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					SAP
World Tour 2012
SAP BW powered by HANA

Thomas Küng, HANA CoE SAP Switzerland Inc.
June 2012
Agenda


1.   Overview – why in-memory?
2.   SAP BW powered by HANA and its benefits
3.   Modeling opportunities
4.   Project approach
5.   Key points to take home




 SAP World Tour 2012                           3
1.    Overview – why in-memory?
2.    SAP BW powered by HANA and its
      benefits
3.    Modeling opportunities
4.    Project approach
5.    Key points to take home




   SAP World Tour 2012
SAP World Tour 2012                    4   4
The Evolution of SAP HANA
Landscape Options

     SAP HANA is an appliance to the application
      (e.g. SAP ERP).                                    Option „HANA“
     Its major benefit is increasing performance of      SAP ERP               SAP ERP
      transactional reporting for one system.                                               HANA 1.0
     SAP HANA replicates /loads data using SAP LT         RDBMS                RDBMS
      Replicator or DataServices
                                                         Option „HANA , SPS3“
     SAP HANA , SPS3 is the primary persistence for
      SAP NetWeaver BW 7.3, SP5.                         SAP NW BW        BWA             SAP NW BW
     All features of SAP NetWeaver BW can and
      should be used with SAP HANA , SPS3                   RDBMS                          HANA 1.0


     „SAP HANA vision“ is the next evolution step and   Option „HANA vision“
      replaces the DB of the ERP system                   SAP ERP               SAP ERP SAP NW BW


                                                           RDBMS                   „HANA vision“




    SAP World Tour 2012                                                                                5
Why we need In-Memory?
Business Challenges of a reporting environment


                                                       requests
                                                     sends report
                                                refinement required
                       Business
                         User                   sends report again                     IT




Business users require…                                             IT departments require…
   Up to date accurate information                                    Providing a landscape for real time reporting
   Get insight from the large volumes of information                  An architecture that scales to my business needs
   Be able to navigate and explore information to get what            Access to data in a more detailed level
    I need (self service)                                              Bring in a technology to fulfill new business
   Simulate scenarios and discover trends (ad-hoc                      requirements
    analysis)                                                          Reduce cost and time to implement new requirements
   React faster to events impacting my operations




 SAP World Tour 2012                                                                                                       6
Why NOW In-Memory
Orchestrating Technology Innovations

Hardware-Innovations                                   Software-Innovations

                       Multi-Core Architecture
                       (8 x 10 core CPU per Server)

                       Massive parallel scaling with
                                                              Row and
                       many nodes
                                                              Column Store


                       64bit address-space – 2TB              Compression
                       in current servers



                                                              No aggregates
                                                              neccessary
                       Prices for RAM are
                       decreasing




 SAP World Tour 2012                                                          7
Shift of Paradigm
Impact for applications



                       Classical Approach

                                 Calculation

                                               Application
                                                 Layer
  Current applications                                                      High performance
  perform many data-                                                        applications delegate
  intense operations in                                                     data-intense operations
  the application layer                                                     to the in-memory
                                                                            platform
                                               Database
                                                Layer
                                                              Calculation


                                                             SAP HANA Approach




 SAP World Tour 2012                                                                                  8
1.    Overview – why in-memory?
2.    SAP BW powered by HANA and its
      benefits
3.    Modeling opportunities
4.    Project approach
5.    Key points to take home




   SAP World Tour 2012
SAP World Tour 2012                    9   9
Evolving In-Memory Footprint in SAP NetWeaver BW

 Data                                        Data Persistency             BW 7.0           BW 7.3            BW 7.3 on HANA
Modeling                                      and Runtime              DB + BWA 7.0     DB + BWA 7.2         SP5 and beyond

                                                                                                                 In-memory
                                             Planning Engine                                                  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       Consumption of
                                                                           Filter +
                                                    Data Manager                         handling and        HANA models in
                                                                         aggregation
                                                                                          flexible joins           BW

