sap hana update - sap australian user group overview.pdf · sap hana update saul cunningham sap big...
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SAP HANA Update
Saul Cunningham SAP Big Data Centre of Excellence
ERP + LOB
Systems of Record
Data “In”
The first 35 years: innovated with ERP & LOB apps
ERP + LOB
Systems of Record
Data “In” Business
Analytics Systems of Engagement
Info “Out”
Five years ago: innovated with analytics
ERP + LOB
Systems of Record
Data “In” Business
Analytics Systems of Engagement
Info “Out”
Mobility Accessible Systems
Two years ago: innovated with mobility
ERP + LOB
Systems of Record
Data “In” Business
Analytics Systems of Engagement
Info “Out”
Mobility Accessible Systems
Oracle DB2 SQL Other
Business Applications Performance Bound by
Data
Oracle SQL
DB2, etc.
Now: innovating with the database
ERP + LOB
Systems of Record
Data “In” Business
Analytics Systems of Engagement
Info “Out”
Mobility Accessible Systems
Oracle Other ELT or ETL HANA
In Memory Database
ELT or ETL Oracle
SQL
DB2, etc.
HANA In Memory Database
HANA accelerates data, apps and analytics
ERP + LOB
Systems of Record
Data “In” Business
Analytics Systems of Engagement
Info “Out”
Mobility Accessible Systems
HANA In Memory Database
Long term: HANA is the database
A common Database Approach for OLTP and OLAP
Using an In-Memory Column Database Hasso Plattner
Transact Analyze Accelerate Transactions + Analysis
directly in-memory
VS
HANA = HAsso’s New Architecure
HW technology innovations
64 bit address space – 2 TB in
current servers
100 GB/s data throughput
Dramatic decline in
price/performance
Multicore architecture (8 x 8 core 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
SAP SW technology innovations
Convergence of improved hardware economics and technology innovations enables SAP to deliver on its
vision of the real-time enterprise with in-memory business applications
Leverage new innovations
HANA In-Memory Vision – realised to market in June 2011
Natively In-Memory | Massively Parallel | OLTP + OLAP | Structured + Unstructured | Legacy + New
3B Scans/s /core | 12.5M aggregates /s /core | 1.5M inserts/s
SCALE TO MEET YOUR NEEDS
Intel Optimised Hardware Applicance
Next Generation Open Appliance | Developed with Intel
IBM | HP | Cisco | VCE | Fujistu | Dell | Hitachi | Lenovo
Non-SAP & SAP systems | Supports Open Standards
Lower Power Usage | Lower TCO | Lower Failure Rate
Grow incrementally
Start small | Grow as required
Scale Up or Scale Out | Data Centre Ready
Cloud Ready
Data Centre Ready
Fault tolerance | Disaster Recovery | High Availability
In machine persistence| Storage replication | System replication
Auto-services restart| Auto-node failover
True real time analytics
No latency between transaction & analytics
Data logically modeled on the fly | No aggregations | No materialised views | No nightly batch jobs
Drastically simplifies data development landscape | Greater agility for the business
1 PB Performance Benchmark Largest in-memory system in the world
(see the 1PB benchmark whitepaper blog at saphana.com)
100 Nodes, 100 TB in DRAM | 10 Years of SD Data | 1.2 T Rows (330 Million transactions / day)
Ad-hoc Simple Queries (e.g. Month Report: 430ms – 647ms | Drill-down: 142ms Complex Queries (e.g. YoY report): 1.2s - 3.1s
Query Throughput (Queries per Hour): 7,547 for 1 stream | 57,202 for 10 streams | 112,602 for 60 streams
Breakthrough Transactional Performance
Insert: 1.5M Records/sec
COLUMN BUFFER ROW BUFFER COLUMN STORE Column store as transaction database | Foundation for
SAP Business Suite
Single copy of the data | Real real-time!
Maximising the Boundaries of all 5 Dimensions of Data...
Going Deep { complex questions, interactive questions, multi-dimensional
And Broad { large amounts of data, many types of data, all necessary data
In Real-time { recent data, real-time data
ASAP { fast response, interaction within the window of opportunity
With Agility { no data prep, no-pre-aggregates , no-tuning
Renewal Everywhere
2013 & Beyond Legacy + New – Without compromise
2012 HANA Platform (Developers, Startups, 10K/100K, New Frontiers) Business One, Planning, CRM & others on HANA
2011 HANA for Analytics (BOBJ, BW and other) HANA GA
2010 & Prior Designing & building HANA
1042 Customers | > 500 live | 19 in 10K Club
100K Club Winners
Yodabashi
NonFu Spring Co.
