© 2012 SAP AG. All rights reserved. 79
  • 76. Additional ResourcesZions Bancorporation articles:CIO.com - Bank Adopts Security Data Warehouse to Fight PersistentSecurity ThreatsBanktech.com - Banks Push Hadoop Envelope to Open Big Datas SecretsShadoop:CIO.com - Zettaset to Offer Role-Based Access Control for Hadoop© 2012 SAP AG. All rights reserved. 80
  • 77. DisclaimerSAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet®, PartnerEdge, BW,BusinessObjects, BO Explorer, HANA and other SAP products and servicesmentioned herein as well as their respective logos are trademarks or registeredtrademarks of SAP AG in Germany and in several other countries all over theworld.Zettaset is the logo and trademark of Zettaset in USA and several other countriesAll other product and service names mentioned are the trademarks of theirrespective companies.SIMH is respective logo of SAP In memory HANA Group in LinkedInNo part of this presentation may be copied or reproduced without the totalpresentation without written permission of the presenter or Zettaset© 2012 SAP AG. All rights reserved. 81
  • 78. Sponsors Logo Name SAP America ZettaSet K2 Partners ICM America© 2012 SAP AG. All rights reserved. 82
  • 79. HANA in Cloud-Based ScenariosSAP HANA in Cloud-Based ScenariosYusuf Bashir, HANA Solutions ManagementJuly 30, 2012 HANA Solutions ManagementYusuf Bashir,July 2012
  • 80. Cloud Provides Unique Advantages• Radical reduction in CAPEX By 2014, 31% of net new IT• No servers to buy spending will be invested in the public Cloud• Pay-as-you-go monthly subscriptions (OpEx) Spending on public Cloud services is growing 6x faster than IT spending• Elastic storage generally• Increasingly unpredictable data volumes (e.g. Big Data) By 2014, the total market for public Cloud services will be $56B, up• Data growth outpacing ability to persist locally from just $17B in 2009• Immediate provisioning & availability• Potential for higher SLAs vs. internal IT capabilities• Outsourced server administration and management “Enterprise data will increase 650% over the next 5 years” –Gartner “Enterprise data will double every 18 months” --IDC © 2012 SAP AG. All rights reserved. 85
  • 81. Cloud Landscape Is CrowdedDevelopers Prefer Options Around PaaS & IaaS© 2012 SAP AG. All rights reserved. 86
  • 82. SAP HANA for Cloud Solution Areas HANA for Cloud 1 2 3 HANA High Performance HANA “AppCloud” HANA Hosting Cloud• Sales & Operations Planning • Hosting & Managed Services •HANA Dev Edition Sandbox (free 30-day trial)• BI On Demand • Outsourcing & BPO/ITO• SAP Cloud Apps Expense Insight • Private Hosted Cloud • HANA High- HANA Dev Edition on Amazon Web Services• powered by Successfactors Analytics HANA Hosting • Performance HANA HPC for Productive Usage•Consumer HANA Apps (e.g. Recalls+, with select partners (coming)Charitra) Cloud © 2012 SAP AG. All rights reserved. 87
  • 83. HANA “AppCloud”Internal Use of HANA in the SAP Cloud• Sales & Operations Planning (S&OP) NEW• BI on Demand Advanced Edition• Expense Insight COMING SOON• Successfactors Analytics• Supplier InfoNet COMING SOON• Consumer Apps (Recalls+, Charitra)© 2012 SAP AG. All rights reserved. 88
  • 84. Hosting SAP HANA High Performance Cloud (HPC)Benefits of Participating Phase 1 Phase 2 HANA Hosting HANA High Performance Cloud HANA hosting on certified HW Global network of most strategic HANA hosting (open to all) partners (by invitation only) Benefits: Same benefits as HANA Hosting, plus: 1. Hosting packages promoted to 1. HANA Dev Edition to drive developer SAP account teams. adoption on paid cloud instances. 2. Ability to resell HANA with 2. Ability to resell select certified HANA Apps perpetual licenses only. with run-time HANA license. Internal deployment of HANA on cloud recommended as 1st step. Customers Using HANA Hosting Today Global HPC Partners Shortlisted: Visy (Telstra) Komatsu (Telstra) University of Kentucky (Dell)© 2012 SAP AG. All rights reserved. 89
  • 85. SAP HANA High Performance CloudEntry Point for HANA Developers & ISVs© 2012 SAP AG. All rights reserved. 90
  • 86. SAP HANA High Performance CloudBenefits of Utility Pricing Annual Costs Example Co-Located Data Center With Utility Pricing HANA on cloud can help maximize capacity during Capacity wasted Server $49,005 Instance $33,415 peak use with traditional Hardware Hours on-premise deployments Network Data $9,801 $1,215 Hardware Transfer Hardware $17,642 Maintenance Co-Location $504,187 Expense Remote Hands $6,075 Support Data Transfer $2,686 Total $589,395 Total $35,061© 2012 SAP AG. All rights reserved. 91
