Post on 27-Oct-2014
transcript
Lothar Henkes / Brian Wood, SAP BW Product Management
Month September, 2011
EIM201
SAP NetWeaver BW 7.3 Overview and Roadmap
© 2011 SAP AG. All rights reserved. 2
Disclaimer
This presentation outlines our general product direction and should not be relied on in making a
purchase decision. This presentation 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 SAP's
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, either
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.
Agenda
Highlights SAP NetWeaver BW 7.3
Enterprise Data Warehousing
SAP NetWeaver Business Warehouse Accelerator
SAP NetWeaver BW‘s use and roadmap of In Memory technology
Deploying HANA Data Mart with SAP NetWeaver BW
Next version SAP NetWeaver BW powered by SAP HANA
Summary
© 2011 SAP AG. All rights reserved. 4
SAP NetWeaver BW AdoptionProductive SAP NetWeaver BW Systems – Constant Growth
Stable Product, Large installed Base,
Constant Growth
Adoption of SAP NetWeaver BW constantly
growing
Unaffected by economic down-turn in 2009
More than 12,000 customers referring to
more than 15.000 productive systems
13,910
14,217
14,450
14,693
14,952
15,243
15,533
15,671
12,000
12,500
13,000
13,500
14,000
14,500
15,000
15,500
16,000
Q3 0
9
Q4 0
9
Q1 1
0
Q2 1
0
Q3 1
0
Q4 1
0
Q1 1
1
Q2 1
1
Agenda
Highlights SAP NetWeaver BW 7.3
Enterprise Data Warehousing
SAP NetWeaver Business Warehouse Accelerator
SAP NetWeaver BW‘s use and roadmap of In Memory technology
Deploying HANA Data Mart with SAP NetWeaver BW
Next version SAP NetWeaver BW powered by SAP HANA
Summary
© 2011 SAP AG. All rights reserved. 6
Executive Summary Major Benefits of SAP NetWeaver BW 7.3 are:
Enhanced Scalability & Performance for faster decision making
Remarkably accelerated data loads
Next level of performance for BW Accelerator
Increased flexibility through SAP BusinessObjects BI and EIM integration
Tighter integration with SAP BusinessObjects Data Services
Enhanced integration with SAP BusinessObjects Metadata Management
Reduced TCO and improved development efficiency
Automated creation of Semantic Partitioned Objects
Graphical data flow modeling and best practice data modeling patterns
Simplified configuration and operational management
Admin Cockpit integrated into SAP Solution Manager
Wizard based system configuration
© 2011 SAP AG. All rights reserved. 7
BW 7.3 Ramp-up Program November 2010 – May 2011
Very successful Ramp-Up
program
Overall 36 customer projects
Very good feedback from
customers on:
Installation & Configuration
Quality & Reliability
Functional completeness
Currently 24 live customers
Reference customer – Bluefin, UK http://www.sdn.sap.com/irj/scn/go/portal/prtroot/docs/libr
ary/uuid/105520f5-4a93-2e10-47ac-
a0a94c354490?QuickLink=index&overridelayout=true
Quality / Stability
• The quality and stability of the release is of a good standard.
BW 7.3 seems to be stable and has very useful functionality
• Minor issues (fixed by Support). Satisfied with overall quality and stability
• . . .
Functional Completeness
• Provides several capabilities of major advantage to lowering TCO
and simplifying EDW architecture
• Great Release
• . . .
Improved data load performance
• 20 mio records loaded. Remarkable improved data load performance
seen
• . . .
