sap real time data platform
DESCRIPTION
Identifies the driving needs for RTDP; shows what a typical complex integration scheme might look like; discusses guiding principals for non-disruptive migrations; and provides the roadmap for RTDPTRANSCRIPT
SAP Real-time Data Platform October 2012
© 2012 SAP AG. All rights reserved. 2
The Big Challenge Big Opportunity
BUSINESS NEEDS
Emergence of Social
Business, Geospatial data,
and Event Data
Multi-Channel Experience
(Mobile Experience)
Quick insights and right
action from huge amount of Data
Trust in Quality & Governance of Information
DATA CHARACTERISTICS
Exploding data volumes
Increasing data variety and sources including social,
machine generated
Accelerated data
processing velocity
TECHNOLOGY TRENDS
Storage / Memory / CPU
advances
EDW / Distributed MPP /
Hadoop
Data Mining/Predictive analysis
In-memory computing
Real-time Data Access and Event Management
© 2012 SAP AG. All rights reserved. 3
Current enterprise dilemma Can’t achieve goals due to complex and siloed IT environment
DM
DB
CRM
DB
EDW
DB
DM
DB
DM
DB
DM
DB
ERP Planning
DB
EDW DM
DM
DB
DM
DB DB
DM
DB DB
DB
EDW
DB
DB
Other Apps
DB
EDW
© 2012 SAP AG. All rights reserved. 4
New Initiatives Placing Great Stress on Infrastructure
Social Analytics Mobile Big Data Cloud
ERP Other Apps EDW
DB
EDW DM DM
DB
DM
DB
DM
DB DB DB
DM
DB DB
DM
DB
DB
DM
DB
DM
DB
EDW
DB
CRM
DB
EDW
DB
Planning
DB DB
© 2012 SAP AG. All rights reserved. 5
Popular Solution: Make all your systems talk to each other But what if you want to add a new system?
Social Analytics Mobile Big Data Cloud
ERP Other Apps EDW
DB
EDW DM DM
DB
DM
DB
DM
DB DB DB
DM
DB DB
DM
DB
DB
DM
DB
DM
DB
EDW
DB
CRM
DB
EDW
DB
Planning
DB DB
© 2012 SAP AG. All rights reserved. 6
Introducing the SAP Real-time Data Platform Transforming enterprises for the 21st century
Freedom to Innovate:
Existing apps rejuvenated
SAP Real-time Data Platform Extreme Data Management Capabilities to
transact | move | store | process | analyze | deliver
ERP Custom
Apps Planning Analytics
Big
Data EDW EDW EDW/
DM
Cloud SCM Mobile
New experiences for end users New apps unleashed
© 2012 SAP AG. All rights reserved. 7
Next generation SAP Real-time Data Platform Vision
Information Management & Real-Time Data Movement
Transactional
Data
Management
In-Memory
Data
Management
Analytics
EDW Data
Management
Mobile Data
Management
Com
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
Open APIs and Protocols
SAP Real Time Data Platform
Federated Access
Business
Warehouse
Business
Intelligence Mobile Applications ERP
© 2012 SAP AG. All rights reserved. 8
Guiding principle Non-Disruptive Migrations
SAP HANA
SAP Real Time Data Platform
SAP Sybase IQ
SAP Sybase ASE
SAP Sybase SA
Non-disruptive migration process: Key features
► Bidirectional data replication: A SAP HANA version of an application can coexist with a version
running on another RTDP server.
► Complete and accurate information: Unified information management to model, integrate,
cleanse, monitor, and govern a variety of data
► Common standards: Including guarantees such as high availability and disaster recovery as
well as programming APIs, make development of new applications simpler.
► Common tooling: Provides a consistent experience to IT staff handling migration.
► Near zero down time: Tools, Methodologies and compatibility layers(s).
