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TRANSCRIPT
SAP Innovation Summer Camp
Come with a Curiosity – Leave with a Plan
SAP BW 7.30 on HANA
Track Hosts:
Lars Buescher & Gilberto Henn
SAP AGS, July 2012
Finance Track The SAP ERP Financials Solution Powered by the SAP HANA platform
© 2012 SAP AG. All rights reserved. 3
Agenda
WHY do we talk about SAP BW 7.30 on HANA in Financials?
Enterprise Data Warehouse in our Financials Solution
Corporate functions enabled and supported by in-memory technology
WHAT is new, when SAP BW runs on a HANA powered DB?
Main advantages and process improvements
HOW: Architectural changes to enable these improvements
WHO is benefitting from BW 7.30 on HANA?
Acceleration of central corporate business functions
Acceleration of Financials Reporting
Financials Solution powered by SAP HANA
WHY do we talk about BW 7.30 on HANA in Financials
© 2012 SAP AG. All rights reserved. 5
Financials Solution Architecture:
Horizontal and Vertical Integration
Financial
Transfer
SAP LT
Business Reporting and Analytics
Central Financial Accounting
SAP ERP 6.0
Integrated Planning Consolidation
Enterprise
Performance
Management
MS Office On Demand
Services
Browsers Mobile
Information
Consumption
Reporting &
Analytics
Enterprise
Applications
Central Journal
= Real Time Replication with SAP Landscape Transformation Platform
Enterprise
Portals Embedded in
Enterprise Apps
(
…
)
Non-SAP
Any DB
In Memory
Enterprise
Data
Warehouse
Business Warehouse powered by
HANA
HANA Database
© 2012 SAP AG. All rights reserved. 6
SAP BW, powered by SAP HANA - Overview Complete coverage of local, operational and corporate needs
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 Business Suite
SAP HANA SAP BW
Enterprise Data Warehouse
Design and Operations
Analytic Engine (Queries)
SAP BW Architected Data Marts
Data Acquisition (DataSources)
SAP HANA Models
SAP BusinessObjects BI
Replication Services SAP BusinessObjects
Data Services
SAP BW ETL Connectivity
extraction
logic
realtime
replication
data
harmonization
analytic
requirements
Corporate View on Information
Standardization
Operational View on Information
Agility
Feed SAP BW or SAP
HANA through Extractors
or SBO DataServices
Agile SAP HANA Modeling to
complement corporate SAP BW
Models for ad hoc business needs
SAP BW Analytic Engine to offer complete
set of OLAP functionality on top of SAP
HANA Calculation Engine
Integrate SAP BW ABAP lifecycle management
with SAP HANA Operations
Integrate
corporate
information into
data marts
© 2012 SAP AG. All rights reserved. 7
(Corporate) Functions enabled and powered
by SAP HANA
Native (new)
functionality on SAP
HANA
SAP Consolidations
SAP Integrated
Planning
… more to come…
SAP Analytics
CO-PA Accelerator
HANA Accelerators
(side-by-side scenarios)
Finance & Controlling
Accelerators
Financials Accounting
Controlling
Material Ledger
Production Cost
Analysis
HANA Application
Accelerators
Project System
Reporting
Sales Order
Reporting
FI Open Item /
Aging Reporting
… Use your
imagination …
BW based functionality ERP based functionality ERP based functionality
Financials Solution powered by SAP HANA
WHAT is new in BW 7.30 on SAP HANA?
© 2012 SAP AG. All rights reserved. 9
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
SAP HANA-optimized DataStore Objects
SAP HANA In-Memory platform
SAP HANA-optimized 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, 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.