Mitsui Knowledge Co.
Club 10k
Essar
SAP IT
Cornell University
Charmer Sunbelt Beverages
Provimi
Emirates Airlines
Haier
Fastest growing product in SAP history
TCO lowered
Data Search Discovery & Analysis
Data Itself guides the process| triggers further questions
Big data | Search driven| Mashable
Next generation visualizations | Cloud | On-Premise
Discover | Search | Explore | Share
SAP Mobile Analytics
Targeted & Personalized | Always Accessible | Collaborative
Augmented experience | Geo Spatially Savvy | Real-time Platform & Targeted Apps
Leverage existing BI platform | Optimized for Big data
Author Once | Multiple Platforms |Securely Managed | Easily deployed
Big Queries, Big Data & Quality Data
Unstructured data frontier | Linking EDW & Hadoop is essential
Hadoop |Cost Efficient to Store & Process| Scalable
In-Memory computing | High Value Data | User Driven | Federate SAP HANA & Hadoop via SAP BI & EIM
Predictive Analysis & Big Data
Increased Business Interest | Increased Data Value |Increasing Technology Performance
Empower the Business |In-time insights | Context Driven
R-Integration | In-Built PAL in HANA|In-house developed algorithms
Next Generation Real-Time Applications
Real-time operational analytics | non-disruptive | Cloud or On-Premise
Applications Powered by HANA | Line of Business | Solutions for Industry (e.g. Telco, Utilities, Banking) |
Cross Industry
Platform for Enterprise | Central Master Data Hub | CRM 360 | Business Suite on HANA | Cloud or On-
Premise
The SAP HANA real-time data platform is for next generation applications & businesses
Analytical | Data Warehouses | Transactional
Speed is key | Develop| Integrate| Analyze | Decide
SAP Real Time Data Platform
Specialist in genome research
Want to enabled cancer treatment optimised to patients DNA
Research shows that your DNA anomalies you will respond to certain drugs and therapies better than
others
41% of us will be diagnosed with cancer at some point in our lives…
Case Study – Mitsui Knowledge Industry
Vision is possible as DNA sequencing costs have fallen dramatically
Soon cost will be under $1000 and be completed in less than a day
Challenge is now to reduce the time to analyze the genome sequence
Case Study – Mitsui Knowledge Industry
Data analysis for sequenced genome 3 step process taking 2-3 days | Aim to get as fast as
possible
Preprocess
- Alignment of patient’s DNA sequence with a “normal” sequence
Data analysis
- Complex comparison of patients DNA identified against 10’s of millions of preprocessed sequences to identify mutations/variants
Actions
- List of actionable mutated genes and related medicines
- Create predictive model (prognosis, driver mutation, etc.)
Case Study – Mitsui Knowledge Industry
Hadoop used to preprocess the DNA sequence
HANA used to analyze the genome sequence
R used to in predictive modeling of treatments
“Genomic DNA analysis in real-time will transform how we enable comprehensive patient care to fight against cancer. SAP HANA will be the mission
critical and reliable data platform to make real-time cancer analytics into a reality. Separately, our internal technical comparison demonstrated that SAP
HANA outperforms a traditional disk-based system by factor of 408,000 when performing other types of data analysis.”
Yukihisa Kato, Director & Executive Officer, CTO, Research and Development Center, MITSUI KNOWLEDGE INDUSTRY CO.,LTD.
“ ”
Case Study – Mitsui Knowledge Industry
More information
http://www.saphana.com/
http://www.sap.com/solutions/index.epx
https://experience.sap.com/
Saul Cunningham
Big Data Centre of Excellence
SAP Australia
Tel: +61 417 495 010
Email: [email protected]
Backup slides
HANA High Availability – Disaster Tolerance Cluster across Data Centers incl. mount management
HANA HA-API supports the remount process
and tells OS/hardware partner tools to offer the
right file systems at the right node.
HA & DR Details
Solution Supported Downtime Scenarios Detect RPO RTO Ramp Failback
System
Replication
Business continuity for planned downtime, software
fault, host crash or disaster Manual 0 1-2 mins seconds Manual
Storage
Replication
Business resumption when disaster;
recovery from local storage corruption Manual 0 hours-days mins-hours Manual
Persistence Restart from software fault, power outage
or host crash Auto 0 mins mins-hours Auto
Backups Recovery from storage or data corruption,
operator error, host crash, or disaster Manual large hours-days mins-hours Auto
Host Auto
Failover
Automatic recovery from software fault
or host crash Auto 0 ~20 mins mins-hours Auto
Service Auto
Restart Automatic recovery from software fault Auto 0 ~5 mins mins-hours Auto