                                                                                                                 SAP HANA
                                                                       BWA instead of      BWA-only
                                                        InfoCubes                                                optimized
                                                                        aggregates         InfoCubes
                                                                                                                 InfoCubes

                                                                                                                 SAP HANA
                                                                                         BWA reporting
                                                DataStore Objects                                                optimized
                                                                                           for DSOs
                                                                                                              DataStore Objects
   




                                                                                                                HANA data for
                                                    EDW Processes
                                                                                                                 BW Staging



 SAP World Tour 2012                                                                                                              10
SAP NetWeaver BW7.3 powered by SAP HANA
Added Value

Accelerated performance
 Excellent query performance as proven with BWA
 Accelerated In-Memory planning capabilities
 Performance boost for ETL processes


Simplified 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
                                                                         Speed
 Integrated and embedded flexibility for Data Marts


                                                                                   Flexible



  SAP World Tour 2012                                                                         11
Advantages of SAP BW powered by HANA
Difference between SAP BW on HANA vs. BW on xDB

                                             1




                                             2



SAP NetWeaver BW 7.x on xDB                      SAP NetWeaver BW 7.3, SP5 on HANA
 Database server and BWA server                  One SAP HANA In-Memory platform
 Standard DataStore Objects                      SAP HANA-optimized DataStore Objects
 Standard InfoCubes                              SAP HANA-optimized InfoCubes
 BW Integrated Planning                          In-Memory planning engine
 SAP HANA Data Marts running side-by-side        Consumption of SAP HANA models
                                                  BW staging from SAP HANA



                       Migration without new implementation –
                         No disruption of existion szenarios!

 SAP World Tour 2012                                                                      12
Current Situation
SAP BW DataStoreObject (DSO)


DataStore Objects are fundamental
building blocks for a Data Warehouse     Delta upload         Query
architecture
They are used to create consistent
delta information from various sources
Reporting can be done on a detailed
level
                                                                   Activation
In today's RDBMS-based
implementation, the activation and
querying operations are extremely
performance-critical




                                                 Parallel Upload

 SAP World Tour 2012                                                            13
SAP HANA optimized DSO
Faster Activation


In-Memory optimized DSOs
  Delta calculation completely integrated in
   HANA
                                                     traditional   In-Memory
      – Speeding up data staging to DSOs by factor
        5-10
      – Avoids storage of redundant data (CalcView
        instead of change log)


   After the upgrade to BW on HANA all
    DSOs remain unchanged
      – Tool support for converting standard DSOs
        into IN-Memory DSOs


   Using in-memory optimized data
    structures for faster access




 SAP World Tour 2012                                                           14
In-Memory Optimized DataStore Objects
Performance 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


 SAP World Tour 2012                                                                                                                          15
SAP HANA optimized InfoCubes
Faster Data Loads and simplified structure

                                                                                        traditional
In-Memory optimized InfoCube
                                                                 MD            MD
  In-memory Optimized InfoCubes represent “flat”                     D
   structures without Dimension tables and E tables (less                                   F
                                                                                                E
                                                                                    Facts
   joins).

 Simplified Data modeling and faster remodeling of                   D
  structural changes
                                                             MD             MD

 After the upgrade to BW7.3, SP5 all InfoCubes remain                Conversion/New
  unchanged
   – Tool support for converting standard InfoCube into                                 In-Memory
     IN-Memory InfoCube.                                    MD            MD

                                                                                                F
 Up to 5 times faster data loads (customer experience)
                                                                                Facts



                                                            MD            MD


 SAP World Tour 2012                                                                                16
In-Memory Planning
Simple Disaggregation Example


                                 user changes
                                  a plan value



Traditional Approach                             HANA-Based Approach
1. Determine the delta  +50                     1. Determine the delta  +50

2. Disaggregate (in application server)          2. Send 1 value to HANA database
  per week (52)                                    + instruction to disaggregate and
  per branch (500)                                 how
  26000 combinations / values
                                                 3. Disaggregate (in HANA engine)
3. Send 26000 values to DB to save                  per week (52)
                                                    per branch (500)
                                                  create + save 26000 values