Note: SAP Sybase ASE, SAP Sybase IQ and SAP Sybase SA will continue to be developed and supported
© 2012 SAP AG. All rights reserved. 9
Application transparency
for OLTP + analytics
Data movement choices for
tying applications together
Embedded data integration &
quality management within
in-memory database
Common tools for modeling
and monitoring
Complete platform extended
to cloud deployment
Seamless data movement
between on-premise and
cloud
Seamless information
management on-premise and
cloud
Modeling and monitoring
extended to cloud
OLTP, analytics, and streaming
incorporated in single platform
Orchestrated data movement
across the platform
Complete information
management capabilities in
RTDP
Unified modeling and
monitoring across the platform
RTDP: 2013
RTDP
SAP real-time data platform Roadmap
RTDP
Integrate
Optimize
Synthesize
© 2012 SAP AG. All rights reserved. 10
INTEGRATE: SAP ERP on RTDP C
om
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
SAP Real Time Data Platform
Business
Warehouse
Business
Intelligence Mobile Applications SAP ERP
Information Management & Real-Time Data Movement
Open APIs and Protocols
Transactional
Data
Management
In-Memory
Data
Management
Analytics
EDW Data
Management
Mobile Data
Management
Federated Access
● ERP on SAP Sybase ASE and SAP HANA
© 2012 SAP AG. All rights reserved. 11
INTEGRATE: Real-Time Streaming Analytics on SAP HANA
● Sense and react to real world events in real time
● Stream Analytics meets Operational Analytics offering extended insight
Com
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
Open APIs and Protocols
SAP Real Time Data Platform
Business
Warehouse Business Intelligence Mobile Applications ERP
Information Management &
Real-Time Data Movement Complex Event Processing
Transactional
Data
Management
In-Memory
Data
Management
Analytics
EDW Data
Management
Mobile Data
Management
Federated Access
© 2012 SAP AG. All rights reserved. 12
INTEGRATE: Real-time Replication
● Real-time replication for non-SAP scenarios from systems built on SAP Sybase ASE and
Oracle Databases.
● Operational Business Intelligence substantially improved through multi-source data input
and analytics
Com
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
Open APIs and Protocols
SAP Real Time Data Platform
Information Management &
Real-Time Data Movement
Business
Warehouse
Business
Intelligence Mobile Applications ERP
Replication
Transactional
Data
Management
In-Memory
Data
Management
Analytics EDW
Data
Management
Mobile Data
Management
Federated Access
© 2012 SAP AG. All rights reserved. 13
INTEGRATE: Master Data Governance & High Volume
Master Data Management with SAP HANA
Com
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
Open APIs and Protocols
SAP Real Time Data Platform
Information Management &
Real-Time Data Movement
Business
Warehouse Business Intelligence Mobile Applications ERP
● Master data governance at creation with SAP applications running on SAP HANA
● Single view of high-volume master data (ex. customer) across the enterprise across
hierarchies and relationships
Data Integration &
Data Quality Mgmt
Master Data
Management
Open APIs and Protocols
Transactional
Data
Management
In-Memory
Data
Management
Analytics
EDW Data
Management
Mobile Data
Management
Federated Access
© 2012 SAP AG. All rights reserved. 14
INTEGRATE: Common Information Modeling
● Information architecture modeling, metadata management, and data profiling for single
view on the enterprise information
● Link business & IT with easy-to-use UIs for modeling, managing, and monitoring
information
Information Management &
Real-Time Data Movement Com
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
Open APIs and Protocols
SAP Real Time Data Platform
Business
Warehouse
Business
Intelligence Mobile Applications ERP
Metadata Mgmt
and Profiling
Transactional
Data
Management
In-Memory
Data
Management
Analytics EDW
Data
Management
Mobile Data
Management
Federated Access
© 2012 SAP AG. All rights reserved. 15
INTEGRATE: Common Monitoring
● Progressively integrated system administration infrastructure for all engines in the
platform