  • 87. SAP HANA High Performance CloudIdeal Choice for Analytics on Big DataTwitter generates over 300 Million Tweets ON PREMISEper day, translates to ~200TB of tweet data SAP Businessper year. Suite HILO-based User InterfaceTwitter‟s FireHose API delivers Tweets in ~3M records from ERPreal time @ 260Mbps. HANA HPCAssuming 0.1% relevancy of all Tweets to a SAP or Partner App Productcustomer, 200 GB of tweets (“hot data”) are Masterloaded into HANA using a high speedHANA Data Loader based on Data HANAServices 200 GB of Tweets & 3 million product master recordsMillions of product, customer or suppliermaster records can be pushed in real-time HANA Data Loaderfrom on-premise SAP ECC to HANA Cloud Data from other Hadoop-based Storeusing SAP Landscape Transformation Cloud 200 TB Tweets/Year Applications via(SLT). This table can then be used as Data Servicesfiltering criteria to select the hot data to loadinto HANA. Twitter Firehose API CLOUD © 2012 SAP AG. All rights reserved. 92
  • 88. SAP HANA License Models for CloudWhat‟s Available Today1. Test & Demo*  Rental for 5K € per instance per year (tiered pricing + regional uplifts)  Perpetual for 15K € per instance + annual maintenance (tiered pricing + regional uplifts)  Classic hosting offered by cloud partners on certified platforms.2. Development (Supported Platforms – Productive Usage)*  Perpetual for 2K € per user + annual maintenance (tiered pricing + regional uplifts)  Classic hosting offered by cloud partners on certified platforms.3. Development (Non-Supported Platforms – Non-Productive Usage)  License at no cost, developers pays for instances  Offered by Amazon Web Services, non-supported platform.4. Full Use Perpetual  Perpetual for 128K € per 64GB (HANA unit) + annual maintenance (tiered pricing + regional uplifts) or HANA Edge for 40K € limited to 64GB.  Classic hosting offered by cloud partners on certified platforms.5. COMING SOON: HANA App Run-Time*  Usage or flat royalty-based OEM restricted to select HANA Apps. Price of app determined by developer.  Only available with select certified HANA Apps through HANA High Performance Cloud partners. * only available to SAP Partners. © 2012 SAP AG. All rights reserved. 93
  • 89. SAP HANA for CloudFinal Summary HANA Cloud is a new deployment option (not a product) Customers can deploy HANA in the cloud via hosting partners + benefit from OpEx OEM of HANA for select certified apps coming with HANA HPC© 2012 SAP AG. All rights reserved. 94
  • 90. Thank youFor more information please contact:Yusuf BashirHANA in Cloud Solutions Management (Palo Alto Bldg 2)yusuf.bashir@sap.com+1 (415) 990-1333
  • 91. What is BI all aboutLogically Organized Business AwarenessExecutive Stakeholders: Dashboards and Info-widgetsBusiness Analysts: Self Service for „Don‟t Know‟ analyticsManagement: Performance and actionable Decision AnalyticsOperational: Data Reports and Graphical „Daily Reports‟Right Information when you need itReal-Time Benchmarking and AlertsInformation Consumption WorkflowHow do I need my information access organized© 2012 SAP AG. All rights reserved. 98
  • 92. Information Consumption PrioritiesGlobalize and mobilize Information„One Company, One Truth‟ globallyEase of access and useEasy to access and find information in a logical Info WorkflowSelf-Service AnalyticsIncreased decision output without increasing headcountPerformance Measures and AlertsManagement needs actionable KPI‟s for rapid responseException reportingProactive alerts management on business patterns & behaviorsDay-to-day global reportingInstant access to current state of business status© 2012 SAP AG. All rights reserved. 99
  • 93. Why HANA Application Type Records Query Run Time BW ETL delayed 20-50 mill < 10 seconds BW Accelerator ETL delayed 60-300 mill < 10 seconds Accelerated BO Explorer ETL delayed 60-300 <10 seconds million Accelerated BO WebI ETL delayed 60-300 < 10 seconds Analytics million HANA True Real-Time 1-‟N‟ billion < 5 seconds •These are independent benchmark results • BW results are dependent on optimal Architecture & Modeling and clean-up of Cubes • BWA Results are dependent on BW • BO Explorer results are based on accelerated data from BWA • HANA results are based on optimized db transforms and right-modeling© 2012 SAP AG. All rights reserved. 100
  • 94. „Without business in business intelligence, BI is Dead‟ Gartner 2010 Make your Customer the new lead of the company© 2012 SAP AG. All rights reserved. 101
  • 95. What we will Cover HANA in a SAP BI Landscape HANA Deployment Options HANA Best Practice Methodology Building your HANA SWOT Team The Evolution Path of HANA Social Network Questions© 2012 SAP AG. All rights reserved. 102
  • 96. HANA Architecture Administration Reporting Data Sources HANA Engine© 2012 SAP AG. All rights reserved. 103
