DMM205.pdf

Public
DMM205 – Data Management Strategies with SAP
HANA for SAP Software Systems

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 2
Public
Speakers
Bangalore, October 5 - 7
Anudeep Hegde
Las Vegas, Sept 19 - 23
Richard Bremer
Barcelona, Nov 8 - 10
Richard Bremer

Public
Disclaimer
The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of
SAP. Except for your obligation to protect confidential information, this presentation is not subject to your license agreement or
any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this
presentation or any related document, or to develop or release any functionality mentioned therein.
This presentation, or any related document and SAP's strategy and possible future developments, products and or platforms
directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice.
The information in this presentation is not a commitment, promise or legal obligation to deliver any material, code or functionality.
This presentation 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. This presentation is for informational
purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this
presentation, except if such damages were caused by SAP’s intentional or gross negligence.
All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially
from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only
as of their dates, and they should not be relied upon in making purchasing decisions.

Public
Agenda
Introduction
 What are “large volumes of data”?
 Tools in and around SAP HANA for managing large data volumes
Data management for SAP S/4HANA
 ILM and archiving
 Data aging
Data management for SAP HANA Data Warehousing
 SAP HANA dynamic tiering
 Warm data management in SAP BW powered by SAP HANA

Public
Introduction
What are “large volumes of data”?
Tools in and around SAP HANA for managing large data volumes

Public
Often 100s of TB,
into PB range
No clear categorization
in OLAP/OLTP
Can have
mission-critical
component
Mostly low
value-density data
Partially / largely unstructured
Laying the foundations
What are “large volumes of data”?
“Large” is relative – scenario, size of business, …
SAP S/4HANA
 Few TB can be large
 Often extreme requirements
on business continuity
Data Warehousing
 Tens to hundreds of TB
 Focus on reporting, analytics
and data management capabilities
IoT and other Big Data
 Order of Petabytes
 Volume and data management
critical
 Demanding variety and velocity
SAP
HANA
SAP
S/4HANA
SoH
SAP HANA
data
warehouse
IoT
scenarios,
“Big Data”
Terabytes
to tens of terabytes
Mission critical systems
Very high OLTP requirements
Increasing OLAP workload
Dominated by high value-density data
Tens of Terabytes, into range of 100s of TB
Focus on reporting and analytics
Can have characteristics of system of record
Mix of high- and low-value-density data

Public
The three pillars of a data management strategy for SAP HANA
Minimize data in HANA
 Archiving
 Housekeeping
 Generally: classical data volume
management (DVM)
 Plus: simplifications of data
model, e.g. SAP S/4HANA
Scale HANA memory
 Find the hardware configuration
fitting to your needs
 Scale-Up
 Scale-Out
Reduce memory footprint
 Data aging
 Dynamic tiering
 Generally: multi-temperature
data management
Data Volume Management in the Context
of SAP S/4HANA Conversions
i
ITM211 (L1)
Simple Steps for Aging Your Data in
SAP S/4HANA
i
DMM166 (HO2)
Managing Data Temperature with
SAP HANA Dynamic Tiering
i
DMM266 (HO2)
Data Center Architecture for SAP
HANA (On Premise, Hybrid, Cloud)
i
ITM214 (L2)
The Goal
 Enable you to run any SAP System of any Size on SAP HANA

Public
Laying the foundations
Reasons for implementing data volume management
Motivations for active data volume management
Data Center Operability:
 Elasticity of data volume in given hardware
 Business-continuity SLAs often limit in-DB
data volume
Technical Feasibility:
 Size limits of single-node HANA
 Maximum number of scale-out nodes
Viability:
 Not all data has high value density
Extreme example: IoT and other Big Data scenarios
 Legal obligations
SAP HANA
External store
(e.g. archive)
Application
Tier 2
Tier 1