 SAP World Tour 2012                                                                    17
1.    Overview – why in-memory?
2.    SAP BW powered by HANA and its
      benefits
3.    Modeling opportunities
4.    Project approach
5.    Key points to take home




   SAP World Tour 2012
SAP World Tour 2012                    18   18
Enterprise Data Warehouse Information architecture

  OLAP                  Ad-hoc         …                                      Predictive           …        Text/Web/
                                                   Dashboards
 Analysis               Reports                                                 Apps                       Social Media
                                                                                                                          SAP NetWeaver BW
                                                                                                                          •   Catalog of key figures and
                      Data Mart 1                        Data Mart 2           Data Mart 3
                                                                                                                              characteristics
                       Enterprise Data Warehouse                                                                          •   Data load und process handling
                                                                                                                          •   Business analytics and planning
                                           SAP NetWeaver BW
                          Agile &                                                       1                                 •   Authorizations
                        operational                            Architected Data Marts
                                           Management




                        Datamodel
                                            Metadata




                                                                Reporting / Planning                                      •   Change- und transport management
                                                               DataWarehouse Layer                                        •   Dataintegration SAP- and non-SAP-
                                                                 Acquisition Layer                                            data
                               2

                              SAP HANA                       Data Management Platform
                                                         SQL/Calc/Planning/Aggr. Engine                                   SAP HANA
                                                                                                                          •   Database for SAP NetWeaver BW
                                                                                                                          •   Platform and modeling for agile or
                                SAP BusinessObjects DataServices (Batch)                                                      operational data marts
                               Real-time Replication Services, Data-Streams                                               •   Real-time possibilities
                                                                                                                          •   High performance applications
                                                                                                                              delegate data-intense operations to
                                      MD                DB                                                                    the in-memory platform

            Structured data                              External data                      Unstructured data



SAP World Tour 2012                                                                                                                                                19
Consumption of HANA Models in BW
Mixed Scenarios BW & HANA Schemas – Link Models

                       BO


                                BW                       Query            Query

                                                    CompositeProvider              Link Models



                                     Transient
                                                                        InfoCube
                                     Provider



                                                          HANA

                            1                                     2
                                     AnalyticView
                                                                 BW Schema
                                        HANA Schema(s)




 SAP World Tour 2012                                                                             20
          Layered Scalable Architecture (LSA)
          Reducing of layers (example)
   Architected
    Data Mart
      Layer




                                          US AP EU

                                             1:1
   Transformation
      Business




                                                                      US APJ EU
        Layer




                                          US APJ EU
Propagation Layer




                               US APJ EU           AMS APJ EU   US APJ EU    AMS APJ EU
     EDW




                                   Sales         Delivery        Sales         Delivery
                                     LSA & BW on RDBMS             LSA++ & BW on HANA
                    SAP World Tour 2012                                                   21
1.    Overview – why in-memory?
2.    SAP BW powered by HANA and its
      benefits
3.    Modeling opportunities
4.    Project approach
5.    Key points to take home




   SAP World Tour 2012
SAP World Tour 2012                    22   22
Different project approaches


DB Migration to SAP HANA


 Preparation          DB migration            Optimization
                                              (LSA, opt. BW Objects)




Transport/rebuild of BW objects (partly)


                      Transportation          Optimization
                       (BW Objects, partly)         (e.g. LSA)

 Preparation                                                             Hist. DataLoad
                                                                              (historical data,
                          Rebuild             Optimization             master- and transactional data)
                                                (e.g. LSA, during
                       (BW Objects, partly)
                                                rebuild of objects)




SAP World Tour 2012                                                                                      23
Prerequisites for Migration to BW powered by HANA

