SAP HANA 介紹.ppt

1、Introduction to HANA,Core Team: xxx,In-Memory Computing,Technology that allows the processing of massive quantities of real time data in the main memory of the server to provide immediate results from analyses and transactions,Increasing Data Volumes,Calculation Speed,Type and # of Data Sources,Lack

2、 of business transparency Sales & Operations Planning based on subsets of highly aggregated information, being several days or weeks outdated.,Reactive business model Missed opportunities and competitive disadvantage due to lack of speed and agility Utilities: daily- or hour-based billing and consum

3、ption analysis/simulation.,Vision: In-Memory Computing Technology Constrained Business Outcome,Sub-optimal execution speed Lack of responsiveness due to data latency and deployment bottlenecks Inability to update demand plan with greater than monthly frequency,Information Latency,TeraBytes of Data I

4、n-Memory,100 GB/s data througput,Real Time,Freedom from the data source,Improve Business Performance IT rapidly delivering flexible solutions enabling business Speed up billing and reconciliation cycles for complex goods manufacturers Planning and simulation on the fly based on actual non-aggregated

5、 data,Competitive AdvantageE.g. Utilities Industry: Sales growth and market advantage from demand/cost driven pricing that optimizes multiple variables consumption data, hourly energy price, weather forecast, etc.,Vision: In-Memory Computing Leapfrogging Current Technology Constraints,Flexible Real

6、Time Analytics Real-time customer profitability Effective marketing campaign spend based on large-volume data analysis,In-Memory Computing The Time is NOWOrchestrating Technology Innovations,HW Technology Innovations,64bit address space 2TB in current servers 100GB/s data throughput Dramatic decline

7、 in price/performance,Multi-Core Architecture (8 x 8core CPU per blade) Massive parallel scaling with many blades,Row and Column Store,Compression,Partitioning,No Aggregate Tables,Real-Time Data Capture Insert Only on Delta,The elements of In-Memory computing are not new. However, dramatically impro

8、ved hardware economics and technology innovations in software has now made it possible for SAP to deliver on its vision of the Real-Time Enterprise with In-Memory business applications,SAP SW Technology Innovations,SAP Strategy for In-Memory,EXPAND PARTNER ECOSYSTEM Partner-built applications, Hardw

9、are partners,CUSTOMER CO-INNOVATION Design with customers,TECHNOLOGY INNOVATION BUSINESS VALUE Real-Time Analytics, Process Innovation, Lower TCO,GUIDING PRINCIPLES,INNOVATION WITHOUT DISRUPTION New Capabilities For Current Landscape,HEART OF FUTURE APPLICATIONS Packaged Business Solutions for Indus

10、try and Line of Business,In-Memory Computing Product “SAP HANA”SAP High Performance Analytic Appliance,What is SAP HANA? SAP HANA is a preconfigured out of the box Appliance In-Memory software bundled with hardware delivered from the hardware partner (HP, IBM, CISCO, Fujitsu) In-Memory Computing Eng

11、ine Tools for data modeling, data and life cycle management, security, operations, etc. Real-time Data replication via Sybase Replication Server Support for multiple interfaces Content packages (Extractors and Data Models) introduced over time Capabilities Enabled Analyze information in real-time at

12、 unprecedented speeds on large volumes of non-aggregated data. Create flexible analytic models based on real-time and historic business data Foundation for new category of applications (e.g., planning, simulation) to significantly outperform current applications in category Minimizes data duplicatio

13、n,SAP HANA,SAPBusiness Suite,SAP BW,3rd Party,replicate,ETL,SAP HANAmodeling,BI Clients,SQL,MDX,BICS,In-Memory,3rd Party,Technical Overview,Calculation models Extreme Performance and Flexibility with Calculations on the fly,Calculation Model A calc model can be generated on the fly based on input sc