  • 97. HANA in the SAP BI Landscape Excel Search WebI Excel Access Crystal Access OLAP Queries Dashboards OLAP BOBJ Widgets BOBJ Auto Oracle DW SearchE Data DATA WAREHOUSEX BEx BOBJ WADP EP TeradataL AD-HOC & PIXILATED ANALYTICS LARGE Db‟sOR * BW ON HANAE BR W SAP BW* SAP HANA 1.0 A Accelerate DATA WAREHOUSE REAL TIME ANALYTICS Asynchronous only BW Operational Queries Data-loads SAP NON SAP SAP ECC SUITES SOURCE SYSTEMS© 2012 SAP AG. All rights reserved. 104
  • 98. The HANA Deployment Options Stand Alone HANA Appliance • SAP ECC Data Only • Non-SAP Data inclusive BW on HANA (All) • Upgrade current BW to HANA • „Big-Bang‟ approach – All or None BW On HANA (Selective Passage) • Upgrade select BW InfoProviders to HANA • Selective BW Objects HANA deployment New BW on HANA installation • Each new BW Object designed for HANA from the start • Database level transforms from the start HANA Platform (Net New) • ECC on HANA • SAP BI on HANA© 2012 SAP AG. All rights reserved. 105
  • 99. HANA Best Practice Flow HANA SI© 2012 SAP AG. All rights reserved. 106
  • 100. The HANA Best Practice Flow Customer SI & SAP HW Partner Build Business Vision Business Value Attainment NA Introduce to RDS‟s SI & SAP NA Run HANA POC Choose RDS and run demo NA Work with Business Identify true business value NA Business Expectations Identify business Expectations NA Run Pilot Load Customer databases NA Confirm Vision Confirm vision Sizing Place HANA Order NA Delivery 8-12 wks Start building HANA On SI or SAP HANA appliance WIP HANA arrives Update Patches and check HW Checks HANA Start Migrate all HANA to Customers Box Checks Save 8-12 weeks Continue development NA© 2012 SAP AG. All rights reserved. 107
  • 101. The HANA Best Practice Checklist Leverage  Follow scientific the HANA checklist methodologies and processes  Plan your work and only then work your Plan  Follow the scientific rules of BI & HANA deployment© 2012 SAP AG. All rights reserved. 108
  • 102. Building the HANA SWOT Team• Identify the HANA - SWAT Roles• Identify the skills required for each Role• Measure each resource against all the skills• Finalize what additional training is required to match role to skills© 2012 SAP AG. All rights reserved. 109
  • 103. The HANA EvolutionStand-Alone BI ApplianceBW on HANAThe SAP HANA PlatformECC on HANAECC and BW running on a common HANA db Current   Future Stand Alone HANA BW on HANA ECC & BW on ECC & BW separate HANA using same HANA BW BW ECC BW ECC© 2012 SAP AG. All rights reserved. 110
  • 104. HANA QuestionsFrom HANA projects and Social Network discussions© 2012 SAP AG. All rights reserved. 111
  • 105. Now that I am certified what next?Today there are more certified consultants than HANA projects(current estimate is around 10k HANA certified resources)But this is going to change quite fastGreen-field HANA projects mandate SAP consultantsWithin a few months customers are starting to do it on their ownIf SAP continues on their projected path we will need around 20 to 30k consultantsin the coming yearsOne of the greatest risks to SAP HANA is a lack of qualified HANA resources withreal customer experience. Not just technical resources but resources with thecustomer experience, capabilities to align business vision and a passion for true‘Business Value Attainment’© 2012 SAP AG. All rights reserved. 112
  • 106. Will HANA Replace BW?As HANA is a database it can replace Oracle or DB2 but not the DWIf we look a little closer BW is actually becoming more critical, and stronger,in the SAP BI landscapeBW on HANANo direct ECC extracts to WebI, unless via BWWebI, Crystal & Dashboard now talk directly to BW without a universeIn ECC 7.3, SP 3 we have a hidden BW in ECC for pushing data to HANA stand-alone appliances© 2012 SAP AG. All rights reserved. 113
  • 107. In the long-run what HANA type will winStand Alone or BW on HANAVery difficult to forecast the future, but am willing to bet (based on what ishappening in Europe)BW on HANA will win the strategic race.. Many US customers start with a stand-alone HANA Most are starting to evolve to BW on HANA ABAP for HANA is already on the wayThere are over 17,000 BW installations worldwideEach one of these is a potential BW on HANA customer© 2012 SAP AG. All rights reserved. 114
  • 108. What do Customers really need?Customers don‟t need just a HANA installed (technocratic installation)They needBVA ( Business Value Attainment)Information that enables them to make better decisionsReview data without constraints of data volumesLook at their business in true real-time – globallyLook at business from inside, value chain and the outside© 2012 SAP AG. All rights reserved. 115