―
© 2011 SAP AG. All rights reserved. 8
Enterprise Data WarehousingPerformance & Scalability
Enhanced modeling capabilities to increase scalability,
flexibility and reduce development and maintenance
overhead
Semantical Partitioning
New type of modeling object with 7.3
Single point of entry for creation and admin
Wizard based creation of data models + data flows
Easy re-modeling of the partitions on an ongoing basis
Embeddable into data flows or data models
In-Memory SAP NetWeaver BWA support
Full integration with archiving and Near Line Storage functionality
Flexible definition of partitions via customer coding (Business Add-Ins)Source1 Source2 Source3
Sem
an
tical
Part
itio
ned
Ob
ject
(SP
O)
© 2011 SAP AG. All rights reserved. 9
SAP NetWeaver Business Warehouse 7.3 Development Efficiency
Graphical Data Flow Modeling introduces new paradigm of BW modeling
Graphical Top-Down Modeling
Enables fast structured modeling directly in the system via drag & drop
Supports later refinement of the same models with technical details
Structure BW implementation with the help of data flows
Group the modeling objects and save them as view of your enterprise model
Document your dataflows via HTML or attach any other document type
Fully integrated in DW Workbench navigation pane for easy access
Ability to transport according to data flows
Share and reuse models using naming conventions for the dataflow
Modeling powered by dataflow templates
Easier and faster modeling using predefined templates (copy and adapt)
Customer can create company standards defining custom templates
Pre-build templates of the Layered Scalable Architecturehttps://www.sdn.sap.com/irj/scn/index?rid=/webcontent/uuid/3032b447-b56d-2e10-8eae-c82e29bd1525
© 2011 SAP AG. All rights reserved. 11
Enterprise Data Warehousing Performance & Scalability -Accelerated Data Loads for DataStore Objects
DataStore objects
Support of database partitioning by time characteristics enables faster access of the data
Activation is changed from single lookups to mass lookup of active table
Avg. 20 – 40% improvement, Max. improvement – 2.5x faster, (lab results)
Varies by data profile (# inserts/updates/deletes) and database platform
Runtime option ―New, unique data records only‖
Omits lookups during activation
Speeds up initial loads, e.g. requests which contain only new data
New rule-type for Transformations: ‗Read from DataStore‘
Fast data lookup to DataStore objects in database and Near-Line Storage.
DBMS specific optimization (DB2 DPF and Teradata)
Activation using database specific SQL commands on mass data
Improvement measured (lab results): factor 2 – 3 times faster compared to standard activation
© 2011 SAP AG. All rights reserved. 13
Enterprise Data Warehousing Real-Time Business Intelligence
Automated combination of write- and read-optimized data containers
HybridProvider
Combines mass data with latest delta
information at query runtime
Consists of a DataStore object, an InfoCube and
an automatically generated data flow between
the objects
DSO object can be connected to a real-time data
acquisition DataSource/DTP
If the DataSource can provide appropriate delta
information in direct access mode a VirtualProvider can be used instead of the DSO.
Facilitates replication of DSO-/VirtualProvider data to SAP NetWeaver BW Accelerator by
switching off database persistency of the InfoCube
© 2011 SAP AG. All rights reserved. 14
Enterprise Data Warehousing SAP BusinessObjects Data Services
SAP BusinessObjects Data Services is SAP’s strategic
solution for adding flexibility in extracting non-SAP data
Available today:
Data Services to schedule loading processes of SAP NetWeaver
BW
Execute and control process chains from Data Services to extract
data via Open Hub Service
SAP NetWeaver BW 7.3 / Data Services XI 4.0:
New Source System type in BW: ‗Data Services‘
Access to Data Services ‗Data Stores‘ and generation of Data
Services data flows (i.e. simple dataflow from DataStore to BW