14、ript or SQL/MDX A calc model can also define a parameterized calculation schema for highly optimized reuse A calc model supports scripted operations,Data Storage Row Store - Metadata Column Store 10-20 x Data Compression, SAP 2007/Page 9,SAP BusinessObjects Data Services Platform,Integrate heterogen

15、eous data into BWA,Extract From Any Data Source into HANA Syndicate From HANA to Any Consumer,Integrated Data Quality Text Analytics,Rich Transforms,SAP HANA Road Map:In-Memory Introduction,Todays System Landscape ERP System running on traditional database BW running on traditional database Data ext

16、racted from ERP and loaded into BW BWA accelerates analytic models Analytic data consumed in BI or pulled to data marts,Step 1 In-Memory in parallel(Q4 2010) Operational data in traditional database is replicated intomemory for operational reporting Analytic models from production EDW can be brought

17、 into memory for agile modeling and reporting Third party data (POS, CDR etc) can be brought into memory for agile modeling and reporting,Step 3 New Applications (Planned for Q3 2011) New applications extend the core business suite with new capabilities New applications delegate data intense operati

18、ons entirely to the in-memory computing Operational data from new applications is immediately accessible for analytics real real time,Step 2 Primary Data Store for BW(Planned for Q3 2011) In-Memory Computing used as primary persistence for BW BW manages the analytic metadata and the EDW data provisi

19、oning processes Detailed operational data replicated from applications is the basis for all processes SAP HANA 1.5 will be able to provide the functionality of BWA,SAP HANA Road Map: Renovation of DW and Innovation of Applications,Step 5 Platform Consolidation All applications (ERP and BW) run on da

20、ta residing in-memory Analytics and operations work on data in real time In-memory computing executes all transactions, transformations, and complex data processing,Step 4 Real Time Data Feed(2012/2013) Applications write data simultaneously to traditional databases as well as the in-memory computin

21、g,SAP HANA Road Map: Transformation of application platforms,Real Time Enterprise: Value PropositionAddressing Key Business Drivers,Real-Time Decision Making Fast and easy creation of ad-hoc views on business Access to real time analysis Accelerate Business Performance Increase speed of transactiona

22、l information flow in areas such as planning, forecasting, pricing, offers Unlock New Insights Remove constraints for analyzing large data volumes - trends, data mining, predictive analytics etc. Structured and unstructured data Improve Business Productivity Business designed and owned analytical mo

23、dels Business self-service reduce reliance on IT Use data from anywhere Improve IT efficiency Manage growing data volume and complexity efficiently Lower landscape costs,There is a significant interest from business to get agile analytic solutions. In a down economy, companies focus on cash protecti

24、on. The decision on what needs to be done to make procurement more efficient is being made in the procurement department“. CEO of a multinational transportation company,Flexibility to analyse business missed by LoB. First performance, and the other is flexibility on a business analyst level, who nee

25、d to do deep diving to better understand and conclude. The second would be that also front-end tools are not providing flexibility“. Executive of a global retail company,Traditional data warehouse processes are too complex and consume too much time for business departments. The companies were frustr

26、ated with usual problems difficulty to build new information views. These companies were willing to move data into another proprietary file format . “ Analyst,Real Time Enterprise: Value Proposition,The Value Blocks,Run performance-critical applications in-memory Combine analytical and transactional

27、 applications No need for planning levels or aggregation levels Multi-dimensional simulation models updated in one step Internal and external data securely combined Batch data loads eliminated,Eliminate BW database Empower business self-service analytics reduce shadow IT Consolidate data warehouses

28、and data marts In-memory business applications (eliminate database for transactional systems),Lower infrastructure costs server, storage, database Lower labor costs backup/restore, reporting, performance tuning,Value Elements,In-Memory Enablers,Sense and respond faster Apply analytics to internal an

29、d external data in real-time to trigger actions (e.g., market analytics) Business-driven “What-If” Ask ad-hoc questions against the data set without IT Right information at the right time,New business models based on real-time information and execution Improved business agility Dramatically improve planning, forecasting, price optimization and other processes New business opportunities