■ NetWeaver BW 7.30 SP6 (minimum SP5); HANA 1.0 SP3
■ Separation of the Java Stack prior to the Upgrade of the ABAP Backend, if not
  already done
■ The new BI Authorization concept is now mandatory. Despite the fact that you
  either create a 7.0x System beforehand to migrate the old BW Authorization, a
  potential Upgrade from BW 3.5 to 7.30 would be possible.
■ The following source databases are supported for database migration:
         ■ Oracle 11.2
         ■ IBM DB2 LUW 9.7
         ■ MaxDB 7.8
         ■ MS SQL Server 2008
         ■ DB2 for i61, 7.1
         ■ DB2 for z/OS V9, V19




    SAP World Tour 2012                                                           24
1.    Overview – why in-memory?
2.    SAP BW powered by HANA and its
      benefits
3.    Modeling opportunities
4.    Project approach
5.    Key points to take home




   SAP World Tour 2012
SAP World Tour 2012                    25   25
Summary

•     Overview – Why in-Memory?
      •  Large data volume
      •  Real-time data
      •  Simulation scenarios
      •  Conclusion: faster reaction time and possibility for new
         business requirements

•     SAP BW powered by HANA and its benefits
      •  In-Memory optimized objects for much better
         performance (activation/loading and reporting)
      •  Simplified InfoCube
      •  Calculations for planning on the in-memory plattform
      •  Conclusion: reduction timeframe of data loading and
         much better reporting/planning performance

•     Modeling opportunities
      •  Combination of agile and architected data marts
      •  Simplified layered architecture (LSA)
      •  Conclusion: more flexibility and less data
         redundancies

•     Project approach
      •  Full migration of existing SAP BW (DB migration)
      •  New installation and partial transport of existing data
         models
      •  Conclusion: Different project approaches depending
         on customers requirements

   SAP World Tour 2012
SAP World Tour 2012                                                26   26
Thank You
Thank you

Contact information:

Thomas Küng
Senior Solution Consultant
HANA Center of Excellence (CoE) Switzerland
thomas.kueng@sap.com
Backup
“SAP BW powered by HANA” in a Nutshell


How does BW-HANA differ from a standard BW 7.3?

(1) DB + BWA  In-Memory DB

(2) InfoCube            In-Memory Optimized InfoCube
(3) DSO                 In-Memory Optimized DSO
(4) Planning            In-Memory Based Planning Engine
(5) Consumption of HANA models
(6) BW staging from HANA




 SAP World Tour 2012                                       30
“SAP BW powered by HANA” in a Nutshell


How does BW-HANA differ from a standard BW 7.3?

(1) DB + BWA  In-Memory DB
         One license, one instance
         No BWA required, no data replication
         Query performance at least as good as with BWA
         Full BWA feature support
         Pure DB Migration, same look&feel. Investments preserved




SAP World Tour 2012                                                  31
“SAP BW powered by HANA” in a Nutshell


How does BW-HANA differ from a standard BW 7.3?

(1) DB + BWA  In-Memory DB

(2) InfoCube             In-Memory Optimized InfoCube
          Simplified flat structure (only one fact and no dimension tables)
          Easier modeling, faster re-modeling
          Faster data loads (no DIMDIs)
          Simple conversion to new type




 SAP World Tour 2012                                                           32
“SAP BW powered by HANA” in a Nutshell


How does BW-HANA differ from a standard BW 7.3?

(1) DB + BWA  In-Memory DB

(2) InfoCube            In-Memory Optimized InfoCube
(3) DSO                 In-Memory Optimized DSO
     Activation step in HANA engine
     Shortened load window
     Fast reporting on DSO




 SAP World Tour 2012                                    33
“SAP BW powered by HANA” in a Nutshell


How does BW-HANA differ from a standard BW 7.3?

(1) DB + BWA  In-Memory DB

(2) InfoCube            In-Memory Optimized InfoCube
(3) DSO                 In-Memory Optimized DSO
(4) Planning            In-Memory Based Planning Engine
     Push-down of Planning steps to HANA database
     Faster Planning cycles




 SAP World Tour 2012                                       34

				
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