DataSource)
Tight integration of Data Services into extended SAP NetWeaver
BW data flow concept
© 2011 SAP AG. All rights reserved. 15
Enterprise Data Warehousing SAP BusinessObjects Metadata Management
End-to-End Data lineage and change impact analysis in
heterogeneous environments
SAP BusinessObjects Metadata Management XI 3.1
Change impact analysis between SAP NetWeaver BW objects
such as DataStore objects, InfoSets, BEx Queries, and InfoCubes
End-to-end capabilities for all data sources through SAP
BusinessObjects Data Services
End-user access to data lineage information for trusted SAP
NetWeaver BW deployment
SAP BusinessObjects Information Steward 4.1
Include detailed field mapping information from source to target
with SAP NetWeaver BW 7.3, SP5
Relation
al
Sources
ETL
DW
SAP
NetWeaver
BW
Analysis
and
Reports
Data Lineage
Impact Analysis
© 2011 SAP AG. All rights reserved. 17
SAP NetWeaver BW Integrated Planning
Continued investments for reduced TCO of SAP NetWeaver BW Integrated Planning
ABAP Planning Modeler
ABAP based Planning Modeler for a non-disruptive
customizing of planning objects and their
related BW objects
The ABAP-based Planning Modeler does not
require functionalities from the Java Stack
for BEx Analyzer based planning applications
© 2011 SAP AG. All rights reserved. 18
SAP NetWeaver BW Accelerator 7.20 Taking the BW Accelerator to the next level of performance
Enhanced built-in analytical capabilities *
F4-Value help
MultiProvider calculation handling
Exception aggregation (min, max, count distinct)
BWA based InfoCube
Use DataStore Objects to create indexes
―BW Workspace‖ Analytic indexes
* Features require update/future release of SAP
NetWeaver BW to be leveraged
SAP NetWeaver BW Accelerator
Calculation Engine
Aggregation Engine
Index
SAP NetWeaver BW
© 2011 SAP AG. All rights reserved. 19
SAP NetWeaver BW Accelerator 7.20 Analytic Index & Composite Provider
Flexible modeling with APD & BWA in SAP NetWeaver BW
Any output of an APD process can be materialized as a analytic
index
Analytic Indexes are exposed as an InfoProvider for Queries
definitions on top of it
Composite Provider: Simple modeling of compositions of analytic
indexes (unions, joins)
Join/Union operation is processed on the fly
CompositeProviders are exposed as standard SAP NetWeaver BW
InfoProvider for BI client
consumption
Agenda
Highlights SAP NetWeaver BW 7.3
Enterprise Data Warehousing
SAP NetWeaver Business Warehouse Accelerator
SAP NetWeaver BW‘s use and roadmap of In Memory technology
Deploying HANA Data Mart with SAP NetWeaver BW
Next version SAP NetWeaver BW powered by HANA
Summary
© 2011 SAP AG. All rights reserved. 23
What is In Memory Computing?
In Memory Computing moves data and information sources from remote databases into local
memory so that results of analyses and transaction are available immediately
Answer Any Question Immediately
100x Faster Analytics
Access Current and Complete Information
Real-Time Access to Transactional Data
Discover Deeper Insights
Eliminate aggregation to interrogate granular data
Manage Large Data Volumes Cost Effectively
Groundbreaking In Memory HW Innovations
Speed
Scale
Flexible
© 2011 SAP AG. All rights reserved. 24
In-Memory Computing – The Time is NOWOrchestrating Technology Innovations
HW Technology Innovations
64bit address space – 2TB in current
servers
100GB/s data throughput
Dramatic decline in price/performance
Multi-Core Architecture (8 x 8core CPU per
blade)
Massive parallel scaling with many blades
One blade ~$50.000 = 1 Enterprise Class
Server
Row and Column Store
Compression
Partitioning
No Aggregate Tables
Insert Only on Delta
The elements of in-memory computing are not new. However, dramatically improved hardware
economics and technology innovations in software have now made it possible for SAP to deliver on
its vision of the Real-Time Enterprise with in-memory business applications
SAP SW Technology Innovations
© 2011 SAP AG. All rights reserved. 25