  • 109. How Stable is HANA today?1 year ago it was difficult to answer this question, today we can saywith confidence that HANA is a stable platformCurrent reports confirm over 400 HANA licenses issued, with around150-200 implementations underwayThe target of 200 million revenue was crossed, the 2012 target is 400million and right now is on trackEurope has more BW on HANA initiatives, and the US has moreStand-Alone HANA implementationsHANA is the fastest SW launch in the history of SW launches© 2012 SAP AG. All rights reserved. 116
  • 110. What are the current Risks?Need extractors to work FasterThis is not happening currently, not a technology capability issue - but process andknowledge issueReal skills don‟t come from training, but from customer experienceRight now this is not happening at a large enough scaleWe are all caught in the license vs. BVA sales conflictAt the „tipping Point‟ will there be enough great resources to carry the torchof business excellencePushing customers too hard on a sales and less on true vision and businessvalue© 2012 SAP AG. All rights reserved. 117
  • 111. What are the sweet spots in HANA?1. Build surgical Applications (Partners & Startups) a. App Types i. General apps that most can use ii. Commoditized Apps by Industry and segment iii. Competitive differentiator Apps b. SAP needs to support Partners and Start-ups a little more2. A solid Business Focused Methodology a. Build a culture of „Business Excellence‟ and not simply of deploying the technology3. Define a true HANA vision prior to commencing a. Just a TCO goal is not enough- there has to be more b. Run a Vision session with business prior to planning c. Use RDS‟s for initial POC and Pilots where possible© 2012 SAP AG. All rights reserved. 118
  • 112. Is HANA really very costly?We need to compare apples to applesSAP is very transparent on the HANA pricing. It is a single price based on perGB utilizedCosts can be planned and controlledCosts can be lowered by eliminating InfoElements® in BW environments and notcarrying junk InfoObjects into HANA (works for BWA too) = Minimum data formaximum informationNever forget the cost of moving a single element from your source systeminto your BI environment with ECC intelligence.Exalytics & Teradata have a suite of products and applications with multipleproducts, complex pricing and licensing. Hidden fees make strategic TCOdifficult to predict and far higherWhen dealing with very large data volumes in true-real-time - there is nocompetition for HANA© 2012 SAP AG. All rights reserved. 119
  • 113. Cost consideration or Competitive positioning …or how big do I need to be to consider HANA Size From What they do Medidata $200 million US Clinical Trials & Service for Pharma NRI NA Japan Data Processing services Hilti $1.9 billion Liechtenstein Construction tools and products Adobe $7.8 billion US Desktop Publishing SW B/S/H $9 billion German Home Appliances Surgutneftegas $12 billion Russia Oil & Gas Colgate Palmolive $15.6 billion US Home & Hygiene cleaning products Lenovo $16.7 billion Hong Kong PC manufacturer Centrica $34.7 billion UK Integrated Energy services Proctor & Gamble $78 billion US Home products BASF $ 84.7 billion German Chemicals© 2012 SAP AG. All rights reserved. 120
  • 114. Your Turn [© 2012 SAP AG. All rights reserved. 121
  • 115. Additional Resourceswww.experiencesaphana.comSAP site for all HANA related informationRapid Deployment Solutionshttp://bivaluenomics.blogspot.com/My Blog for SAP BI and HANALinkedin „SAP In-Memory HANA‟ GroupWorlds largest HANA Social GroupBI Valuenomics – The story of meeting business expectations in BIBook published in 2010Comparative AnalysisComparison between HANA, Teradata, Exadata and ExalyticsThe BI Eye-Q Test© 2012 SAP AG. All rights reserved. 122
  • 116. DisclaimerSAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet®, PartnerEdge, BW,BusinessObjects, BO Explorer, HANA and other SAP products and servicesmentioned herein as well as their respective logos are trademarks or registeredtrademarks of SAP AG in Germany and in several other countries all over the world.BIDT is the logo and trademark of BI Databridge in USA and several other countriesAll other product and service names mentioned are the trademarks of theirrespective companies.SIMH is respective logo of SAP In memory HANA Group in LinkedInNo part of this presentation may be copied or reproduced without the totalpresentation without written permission of the presenter or BI Databridge llc© 2012 SAP AG. All rights reserved. 123
  • 117. SPONSORS Logo Name SAP America ZettaSet K2 Partners ICM America© 2012 SAP AG. All rights reserved. 124

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