SAP HANA Update. Saul Cunningham SAP Big Data Centre of Excellence

Transcription

1 SAP HANA Update Saul Cunningham SAP Big Data Centre of Excellence

2 The first 35 years: innovated with ERP & LOB apps Data In ERP + LOB Systems of Record

3 Five years ago: innovated with analytics Data In ERP + LOB Business Analytics Info Out Systems of Record Systems of Engagement

4 Two years ago: innovated with mobility Mobility Accessible Systems Data In ERP + LOB Business Analytics Info Out Systems of Record Systems of Engagement

5 Now: innovating with the database Mobility Accessible Systems Data In ERP + LOB Systems of Record Business Analytics Systems of Engagement Info Out Business Applications Performance Bound by Data SQL Oracle DB2 SQL Other DB2, etc.

6 HANA accelerates data, apps and analytics Mobility Accessible Systems Data In ERP + LOB Systems of Record Business Analytics Systems of Engagement Info Out Oracle ELT or ETL HANA In Memory Database ELT or ETL SQL Other DB2, etc.

7 Long term: HANA is the database Mobility Accessible Systems Data In ERP + LOB Business Analytics Info Out Systems of Record Systems of Engagement HANA In Memory Database

8 HANA = HAsso s New Architecure A common Database Approach for OLTP and OLAP Using an In-Memory Column Database Hasso Plattner VS Transact Analyze Accelerate Transactions + Analysis directly in-memory

9 Leverage new innovations HW technology innovations Multicore architecture (8 x 8 core CPU per blade) Massive parallel scaling with many blades One blade ~$50,000 = 1 enterprise class server 64 bit address space 2 TB in current servers 100 GB/s data throughput Dramatic decline in price/performance SAP SW technology innovations Row and column store Compression Partitioning No aggregate tables Insert only on delta 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

10 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

11 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 SCALE TO MEET YOUR NEEDS

12 Grow incrementally Start small Grow as required Scale Up or Scale Out Data Centre Ready Cloud Ready

13 Data Centre Ready Fault tolerance Disaster Recovery High Availability In machine persistence Storage replication System replication Auto-services restart Auto-node failover

14 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

15 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

16 Breakthrough Transactional Performance Insert: 1.5M Records/sec Column store as transaction database Foundation for SAP Business Suite ROW BUFFER COLUMN BUFFER COLUMN STORE Single copy of the data Real real-time!

17 Maximising the Boundaries of all 5 Dimensions of Data... Going Deep And Broad In Real-time ASAP With Agility { complex questions, interactive questions, multi-dimensional { large amounts of data, many types of data, all necessary data { recent data, real-time data { fast response, interaction within the window of opportunity { no data prep, no-pre-aggregates, no-tuning

18 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

19 Fastest growing product in SAP history 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

20 TCO lowered

21 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

22 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

23 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

24 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

25 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

26 SAP Real Time Data Platform The SAP HANA real-time data platform is for next generation applications & businesses Analytical Data Warehouses Transactional Speed is key Develop Integrate Analyze Decide

27 Case Study Mitsui Knowledge Industry 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

28 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

29 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.)

30 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.

31 More information

32 Saul Cunningham Big Data Centre of Excellence SAP Australia Tel:

33

34 Backup slides

35 HANA HA-API supports the remount process and tells OS/hardware partner tools to offer the right file systems at the right node. HANA High Availability Disaster Tolerance Cluster across Data Centers incl. mount management

36 HA & DR Details Solution Supported Downtime Scenarios Detect RPO RTO Ramp Failback System Replication Storage Replication Persistence Backups Host Auto Failover Service Auto Restart Business continuity for planned downtime, software fault, host crash or disaster Business resumption when disaster; recovery from local storage corruption Restart from software fault, power outage or host crash Recovery from storage or data corruption, operator error, host crash, or disaster Automatic recovery from software fault or host crash Manual mins seconds Manual Manual 0 hours-days mins-hours Manual Auto 0 mins mins-hours Auto Manual large hours-days mins-hours Auto Auto 0 ~20 mins mins-hours Auto Automatic recovery from software fault Auto 0 ~5 mins mins-hours Auto