SAP HANA SAP In Memory Appliance
MDX SQL BICSSQL
ModelingStudio
Real–Time Replication Services
Data Services
SAP HANA
Other Applications SAP BusinessObjects
SAP NetWeaver BWSAP Business Suite 3rd Party
In-Memory Computing Engine
Calculation and Planning Engine
Row & Column Storage
Preconfigured Analytical Appliance
In-Memory software + hardware(HP, IBM, Fujitsu, Cisco)
In-Memory Computing Engine Software
Data Modeling and Data Management
Real-time Data Replication via Sybase Replication Server
Data Services for ETL capabilities from SAP Business Suite, SAP BW and 3rd Party Systems
Capabilities Enabled
Analyze 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 currentapplications in category
Minimizes data duplication
© 2011 SAP AG. All rights reserved. 26
Different Needs … Different Types of Data Marts
Architected Data Marts:
Consolidated and integral part of EDW & LSA supporting
decision making on corporate data
Centrally managed by IT, standardized data models on
corporate information, aggregated
Long term requirements in terms of stability and consistency
Operational Data Marts:
Real Time Data and volatile
Reporting on large volumes of granular, transactional data
Supporting local business execution
Agile Data Marts:
Independently of the centralized corporate EDW layers
Maximum flexibility for LOBs in data modeling
and integration of LOB specific data
Support strategic decision making in LOBs
Volatile and historical data with fluid data models
Corporate
data sources
Ad-hoc
data sources
Operational
Data Marts
Agile
Data MartsArchitected
Data Marts
SA
P N
etW
ea
ve
r B
W
Inbound data
Data Marts
BW Extractors
and ETL
Data Store Objects
Corporate Memory
InfoCubes
ETL
Operational
Real-time
replication
and ETL
© 2011 SAP AG. All rights reserved. 27
HANA Agile Data Mart Scenario…Running Side by Side SAP NetWeaver BW
Providing a powerful Data Mart scenario
Centrally managed SAP NetWeaver BW solution for core EDW use cases
Provide LOBs or subsidiaries with an agile analytical environment
Load selected analytic models from SAP NetWeaver BW into the agile data mart environment
Flexible remodeling and extending of loaded analytic models
Outstanding query performance
Direct access for SAP‗s BI clients and MS Excel
SAP NW BW
RDBMS
HANA 1.0BWA
Agenda
Highlights SAP NetWeaver BW 7.3
• Enterprise Data Warehousing
• SAP NetWeaver Business Warehouse Accelerator
SAP NetWeaver BW‘s use and roadmap of In Memory technology
• Deploying HANA Data Mart with SAP NetWeaver BW
• Next version SAP NetWeaver BW running on HANA
Summary
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 29
Evolving In-Memory Footprint in SAP NetWeaver BW
Planning Engine
Data Manager
InfoCubes
DataStore Objects
Analytic Engine
Data Persistency and
Runtime
Data
Modeling
En
terp
rise
Da
ta W
are
ho
use
an
d D
ata
Ma
rt M
od
elin
g w
ith
SA
P N
etW
ea
ve
r B
W
BWA instead of
aggregates
Filter +
aggregation
BWA-only
InfoCubes
BWA reporting
for DSOs
In-Memory optimized
DataStore Objects
In-memory
planning engine
First calculation
scenarios in BWA
Additional
calculations
in-memory
MultiProvider
handling and flexible
joins
BW 7.0
DB + BWA 7.0
BW 7.3
DB + BWA 7.2Planned: BW 7.3 on HANA
SP5 and beyond
EDW Processes
In-Memory optimized
InfoCubes
Consumption of HANA
models in BW
HANA data for
BW Staging
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 30
In-Memory Computing Product - VisionSAP High Performance Analytic Appliance
In-Memory Computing Platform
SAP
BusinessSuite
Mobile
SAP
BusinessSuite
BI Clients
SAP
BusinessWarehouse
NewSAP
Applications
Further
Applications
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 31
The Evolution of SAP HANA – Landscape Options
SAP HANA is an appliance to the application (e.g. SAP ERP).
Its major benefit is increasing performance of transactional
reporting for one system.
SAP HANA replicates /loads data using SAP LT Replicator or
DataServices
SAP HANA , SPS3 is the primary persistence for SAP NetWeaver
BW 7.3, SP5.