Public
In-DB data lifecycle and archiving technologies
Two complementary approaches at data management
In-DB data lifecycle (DT, aging, …)
Reduce memory footprint of data in HANA
 Gain elasticity for data volume in chosen hardware
Data prioritization approach
 Transactionally integrated part of database
 Technically full read/write access to data
– Applications may prohibit writing
Not separable from SAP HANA DB
 Same availability SLAs, coupled operations
 All data in backups, HSR for all data, ...
Externalization (archives, NLS, …)
Reduce size of HANA database to be managed
 Optimize system management
Archiving-approach to data management
 Move data to external store
 Read-only
– Accessibility depends on application / archive store / …
Independent archive operations
 Typically lower availability SLAs
 Independent backups, not included in HSR, ...
Prioritize > Archive

Public
The toolset
SAP’s offering for large-scale data management
There is no Swiss Army knife
HANA-integrated stores
 Pure HANA technology
(e.g. data aging)
 Dynamic Tiering
HANA-connected
 Remote databases (SDA)
 Hadoop / HANA Vora
Archiving solutions
 Application-specific
SAP HANA
(memory store)
Data Files
Dynamic Tiering
(disk store)
Unload from memory
of “historical” or ”warm”
data for S/4HANA and BW
Archive Stores
SAP HANA Vora
Applications / End Users
SAP IQ
SAP HANA Database

Public
Data management for SAP S/4HANA
ILM and archiving
Data aging

Public
SAP S/4HANA: data management strategy
Archiving, SAP ILM, and data aging
Data management strategy in SAP S/4HANA
Based on two pillars:
 Archiving is the basis for data management:
– Proven and well-established standard data archiving
– Manage end-of-life of data / system using SAP
Information Lifecycle Management (ILM)
 Data aging
– Optimize storage costs of technical and business objects
such as IDOCs
– Standard data management approach for newly
developed HANA-based applications such as SAP
S/4HANA Finance.
SAP S/4HANA
Data Aging
SAP ILM
Footprint reduction
potential estimated
by sizing report
(SAP note 1872170)
Compliant Archive
Archive
Classic
Archiving

Public
Current vs. Historical based on business logic
Example: SAP S/4HANA Finance
Aging governed by two sets of criteria
 Customizing: residence time
 Business logic
Which FI documents do not qualify for aging?
 Within Residence Time
(Current and previous Fiscal Year)
 Documents with open items (or recently cleared)
 Documents that are operationally important
 Even if a document is outside of residence
time, it may remain “current”
2015
2014
2013
…
2016
2012
current
historical
Simple Steps for Aging Your Data in
SAP S/4HANA
i
DMM166 (HO2)

Public
Trading-off memory footprint, performance, and default visibility
2
4
n
Main OLTP
work load
Typical OLAP
work load
Data
Invisible Data
Minimal in
main memory
Configurable
in main
memory
Primarily on
disk
Applications to
implement access
Built-in
conditions
(SAP Dev)
Residence
time
(Customer)
H
i
s
t
o
r
i
c
a
l
Visible
C
u
r
r
e
n
t
Years
(example)
By default

Public
Partitioning by application-defined temperature
The technical view (I)
Additional generic Aging column for horizontal
partitioning
Insert Aging value (temperature) during aging run
 To move closed objects from HOT/Current to COLD/Historical
 To support partition pruning
 Same value for all records of object
 ABAP Data type: DATA_TEMPERATURE (DATS)
COLD partitions
Many of them
 Mapped to page-loadable columns (a.k.a. paged attributes)
 No uniqueness enforcement by DB
 SQL accessible
Update to Cold
Hot Partition Cold Partition(s)
Aging Key
Cold
Cold
Cold
Cold
Cold
Aging Key
For more information on page-loadable
columns, see Time Selection Partitioning in
the SAP HANA administration guide at
http://help.sap.com/hana_platform#section4
i

Public
Partitions and access using SQL
The technical view (II)
A B … _DATAAGING
… 00000000
… 00000000
… 00000000
… 00000000
… … … …
Partition 1
CURRENT
Partition 2
2012
Partition 3
2011
Partition 4
2010
SELECT A, SUM(B) FROM T GROUP BY A WITH RANGE_RESTRICTION(‘20120701‘)
Aging Column
A B … _DATAAGING
… 20120511
… 20120511
… 20120127
… 20120713
… … … …
A B … _DATAAGING
… 20110723
… 20110217
… 20111016
… 20111016
… … … …
A B … _DATAAGING
… 20100117
… 20101118
… 20100922
… 20100922
… … … …
A B … _DATAAGING
… 20120511
… 00000000
… 20111016
… 00000000
… … … …
“Time Selection” Partitioning on column _DATAAGING
Partition Pruning

