sap hana|sap hana databasee| introduction to saphana


Presentation Description

SAP HANA, sap hana implementation scenarios, sap hana deployment scenarios, SAP HANA Implementations, sap hana implementation and modeling, sap hana implementation cost, sap hana implementation partners, Applications based on SAP HANA, SAP HANA Databases.


Presentation Transcript

PowerPoint Presentation:

Introduction to SAP HANA

In-Memory Computing :

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

PowerPoint Presentation:

Increasing Data Volumes Calculation Speed Type and # of Data Sources Lack 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transparency Sales & Operations Planning based on subsets of highly aggregated information, being several days or weeks outdated.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 consumption analysis/simulation. 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 Current Scenario Information Latency

PowerPoint Presentation:

TeraBytes of Data In-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 data Competitive Advantage E.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. In-Memory Computing Leapfrogging Current Technology Constraints Flexible Real Time Analytics Real-time customer profitability Effective marketing campaign spend based on large-volume data analysis Future State

In-Memory Computing – The Time is NOW Orchestrating Technology Innovations:

In-Memory Computing – The Time is NOW Orchestrating Technology Innovations HW Technology Innovations 64bit address space – 2TB in current servers 100GB/s data throughput Dramatic decline 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 improved 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:

SAP Strategy for In-Memory EXPAND PARTNER ECOSYSTEM Partner-built applications, Hardware 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 Industry and Line of Business

In-Memory Computing Product “SAP HANA” SAP High Performance Analytic Appliance:

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 Engine 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 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 duplication SAP HANA SAP Business Suite SAP BW 3rd Party replicate ETL SAP HANA modeling BI Clients SQL MDX BICS In-Memory 3rd Party

Technical Overview:

Technical Overview Calculation models – Extreme Performance and Flexibility with Calculations on the fly Calculation Engine Calculation Model Distributed Execution Engine Row Store Column Store SQL MDX SQL Script Plan Model other Compile & Optimize Physical Execution Plan Logical Execution Plan Parse In-Memory Computing Engine Calculation Model A calc model can be generated on the fly based on input script 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 – 10-20x Data Compression

SAP BusinessObjects Data Services Platform:

© SAP 2007/Page 9 SAP BusinessObjects Data Services Platform Integrate heterogeneous 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 :

SAP HANA Road Map: In-Memory Introduction Today‘s System Landscape ERP System running on traditional database BW running on traditional database Data extracted 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 into memory for operational reporting Analytic models from production EDW can be brought into memory for agile modeling and reporting Third party data (POS, CDR etc) can be brought into memory for agile modeling and reporting

SAP HANA Road Map: Renovation of DW and Innovation of Applications:

Step 3 – New Applications (Planned for Q3 2011) New applications extend the core business suite with new capabilities New applications delegate data intense operations 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 provisioning 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

PowerPoint Presentation:

Step 5 – Platform Consolidation All applications (ERP and BW) run on data 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 computing SAP HANA Road Map: Transformation of application platforms

Real Time Enterprise: Value Proposition Addressing Key Business Drivers :

Real Time Enterprise: Value Proposition Addressing 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 transactional 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 models 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 protection. 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 need 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 […] we re frustrated 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:

Real Time Enterprise: Value Proposition The Value Blocks Run performance-critical applications in-memory Combine analytical and transactional 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 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 and 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  f aster, more accurate business decisions based on complex, large data volumes High performance “real-time” analytics Support for trending, simulation (“what-if”) Business-driven data models Support for structured and un-structured data Analysis based on non-aggregated data sets

HANA Information Modeler:

HANA Information Modeler

HANA Information Modeler Creating Connectivity to a new system:

HANA Information Modeler Creating Connectivity to a new system

HANA Information Modeler Creating Attribute View:

HANA Information Modeler Creating Attribute View

HANA Information Modeler Defining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types):

HANA Information Modeler Defining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types)

HANA Information Modeler Data Preview:

HANA Information Modeler Data Preview

HANA Information Modeler Creating Hierarchies:

HANA Information Modeler Creating Hierarchies

HANA Information Modeler Creating Analytic View:

HANA Information Modeler Creating Analytic View

HANA Information Modeler Creating Analytic View:

HANA Information Modeler Creating Analytic View

PowerPoint Presentation:

THANK YOU Head Quarters: 9301 Southwest Freeway, Suite 475, Houston TX 77074 USA P: +1-832-849-1120 F: +1-832-849-1119 E: Offshore office: 3 rd Floor, RPAS Chambers, Begumpet, TS - 500016 India P: +91-40-64101333 F: +1-832-849-1119 E:

authorStream Live Help