Migration without reimplementation - no disruption of existing
scenarios
© 2012 SAP AG. All rights reserved. 10
DataStore Objects in SAP NetWeaver BW 7.3 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 Table Change Log
Activation Queue
Query Delta upload
Parallel Upload
Activation
© 2012 SAP AG. All rights reserved. 11
DataStore Objects in SAP NetWeaver BW 7.3 Creation of Consistent Delta Information
Traditional 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
Lookup Calculate
Delta Update
Active Data Table Change Log
© 2012 SAP AG. All rights reserved. 12
SAP HANA-optimized DataStore Objects Accelerated Data Loads
SAP HANA-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 5-10
Avoids storage of redundant data
After the upgrade to BW on HANA all
DSOs remain unchanged
Tool support for converting standard DSOs
into SAP HANA-optimized DSOs
No changes of Dataflow required
Database
Layer
Database
Layer
User interface
Layer User interface
Layer
Application
Layer Application
Layer
Presentation
DSO Objects
Activation
Data
Presentation
DSO Objects
Activation
Data
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, 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.
SAP NW BW
© 2012 SAP AG. All rights reserved. 13
SAP HANA-optimized DataStore Objects Performance Figures
BW 7.30 - RDMBS based
Runtim
e in
seconds
© 2012 SAP AG. All rights reserved. 14
SAP HANA-optimized DataStore Objects Performance 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 5 - 10
... storage of redundant data was
prevented
Runtim
e in
seconds
© 2012 SAP AG. All rights reserved. 15
New InfoCube Design
SAP HANA-optimized InfoCubes Faster Data Loads and More Efficient Data Model
MD
F
Facts
MD
MD MD
Traditional InfoCubes tailored to a relational DB consist
of 2 Fact Tables and the according Dimension tables
SAP HANA-optimized InfoCubes 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, 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.
*Tables for compressed data
Facts
D
D
MD MD
MD MD
F E
Conversion / New
© 2012 SAP AG. All rights reserved. 16
Query performance Proven 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 BW
Query 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, 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. 17
Consumption of SAP HANA Data Models Tight Integration SAP HANA Data Mart Scenarios and SAP NetWeaver BW
CompositeProvider
InfoCube Transient
Provider
Query Query
BW schema HANA schemas
AnalyticView SAP
HANA
SAP NW
BW
HANA Data Marts and HANA In-Memory
platform for BW can run in one instance
Tight integration between HANA Data Mart
scenarios and SAP NetWeaver BW
Providing additional flexibility by combining
ad-hoc data models from Data Marts with
consolidated data in the EDW
No need to manually create/maintain Metadata
for Analytic Views in SAP NetWeaver BW
Transient InfoProvider dynamically generated on
top of Analytic Views during Query runtime
Query: e.g. Analysis, Xcelsius, Web Intelligence
(Web I)
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, 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. 18
BW In-Memory Planning Accelerated Planning Functions
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, 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.
Database
Layer
Database
Layer
User interface
Layer User interface
Layer
Application
Layer Application
Layer
Presentation
Orchestration
Calculation
Data
Presentation
Orchestration
Calculation
Data
SAP NW BW
SAP NW BW
SAP NW BW
SAP
HANA xDB
SAP NW BW
Financials Solution powered by SAP HANA
Integrated Planning on In-Memory Database
© 2012 SAP AG. All rights reserved. 20
In-Memory Database
S
AP
HA
NA
SA
P N
W B
W
S
AP
NW
BW
Classic Database
In-Memory Planning The Technological Change
xD
B
S
AP
NW
BW
SA
P N
W B
W
Presentation
Orchestration
Calculation
Data
Presentation
Orchestration
Calculation
Data
User
interface
Layer
Application
Layer
Database
Layer
Same user experience
• runtime UI
• modeling UI
Fully compatible
• Same models
• Authorization, locking
• List-based operations
… with faster
• Planning Functions
(Copy, distribute, Set value, …)
• Disaggregation in the query
© 2012 SAP AG. All rights reserved. 21
Current Planning Architecture
Database
Layer
Application
Layer