Public
Access to data
Split „Current ↔ Historical“ not exposed in Fiori Applications

Public
Released data aging objects
1 Basis – available SAP Business Suite on HANA and SAP S/4HANA
2 Application – SAP S/4HANA only
Application Log1
IDocs1
Change Docs1
Workflow1
FI Document2
Unified Journal Entry2
Material Document2
✔
Availability
SAP NetWeaver 7.40 SPS 12
SAP Simple Finance add-on 1.0
✔
✔
✔
SAP NetWeaver 7.40 SPS 12 ✔
SAP S/4HANA - 1511 update
✔
✔
SAP S/4HANA Finance 1503 SPS 03
SAP S/4HANA – 1511 update

Public
Summary: Aging & archiving in SAP S/4HANA Finance
Data Archiving
 We target a full archiving coverage for Finance within SAP S/4HANA for both On-
Premise and Cloud
 Current Situation
 SAP S/4HANA refactoring has led to some changes regarding archiving, cf.
Note 2190848
 In the context of related investments regarding Data Privacy and Protection
we will re-establish full coverage for Finance scope within SAP S/4HANA
Data Aging
 Data Aging is regarded as major strategic element within SAP S/4HANA Finance
 We target completion of data aging capabilities within Accounting and
subsequent tackling of the subject within Operations: Cash, Treasury, etc.
 We are aware of the necessity of mass data handling of possibly unprecedented magnitude
 The general SAP S/4HANA aging & archiving strategy also holds for Finance
KEY MESSAGES

Public
Data management for SAP HANA Data
Warehousing
SAP HANA dynamic tiering
Warm data management in SAP BW powered by SAP HANA

Public
EDW Propagation
EDW Transformation
SAP BW powered by SAP HANA
Warm-data management and BW Nearline Storage
BW – Operational Data
Data Categories in a BW System
Staging Layer
Analytic Mart
Business Transformation
Corporate
Memory
EDW Propagation
EDW Transformation
BW
NLS
Archived
Use of warm-data management in SAP BW (dynamic tiering or planned extension node concept):
PSA tables, write-optimized DSOs of the corporate memory
With some care: selected “warm” tables of the EDW propagation and transformation layer

Public
Data Management in the SAP HANA Data Warehouse
HANA-centric DWH with SAP BW and HANA native capabilities
Modern ways for data management
 Multi tiering via Dynamic Tiering,
Hadoop and SAP IQ
 Virtual access (SDA tables) as core
technology
 Integration possibilities for all components
and profiles
 Hadoop as Data Lake (not DW) for HANA
DWH (via Open ODS Views)
 Hadoop as Near-Line Storage (NLS) only for
SAP BW (since SAP BW 7.5 SP4)
 Age to Hadoop via DLM for SAP HANA
models
Hadoop
SAP BW
HANA
Modeling
Dynamic Tiering
SAP
HANA
Vora
NLS
SAP IQ /
Hadoop
Open ODS View
virtual table (SDA)
virtual
table
read
BI Clients

Public
Key aspects at a glance
Add-on option to SAP HANA
Manage data of different temperatures
 Hot data (always in memory) – classical HANA
 Warm data (disk based data store)
Introducing a new type of table:
 Extended table – disk-based columnar table
 A table is either 100% in memory or 100% extended (SPS 12)
Extending the SAP HANA database
Deep integration
 Installation, monitoring, administration, backup, HA
 Common transaction management
 Transparent query processing & optimization
Target scenarios
Data warehousing, analytical applications
SAP HANA
hot store
(in-memory)
SAP HANA warm store
(dynamic tiering)
Extended table
(definition)
Extended table
(data)
Fast data movement and optimized push down
query processing
All data of extended table resides in warm store
SAP HANA Database System
Hot table
(definition/data)