● Evolution of appliance form factors to bring in a more embracing eco-system strategy on
shared responsibility on servers and storage
Com
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
SAP Real Time Data Platform
Business
Warehouse
Business
Intelligence Mobile Applications ERP
Information Management & Real-Time Data Movement
Transactional
Data
Management
In-Memory
Data
Management
Analytics EDW
Data
Management
Mobile Data
Management
Federated Access
© 2012 SAP AG. All rights reserved. 16
INTEGRATE: Big Data Performance
● Enhanced performance and expanded support of big data sources
● Accelerated sentiment intelligence and text mining across all relevant data sources with a
high-level of quality
Com
mon D
esig
n &
Modelli
ng
Envir
onm
ent
Com
mon
La
nd
sca
pe
Enviro
nm
ent
Open APIs and Protocols
SAP Real Time Data Platform
Information Management &
Real-Time Data Movement
Business Warehouse Business Intelligence Mobile Applications ERP
Hadoop ETL &
Hadoop Integration
Transactional
Data
Management
In-Memory
Data
Management
Analytics EDW
Data
Management
Mobile Data
Management
Federated Access Query & Data Federation
Text Processing
© 2012 SAP AG. All rights reserved. 17
Summary
1 SAP has a comprehensive data management vision and strategy to address your 21st century business issues
2 SAP is delivering on the Real-time Data Platform for next generation customer applications
3 SAP and Sybase customers have a clear path to reduced complexity and non-disruptive innovation
© 2012 SAP AG. All rights reserved. 18
An Example for Energy Efficiency
ESP (Event Stream
Processor)
Oracle
Oracle
SOI2
server
RS (Replication
Server)
HANA 1.0
RA#1
RA#2
Up to 2.5 billions of
events per day
SOI2
input
adapter
Filtering
Recoding
Imposition
Analytical reporting
on the fly Event Driven
Analytics
Replication flow
Event flow SCADA SCADA SCADA
Real-Time
analytical
calculation
RS
input
adapter
HANA
output
adapter
Real-time
data stream:
consumption of fuel,
energy consumption.
© 2012 SAP AG. All rights reserved. 19
Event Stream
Processor
Replication Environment: some technical details
Oracle
Oracle
Replication
Server
15.7.1
RA
#1
15.7
.1
RA#
2
15.7
.1
Replication flow
ORACLE 10.2.0.5
AIX x64 System:
ORACLE 11.2.0.2
SLES 11SP2, x64
RAM 16GB, 4 CORE
Tables:
1. ZIG_BOIL_ST
2. ZIG_BOILER
3. ETTIFN
4. EVBS
5. EHAUISU
6. EANL
7. ZSET_GAS_IMPOR
T
RS
input
adapter
Tables:
1. HISTORY_ 2. UNITS_
3. INV_ 4. INV_TYPE_
5. INV_TYPE_CLASS_ 6.
DIC_VALUE_
7. GROUP_VALUE_ 8. PARAM_
9. UNIT_CLASS_ 10. UNI_
11. DEF_ 12.
ATTR_DEF_
13. DEF_CLASS_
Backup stream: consumption of fuel,
energy consumption..
billing and
planning data
© 2012 SAP AG. All rights reserved. 20
Event Stream Processor: some technical details
ESP 5.1 SP1
Oracle
RA#
2
15.7
.1
SOI2
input
adapter
Replication flow
Event flow AIX x64
RS
input
adapter
SLES 11SP2, x64 RAM 8GB, 4 CORE
HANA
output
adapter
HANA 1.0
Filtering
Recoding
Real-Time
analytical
calculation
Up to 2.5 billions of
events per day
Millions of events
per day
System: ORACLE 11.2.0.2
© 2012 SAP AG. All rights reserved. 21
Analytical Content Full power by the core engines and L
Standard core
engines used
for standard
analytics
Complex
algorithms
require L
coding
OLAP
Engine
Calculation
Engine
Data foundation
L - Engine
Power
Designer
<deploy>
<maintain>
256 GB and 32 cores HANA node with
140 Mio Events (Records in the fact table) for the last 6 month and all report queries run constantly within 1.5 seconds
© 2012 SAP AG. All rights reserved. 22
ESP
(Event Stream Processor)
ESP
(Event Stream Processor)
SRS
(Replication
Server)
Next level SAP Real-time Data Platform allows to combine
operational, commercial and planning data
ESP
(Event Stream Processor)
Production / Automation
Systems
MES
ERP
DCS/SCADA DCS/SCADA
ECC/ERP
RS
(Replication
Server)
HANA++
Top Level MES
Database
input
adapter
input
adapter
DCS/SCADA
input
adapter
output
adapter
output
adapter
Analytics
BOBJ
Analytics & Reporting
Millions of
events per
minute
Transactio
n
replication
Transactio
nal
replication
Transactio
nal
replication
Transactio
nal
replication
Event
replication
Event
replication
Integration
of analytics
Top level
MES
IQ HANA
Disclaimer: HANA++ internal name