User interface
Layer Presentation
BEx query
Plan Session
Data
Meta data
Delta buffer Local cache
Constraints
Logic
save
Aggregation
Engine
Small data flow
Large data flow
© 2012 SAP AG. All rights reserved. 22
In-Memory Planning Architecture
Database
Layer
Application
Layer
User interface
Layer Presentation
BEx Query
Plan Session
Data index
Meta Data
Delta Buffer
Constraints
Logic
save
Meta Data Aggregation
Engine
Small data flow
Large data flow
© 2012 SAP AG. All rights reserved. 23
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
© 2012 SAP AG. All rights reserved. 24
In-Memory Planning Simple Disaggregation Example 2
Traditional Approach
1. Determine the delta +50
2. Disaggregate (in application 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 HANA database
+ instruction to disaggregate and
how
3. Disaggregate (in HANA engine)
per week (52)
per branch (500)
create + save 26000 values
user changes
a plan value
© 2012 SAP AG. All rights reserved. 25
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
• in-memory acceleration of reads
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
• in-memory acceleration of core functions
• further investments into performance
and functionality
Deployment Options for BW Planning 7.30
Classic DB
Database (SQL)
HANA
Database (SQL)
Calculation engine
Planning engine
HANA
Database (SQL)
Calculation engine
Planning engine
SAP BW
Integr. planning
Standard without in-memory Standard with in-memory Deep in-memory integration
SAP BW
Integr. planning
SAP BW
ABAP planning applications kit
User interface User interface User interface
BEX AAO BEX AAO BEX AAO
A B C
© 2012 SAP AG. All rights reserved. 26
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
• in-memory acceleration of reads
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
• in-memory acceleration of core functions
• further investments into performance
and functionality
Deployment Options for BW 7.30 Actions on Database Level
Classic DB
Database (SQL)
HANA
Database (SQL)
Calculation engine
Planning engine
HANA
Database (SQL)
Calculation engine
Planning engine
SAP BW
Integr. planning
Standard without in-memory Standard with in-memory Deep in-memory integration
SAP BW
Integr. planning
SAP BW
ABAP planning applications kit
User interface User interface User interface
BEX AAO BEX AAO BEX AAO
A B C
Database migration
Nothing to do
© 2012 SAP AG. All rights reserved. 27
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
• in-memory acceleration of reads
Server-side value proposition
• existing feature set (full compatibility)
• integration of reporting and planning
(no redundancies)
• in-memory acceleration of core functions
• further investments into performance
and functionality
Deployment Options for BW 7.30 Actions on Application Layer and Frontend Layer
Classic DB
Database (SQL)
HANA
Database (SQL)
Calculation engine
Planning engine
HANA
Database (SQL)
Calculation engine
Planning engine
SAP BW
Integr. planning
Standard without in-memory Standard with in-memory Deep in-memory integration
SAP BW
Integr. planning
SAP BW
ABAP planning applications kit
User interface User interface User interface
BEX AAO BEX AAO BEX AAO
A B C
Toggle switch, see
SAP Note 1637199
Nothing to do
Financials Solution powered by SAP HANA
WHO is benefitting from BW 7.30 on HANA?
© 2012 SAP AG. All rights reserved. 29
Major Benefits for YOU
No BWA needed
Data do not need to be indexed anymore, no
BW-Accelerator maintenance needed
Replacing the traditional database below BW
Reduction of Data redundancy
Storage Space is reduced dramatically
No new Cubes needed
Direct consumption from DSO’s and HANA views
Keep your traditional InfoCubes
Faster structural changes – agility for business users
Efficient loading process
Data is available for reporting faster, no need for “overnight extractions”
anymore
Faster integrated planning
Spend more time on simulations and analysis
All existing
content and
other
investments
is fully
compatible
and secured
Thank You!
SAP Americas Inc.
Lars Buescher
SAP Active Global Support
Solution Architect
T: +1 484-634-6531
E-Mail: [email protected]
SAP Americas Inc.
Gilberto Henn
SAP Active Global Support
Director – CoE Financials
T: +1 610-618-0838
E-Mail: [email protected]