Public
Data and system management with dynamic tiering
T1 T2
Hot Warm
Explicit Split
Data Management
State as of SPS 12:
 Object-based
 application controlled
 Tool support using DLM
 Cross-store query optimizer,
including Calculation Views
Outlook
 Multi-tier data management
 Rule-based temp. assignment
Backup & Recovery
State as of SPS 12
 Integrated backup (whole system)
 Full point-in-time recovery
 Full, file-based backup
 Backint Interface implemented
Outlook
 Delta backup mechanism
 Storage snapshots
Disaster Recovery
System Replication
 Not built into SPS 12
 Planned for future release
 Storage replication (hardware
solution) possible
Dynamic
tiering
HANA
Index
server
One Data backup
Name
server
XS
engine
DT
table
spaces
Data Center 2
Data Center 1
OS: Mounts
Cluster Manager (virt. IPs)
Primary
(active)
Host 1 Host 2
Secondary
(active, data pre-loaded)
HA
Solution
Partner
HA
Solution
Partner
Transfer
by
HANA
database
kernel
Host 1 Host 2
Data /
Log
Data /
Log
Data /
Log
Data /
Log

Public
HANA
Standby
node
2 TB RAM
Planned New Warm Data Management for SAP BW
“Extension node” concept in HANA scale-out landscape
Extension
node
“Standard” Group: normal
BW sizing, CPU/RAM ratio
 HOT data location
Extension node for warm data:
relaxed sizing requirements
(higher data/RAM ratio)
Master Slave
1
Slave
2
Slave
3
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
2 TB data
SAP BW
This is the current state of planning and may be changed by SAP at any time.
Standby
node
2 TB RAM
Standby
node
2 TB RAM
Enhanced Data Lifecycle Management for Warm
Data
• Easy to set up and significantly reduced administration
effort
• Support of all SAP HANA features for operations, updates
and data management
Typical Landscape Characteristics
• Usage of standard SAP HANA nodes
• Simplified sizing formula
• Optimized RAM/CPU ratio for warm data
(Runs as “asymmetric” Scale-Out cluster) *
• Differentiation between “hot” and “warm” via BW
application (definition of Object Groups)
• Supported BW objects: PSA, write optimized DataStore
Objects , advanced DataStore Objects
In order to implement the extension node concept in
SAP BW, refer to SAP Note 2317200:
https://launchpad.support.sap.com/#/notes/0002317200
i
Find steps for configuring an extension node setup in
SAP Note 2343647:
https://launchpad.support.sap.com/#/notes/0002343647
i * Planned

Public
HANA Extension Group Deployment Options
Option 1 (“simple”):
• Assign an existing node as “extension node”
• Capacity of extension node: data <= 100% RAM
• minimal re-configuration required
• Ex.: 4 TB  5 TB capacity increase (ignoring master)
Option 2 (“advanced”):
• Assign an existing node as “extension node”.
• Capacity of extension node: data <= 200% RAM
• Re-configuration on storage&I/O level may be necessary
(HW partner dependent)
• Ex.: 4 TB  7 TB capacity increase (ignoring master)
Option 3 (“special”): *
• Add a special Extension Node with specific I/O,
Storage, RAM setup – HW partner offerings differ
• Own Standby for Extension node required
• model “warm data & re-distribute data
• Ex.: 3 TB  4 + 2xn TB capacity increase
Note: The examples show only one extension node, but the same is possible for more than one node. But keep in mind that only data
can be classified and sized as “warm” that fulfils the BW restrictions. Data distribution of more then ~50% in “warm” are not realistic!
HANA – with extension node “simple” – example 2 TB hosts
Master Slave
1
Slave
2
Slave
3
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
Standby
node
2 TB RAM
Standby
node
2 TB RAM
Extension
node
2 TB RAM
max 2 TB
data
HANA – with extension node “advanced” – example 2 TB hosts
Master Slave
1
Slave
2
Slave
3
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
Standby
node
2 TB RAM
Standby
node
2 TB RAM
Extension
node
2 TB RAM
max 4 TB
data
HANA – with extension node “special” – example 2 TB hosts
Master Slave
1
Slave
2
Slave
3
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
2 TB RAM
1 TB data
Extension
node
n TB RAM
max 2 x n
TB data
Standby
Extension
n TB RAM
2 TB RAM
Standby
Hot
*
Planned
offering
Open
for
customers