All features of SAP NetWeaver BW can and should be used with
SAP HANA , SPS3
„SAP HANA vision― is the next evolution step and replaces the DB
of the ERP system
(*) Migrating to further SAP HANA releases is optional
Option „HANA“
SAP ERP
RDBMS
SAP ERP
RDBMS
HANA 1.0
Option „HANA , SPS3“
SAP ERP
RDBMS
SAP ERP
„HANA vision“
Option „HANA vision“
SAP NW BW
SAP NW BW
RDBMS
SAP NW BW
HANA 1.0
BWA
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 32
Planned: 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
Simplified data modeling and reduced materialized layers
Integrated and embedded flexibility for Datamarts Speed
Scale
Flexible
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 33
Planned: SAP NetWeaver BW7.3 powered by SAP HANA How does BW 7.3 running on HANA differ from BW running on xDB ?
SAP NetWeaver BW 7.x on xDB
Standard DataStore Objects
Data Base server and SAP NetWeaver BWA
Standard InfoCubes
BW Integrated Planning
HANA Data Marts running side-by-side BW
SAP NetWeaver BW 7.3 on HANA
In-Memory based DataStore Objects
SAP HANA In-Memory platform
In-Memory based InfoCubes
In-Memory planning engine
Consumption of HANA artifacts created via HANA studio
BW staging from HANA
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
Migration without reimplementation - no disruption of existing scenarios
© 2011 SAP AG. All rights reserved. 34
DataStore Objects in SAP NetWeaver BW 7.30 Overview and challenge
DataStore Objects are fundamental building blocks
for a Data Warehouse architecture
They are used to create consistent delta information
from various sources
Reporting can be done on a detailed level
In today's RDBMS-based implementation, the
activation and querying operations are extremely
performance-critical
Active Data TableChange Log
Activation Queue
QueryDelta upload
Parallel Upload
Activation
© 2011 SAP AG. All rights reserved. 35
DataStore Objects in SAP NetWeaver BW 7.30 Creation of Consistent Delta Information
Current architecture
Activation algorithm calculates the changes of
each record and creates heavy load on the DBMS
Delta calculation performed on the application
server, too complex to push it down to the DBMS
as SQL / Stored Procedure
Roundtrips to application server needed for delta
calculation
Activation Queue
Sorted Full Table Scan
Data
Packages
LookupCalculate
DeltaUpdate
Active Data Table Change Log
© 2011 SAP AG. All rights reserved. 36
Planned: In-Memory Optimized DataStore Objects Accelerated data loads
In-Memory optimized DSOs
Delta calculation completely integrated in HANA
Using in-memory optimized data structures for
faster access
No roundtrips to application server needed
Speeding up data staging to DSOs by factor 7-10
Avoids storage of redundant data
After the upgrade to BW on HANA all DSOs
remain unchanged
Tool support for converting standard DSOs into IN-
Memory DSOs planned
No changes of Dataflows required
Database
Layer
Database
Layer
User interface
LayerUser interface
Layer
Application
LayerApplication
Layer
Presentation
DSO Objects
Activation
Data
Presentation
DSO Objects
Activation
Data
SAP NW BW
SAP NW BW SAP NW BW
SAP NW BW
SAP HANA xDB
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 37
In-Memory Optimized DataStore ObjectsPerformance Figures
BW 7.30 - RDMBS based
Runtim
e in
seconds
© 2011 SAP AG. All rights reserved. 38
In-Memory Optimized DataStore ObjectsPerformance Figures
BW 7.30 - RDMBS based In-Memory optimized
Using in-memory computing technology
… one of the most time consuming staging
operations – the request activation – was
speed up tremendously by factor 7 - 10
... storage of redundant data was prevented
Runtim
e in
seconds
© 2011 SAP AG. All rights reserved. 39
Planned: Query performanceProven query performance as known from BWA
Query acceleration on BW InfoCubes
No replication – fast query access directly on primary
data persistence
Indexes on InfoCubes and InfoObjects no longer required
-> No Roll-ups, Change runs
In-memory Calculation Engine
– TopN, BottomN,
– Exception aggregation
– Currency conversion
– . . .
