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HANA – the backbone and engine
for S/4HANA
Chris Kernaghan
Principal Consultant
2
HANA History
3
SAP HANA SAP BW
Powered
by SAP
HANA
Business
Suite
powered
by SAP
HANA
Simple
Finance
powered
by SAP
HANA
SAP S/4
HANA
2011 2012 2013 2014 2015
• In Memory
platform
• Real time
analysis
• Real time
reporting
• Real time
Business
• OLAP & OLTP
together
• SAP Hana
Enterprise Cloud
• Instant financial
insight
• No aggregates
• Single Source of
truth
• Simplified data model
• New user experience
• Advanced processing
• Choice of deployment
HANA Release strategy
SAP HANA SP XX . XX . XX
Service Packs
• Released every 6 months from a single delivery stream.
• Cumulative and downwards compatible
• Linked with Client tools
Revisions
• Released on demand
• Sometimes linked to client tools – check release notes
Data Centre Release
• Released every 6 months, after being run on Production systems for 2+ weeks
• Recommended for Production systems between
Maintenance Revisions
• Used to keep customers on lower support packages to get defined subset of
fixes
• Only valid for 3 months
4
Revision Maintenance Revision
SAP HANA within the stack
5
Example S/4 HANA Landscape
6
SuccessFactors
Ariba
SAP
Hana
Cloud
Platform
S4 Landscape Customer
DMZ
HANA
DB/CI
Application
Server(s)
Gateway/Fiori
Server
SLT
Server
PI
Server
User
Internal Network
Other SAP
systems
Non-SAP
systems
SAP
Cloud Connector
SAP Data
Services
B.Obj
App tier
B.Obj
Database
• Multi-tenant Database Containers
• Improving deployment options (MCOD, MCOS, MCD)
• DB Control Centre
• Ability to manage multiple databases from a single
console
• Dynamic Data tiering
• Data Lifecycle Manager using temperature control for
data to provide better TCO
• Big Data integration
• Integration with Hadoop and Spark
• TDI
• Hardware vendors leveraging existing infrastructure
• Scale up or Scale out
SAP HANA Evolution SP10 + - Infrastructure
7
SAP HANA Evolution SP10 - Infrastructure
8
SAP HANA Evolution SP10 + - Developer
9
• Web IDE platform
• No need to deploy HANA Studio to desktops
• Better code checking and performance tools
• Expensive SQL trace
• Performance trace
• Memory consumption reports
• HCP Extensions
• HANA as the underlying data platform for HCP and S4
• Other HANA capabilities
• IOT
• Streaming
SAP HANA Evolution SP10 + - Functionality
10
• Fiori Screens
• Many administration screens replaced with Fiori apps – eg DB Cockpit
• Enhanced HA/DR capabilities
• Better tools for administrating replication and restarts
• Backup and Restore
• Incremental backups and Differential backups
• Continuous log replay (Future)
• Workload management
• Ability to use mixed workloads by creating workload mapping
• NUMA Optimisations
• Cloud deployment
• Security enhancements for multi-tenant deployment
11
SAP HANA Evolution SP10 - Functionality
HANA - the backbone for S/4 HANA

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HANA - the backbone for S/4 HANA

  • 1. HANA – the backbone and engine for S/4HANA Chris Kernaghan Principal Consultant
  • 2. 2
  • 3. HANA History 3 SAP HANA SAP BW Powered by SAP HANA Business Suite powered by SAP HANA Simple Finance powered by SAP HANA SAP S/4 HANA 2011 2012 2013 2014 2015 • In Memory platform • Real time analysis • Real time reporting • Real time Business • OLAP & OLTP together • SAP Hana Enterprise Cloud • Instant financial insight • No aggregates • Single Source of truth • Simplified data model • New user experience • Advanced processing • Choice of deployment
  • 4. HANA Release strategy SAP HANA SP XX . XX . XX Service Packs • Released every 6 months from a single delivery stream. • Cumulative and downwards compatible • Linked with Client tools Revisions • Released on demand • Sometimes linked to client tools – check release notes Data Centre Release • Released every 6 months, after being run on Production systems for 2+ weeks • Recommended for Production systems between Maintenance Revisions • Used to keep customers on lower support packages to get defined subset of fixes • Only valid for 3 months 4 Revision Maintenance Revision
  • 5. SAP HANA within the stack 5
  • 6. Example S/4 HANA Landscape 6 SuccessFactors Ariba SAP Hana Cloud Platform S4 Landscape Customer DMZ HANA DB/CI Application Server(s) Gateway/Fiori Server SLT Server PI Server User Internal Network Other SAP systems Non-SAP systems SAP Cloud Connector SAP Data Services B.Obj App tier B.Obj Database
  • 7. • Multi-tenant Database Containers • Improving deployment options (MCOD, MCOS, MCD) • DB Control Centre • Ability to manage multiple databases from a single console • Dynamic Data tiering • Data Lifecycle Manager using temperature control for data to provide better TCO • Big Data integration • Integration with Hadoop and Spark • TDI • Hardware vendors leveraging existing infrastructure • Scale up or Scale out SAP HANA Evolution SP10 + - Infrastructure 7
  • 8. SAP HANA Evolution SP10 - Infrastructure 8
  • 9. SAP HANA Evolution SP10 + - Developer 9 • Web IDE platform • No need to deploy HANA Studio to desktops • Better code checking and performance tools • Expensive SQL trace • Performance trace • Memory consumption reports • HCP Extensions • HANA as the underlying data platform for HCP and S4 • Other HANA capabilities • IOT • Streaming
  • 10. SAP HANA Evolution SP10 + - Functionality 10 • Fiori Screens • Many administration screens replaced with Fiori apps – eg DB Cockpit • Enhanced HA/DR capabilities • Better tools for administrating replication and restarts • Backup and Restore • Incremental backups and Differential backups • Continuous log replay (Future) • Workload management • Ability to use mixed workloads by creating workload mapping • NUMA Optimisations • Cloud deployment • Security enhancements for multi-tenant deployment
  • 11. 11 SAP HANA Evolution SP10 - Functionality

Editor's Notes

  1. SAP HANA SPS 10 introduces several key high availability and disaster recovery enhancements, such as 1-to-n asynchronous table replication, streamlined log based replication, enhanced auto-host failover for dynamic tiering, and new incremental and differential backup features.  NUMA awareness capabilities within SAP HANA can also be extended with architecture to improve in-memory processing of very large memory and very large numbers of CPU cores with a single node. The foundation is being laid to enable a benefit from 12TB+ with greater than 300+ CPU cores scale up configurations and process even larger data volumes in scale-up configurations and within very large nodes in scale-out configurations.