Public
What technology to choose for SAP BW on HANA?
Generic option for BW and HANA native
 Use for PSA and corporate memory in BW
 Use for “warm” data in native HANA projects
 Use in “mixed” BW + native scenarios
 Requires understanding of different data
processing characteristics
Inhomogeneous technology
 Adding originally non-HANA technology
 Gaps to close in fundamental capabilities
 Evaluate if current offering can satisfy your needs
 Does not require HANA-certified hardware
Extension node option
Dedicated option for BW on HANA only
 Use for PSA, corporate memory and “warm”
advanced DSOs  potentially larger footprint
impact
 Very good understanding of data usage required –
do not use outside of BW
 No plans to make this available outside of BW
All HANA technology
 Full set of HANA system aspects available
 Requires HANA certified hardware
Both can be complemented with BW NLS

Public
HANA Native usage – data modeling
Switch to all-calculation-view modeling approach
Extended tables are not allowed in
all kinds of views
Only Calculation Views
 Cannot add extended table to Analytic or
Attribute Views
 Changing table in such a view to
extended table invalidates the view
Migration to Calculation Views
 Wizard exists to convert existing data
models to calculation views
Typically require manual data aging
 Split table into in-memory and extended
 Support by DLM tool (next slide)
 Replace original table with union calc
view (Replace with Data Source…)
Classical model:
Calculation, Analytic
and Attribute views All Calculation Views
T1 T1_EXT
Split and Union
generation via
DLM tool
T1
All in-memory In-memory and extended
Views
Tables
Managing Data Temperature with
SAP HANA Dynamic Tiering
i
DMM266 (HO2)

Public
SAP Data Warehousing Foundation
Data Lifecycle Manager (DLM)
Define a data temperature management strategy with DLM – available with DWF 1.0 SP01
Leverage SAP HANA tables, SAP HANA Dynamic Tiering (Warm-Store), Hadoop or SAP Sybase IQ in SAP
HANA native use cases with a tool based approach to model aging rules on tables to displace ‘aged’ data to
optimize the memory footprint of data in SAP HANA.
SAP HANA
Data Lifecycle Manager
HOT-STORE
(Column Table)
WARM-STORE
(Extended Table)
DATA
MOVEMENT
Generated SAP HANA View (Pruning / UNION)

Public
SAP Data Warehousing Foundation
Data Lifecycle Manager (DLM) – Data Displacement
Orchestrate and optimize the HANA memory
footprint of data in SAP HANA tables
 Data Modification on primary Application table (e.g. Hot-
Store) – only on records in specific “current” / open
periods
 ‘Aged’ Data within “closed” periods to be archived /
displaced to another Storage Destination
 Define Data Movement rules to displace data between
HANA-, Extended-, Hadoop- or SAP Sybase IQ-tables –
in and out
 Data Movement rules generated into HANA Stored
Procedures to perform mass data movement
 Execution of HANA Stored Procedures using HANA tasks
(manual and scheduled execution)
 Selective data deletion for proper housekeeping with DLM

Public
PLANNED INNOVATIONS Future Direction
Today
SAP HANA dynamic tiering SPS 12
Roadmap SAP HANA dynamic tiering
Summary
Technical integration
Common installation and administration tooling
Support for phased updates
Enterprise DB features
Consistent file-based database backup
Point-in-time recovery
Supporting Intel and IBM Power platforms
Functional integration and usage
New catalog object type “extended table”
Transparent DDL/DML support
Use extended tables in SQL queries, views and
calculation views
Deep integration with HANA data lifecycle manager
Support for dynamic tiering in HANA-related data
provisioning techniques
Co-deployment of HANA+DT on single host
Delta backups for dynamic tiering
Basic HANA system replication for dynamic tiering
Data volume encryption for dynamic tiering
Functional extensions
Multistore table partitioning across in-memory and
disk-store
Optimized partition-based data movement
Extended support for MDC setups
Support for storage snapshots
Improved 3rd party backup tool support
Extended support in dynamic tiering for
capabilities of HANA System Replication
Further encryption features for dynamic tiering
Functional extensions
Optimized data lifecycle management with
multistore partitioned tables in dynamic tiering
Atomic DDL for support of extended tables and
multistore tables in HANA CDS