Snapshot Indexes for Virtual- and QueryProvider
Query acceleration on BW DataStore Objects(DSO)
Acceleration via In-Memory column storage
Additional acceleration via Analytic Views on top of DSO
No changes of processes, MultiProvider, Queries required
SAP NW BWQuery on
InfoCube, Masterdata
AnalyticIndex,
CompositeProvider
Query on
DSO, BW InfoSet
SAP HANA SQL Engine Calc Engine
Aggregation Engine on In-Memory data
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 40
Planned: In-Memory Optimized InfoCubesFaster data loads and easier modeling
Facts
MD MD
MD MD
F
Facts
D
D
MD MD
MD MD
FE
Migration/New
Traditional InfoCubes tailored to a relational DB consist
of 2 Fact Tables and the according Dimension tables
In-memory Optimized InfoCubes tailored to HANA represent
―flat‖ structures without Dimension tables and E tables:
Up to 5 times faster data loads (Lab Results)
Creation of DIM Ids no longer required
Simplified Data modeling
Faster remodeling of structural changes
After the upgrade to BW7.3, SP5 all InfoCubes remain unchanged
Tool support for converting standard InfoCubes
Preliminary lab result: 250 Million records in 4 minutes
No changes of processes, MultiProvider, Queries required
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 41
Planned: Consumption of SAP HANA data modelsTight integration SAP HANA Datamart scenarios and SAP NetWeaver BW
CompositeProvider
InfoCubeTransient
Provider
QueryQuery
BW schema HANA schemas
AnalyticViewSAP HANA
SAP NW BWHANA Datamarts and HANA In-Memory
platform for BW can run in one instance
Tight integration between HANA DataMart
scenarios and SAP NetWeaver BW
Providing additional flexibility by combining ad-
hoc data models from Datamarts with
consolidated data in the EDW
No need to manually create/maintain Metadata
for Analytic Views in SAP NetWeaver BW
Transient InfoProider dynamically generated on
top of Analytic Views during Query runtime
Query: e.g. Analysis, Xcelsius, Webi
Integration BW Analysis Authorization Concept
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 42
Planned: BW In-Memory PlanningAccelerated planning functions
Database
Layer
Database
Layer
User interface
LayerUser interface
Layer
Application
LayerApplication
Layer
Presentation
Orchestration
Calculation
Data
Presentation
Orchestration
Calculation
Data
SAP NW BW
SAP NW BW SAP NW BW
SAP NW
BW
SAP HANA xDB
Traditional Planning runs planning
functions in the App. Server
In-memory Planning runs all planning
functions in the SAP HANA platform
Performance boost for planning capabilities
like:
Aggregation, Disaggregation
Conversions, Revaluation
Copy, Delete, Set value, Repost, FOX
Performance boost for plan/actual analysis
No changes of planning models, planning
processes, MultiProvider, Queries required
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 43
Planned: BW In-Memory Planning Simple Disaggregation Example
Traditional Approach
1. Determine the delta +50
2. Disaggregate (in appl. server)
per week (52)
per branch (500)
26000 combinations / values
3. Send 26000 values to DB to save
HANA-Based Approach
1. Determine the delta +50
2. Send 1 value to DB
+ instruction to disaggregate and how
3. Disaggregate (in DB engine)
per week (52)
per branch (500)
create + save 26000 values
user changes
a plan value
© 2011 SAP AG. All rights reserved. 44
Align Showcase portfolio
Identify | Validate | Approve
Collaborate to Innovate
Key Products | Business Champion
IT Solution Lead
Mobilize Community
Educate | Engage | Develop
Deliver
Corporate Strategy | Global IT Strategy
Evangelize
Customer Engagement | Events | Media
Social Media
SAP Runs SAPAn Engine for Role Modeling and Competitive Advantage
© 2011 SAP AG. All rights reserved. 45
Implementing HANA: High Adoption Rate at SAP
Side-by-Side
• Sales Pipeline
• Profitability Analysis Reporting
• Rapid Deployment Solutions
• Business Process Management
• Customer Product Usage
Real-time Data Store
• BW 7.30 on HANA
New Applications
• SAP Dynamic Cash Management
• Strategic Workforce Planning
© 2011 SAP AG. All rights reserved. 46
HANA Enables New Business ScenariosExample: Sales Pipeline Reporting
Yesterday
in the life of
a Sales
Executive
• 650.000 Opportunities maintained in
different transactional systems (12 million
records, 700 million historical data
records)
• Sales Pipeline reporting compiled into
Business Warehouse
• Multiple reports to conduct trend analysis
• Low Performance and End-User
Experience leads to limited usage
Long Data latency and
data inconsistencies
No sufficient decision
support
No full historical data for
probability analysis
Limited validation of
opportunities
Multiple systems and
applications
High operations and
maintenance Costs
No drill down to single
opportunities
No insights in critical
opportunities
CRM ERP
Business
Warehouse
Data
aggregation
Specific
Reports
© 2011 SAP AG. All rights reserved. 47
HANA Enables New Business ScenariosExample: Sales Pipeline Reporting
Today!
based on
HANA
Live since May 13,
2011
• Data latency reduced from 2 hours to
seconds
• Enables real-time decisions and
significantly increase usage
• Exception based analysis to focus
priorities
• Mobile Device Usage: user specific
access to information anytime anywhere
Increased overall Quality of
Information available
High performance
reporting
Real time decision
Support
Predictive analysis of
historical data
More confidence in
sales forecast
Simplified Architecture
Overall cost reduction
Access to all details
early corrective action
to influence opportunities
CRM ERP
Historical dataHANA
Sybase
Replication
Server
© 2011 SAP AG. All rights reserved. 48
New BI HANA Architecture
Classic
DB
CRM BW
Classic
DB
ECC
HANA
Replication Replication
Extraction Extraction
Business Objects
© 2011 SAP AG. All rights reserved. 49
New BI HANA Architecture
Classic
DB
CRM BW
Classic
DB
ECC
HANA
Replication Replication
Extraction Extraction
Business Objects
Pipeline
Data
CO-PA
Data
ADRM Report
Demo
© 2011 SAP AG. All rights reserved. 51
Take Aways
• Great opportunity to rethink the way we do our business today and leverage this new Technology for redefining the
business processes.
• Distinguish between operational reporting and corporate reporting
• Operations reporting on HANA 1.0
• Corporate analytics on BW and later with BW on HANA 1.0 SP3
• SAP Global IT continues investing in BW solutions
• Usage today for Operational and Agile Data Marts scenarios to replace existing solutions with HANA 1.0
(to analyze huge volume of data in real-time)
• No downtime required on source system side.
© 2011 SAP AG. All rights reserved. 52
Summary
• SAP NetWeaver BW 7.3 offers additional flexibility and modeling capabilities tohighly reduce development efforts
• SAP HANA 1.0 will run non-disruptively side by side SAP NetWeaver BW providing Agile and operational Data Mart scenarios
• SAP NetWeaver BW 7.3 powered by SAP HANA will provide a performance boost for data loads and planning capabilities and a BWA like query performance
SAP NetWeaver BW evolving to a fully In-Memory enabled EDW solution on top of
HANA
This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation 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 SAP's 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, either 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.
© 2011 SAP AG. All rights reserved. 53
Further Information
SAP Public Web:
• SAP Developer Network Enterprise Data Warehousing(general):
http://www.sdn.sap.com/irj/sdn/edw
• EIM 206, The New Planning Modeler and In-Memory Planning Application: Powered by SAP
HANA
• EIM 207, Upgrade to SAP NetWeaver Business Warehouse 7.3
• EIM208, Customize and Automate the EDW LSA Implementation in SAP NetWeaver BW7.3
• EIM 261,Ad-Hoc modeling of In-Memory accelerated data in SAP NetWeaver Business
Warehouse
• EIM 300, SAPNetWeaver Business Warehouse: Powered by SAP HANA
• EIM202, Deep Dive into the SAP In-Memory Technology, Strategy, and Roadmap
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Appendix