Public
Demo
Multistore partitioning with SAP HANA dynamic tiering

Public
Conclusion
The path to successful data management in HANA-based applications
Minimize data in HANA Scale HANA memory
Reduce memory footprint
SAP HANA
(memory store)
SAP HANA
(memory store)
Archive
/dev/null
SAP HANA
(memory store)
Data aging
Dynamic Tiering /
Extension nodes
SAP S/4HANA
DWH
• Extension nodes only for
SAP BW
• Dynamic Tiering also for
native DWH
Scale-
Up
SAP HANA
(memory store)
SAP HANA
(memory store)
SAP HANA
(memory store)
Scale-Out
Find information on certified HANA hardware
setups at HANA hardware directory:
https://global.sap.com/community/ebook/201
4-09-02-hana-hardware/enEN/index.html
i
Housekeeping
Available choices
also depend on
application

Public
More information regarding data management based on SAP HANA
Public Documentation:
http://help.sap.com/hana_platform
 SAP HANA Dynamic Tiering: http://help.sap.com/hana_options_dt
 SAP HANA Data Warehousing Foundation: http://help.sap.com/hana_options_dwf
Documentation on data aging in SAP S/4HANA
Documentation on SAP HANA Vora
Community information
Documents on SCN or similar
 Best practice paper on dynamic tiering
 Quick Start Guide: https://scn.sap.com/docs/DOC-66016
 Blogs on extension node: (concept and details)
 Data Warehousing foundation on SCN: http://scn.sap.com/docs/DOC-62482
 ILM on SCN: http://scn.sap.com/community/information-lifecycle-management
Videos in the SAP HANA academy
 Dynamic Tiering: http://scn.sap.com/docs/DOC-59988
 SAP HANA Vora
 Data Warehousing Foundation

Public
SAP TechEd Online
Continue your SAP TechEd
education after the event!
Access replays of
 Keynotes
 Demo Jam
 SAP TechEd live interviews
 Select lecture sessions
 Hands-on sessions
 …

Public
Further information
Related SAP TechEd sessions:
ITM211 – Data Volume Management in the Context of SAP S/4HANA Conversions
DMM166 – Simple Steps for Aging Your Data in SAP S/4HANA
DMM210 – SAP HANA Data Warehousing: Data Lifecycle Management and Data Aging
DMM104 – SAP HANA Data Warehousing: Overview, Components, and Future Strategy
DMM266 – Managing Data Temperature with SAP HANA Dynamic Tiering
ITM214 – Data Center Architecture for SAP HANA (On Premise, Hybrid, Cloud)
Lecture
Hands-On Workshop
Lecture
Lecture
Hands-On Workshop
Lecture
SAP Public Web
https://scn.sap.com/docs/DOC-66016 – Best practice paper on SAP HANA dynamic tiering
http://scn.sap.com/community/information-lifecycle-management – SCN space for ILM
SAP Education and Certification Opportunities
www.sap.com/education:
BIT665 – Information Lifecycle Management (SAP NetWeaver ILM)
SAP training curricula: HANA – S/4HANA – BW
Watch SAP TechEd Online
www.sapteched.com/online

Public
Please complete your
session evaluation for
DMM205.
Contact information:
Richard Bremer
SAP HANA Platform Product Management
richard.bremer@sap.com
Feedback

Public
© 2016 SAP SE or an SAP affiliate company. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company.
SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate
company) in Germany and other countries. Please see http://www.sap.com/corporate-en/about/legal/copyright/index.html for additional trademark information and notices.
Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors.
National product specifications may vary.
These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its
affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and
services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as
constituting an additional warranty.
In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop
or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future
developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time
for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-
looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place
undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

DMM205.pdf

Recommended

Recommended

More Related Content

Similar to DMM205.pdf

Similar to DMM205.pdf (20)

Recently uploaded

Recently uploaded (20)

DMM205.pdf