Business users of all levels are empowered to conduct immediate ad hoc data analyses and transaction processing using massive amounts of real time data for expanded business insight. It frees up IT resources and lowers the cost of operations.
Defining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types) Right click Data Preview Right click Activate: This action will activate the Attribute View with selected fields as key figures and associated measures.
We can also view distinct values in each of these fields and perform a quick analysis (data disbursement in graphical format) Analyzing the data present in an attribute: (By selecting Dimensions, Measures and applying filters) Also, we can change the type of chart we want to use depending on the type of data.
The model of Attributes and Analytic View will appear as below after establishing the relationships: Activate the view by right clicking in the studio Now the Analytic View is ready to be accessed by the Explorer.
Sap hana Overview
SAP HANA Session 1Introduction
Problem Statement In an organization every year massive amounts of data is created and how fast your business reacts to important information determines whether you succeed or fail. This is a big problem and its getting bigger.In a Sloan Management survey in 201060% of executives said their companies Fewhave more data than they know how touse effectively. Facts IDC estimates that worldwide digitalWith data doubling every 18 months, content added up to 1 trillion gigabytes inthat percentage is going to keep growing. 2011. They predict this will double in 18According to EMC, by the end of 2011 months, and every 18 months thereafter.there was 1.8 Zeta byte of digital data. www.xpress-analytics.com Ph: 8775734486
Now exactly what is a Zeta Byte ? www.xpress-analytics.com Ph: 8775734486
Real Time Consumption of Data People want instant access to information – ‘in the moment’’ - whether that is a moment of risk or a moment of opportunity. If the moment has passed and your business has not taken the right action, it has failed. People want instant answers. They want them to be right. They want them anywhere, any time. www.xpress-analytics.com Ph: 8775734486
Agenda1. Introduction to HANA: Vision and Strategy2. Solution Overview & Roadmap3. Business Value4. HANA Modeling Studio5. Connecting from BOE6. Real time Examples www.xpress-analytics.com Ph: 8775734486
Solution – A Technology to process and analyze massive amounts of data in real time•In Memory Storage•Multi Core Architecture•Columnar Storage•Partitioning•Compression•Massive parallel processing www.xpress-analytics.com Ph: 8775734486
Vision: In-Memory ComputingTechnology Constrained Business Outcome Current Scenario Sub-optimal execution speed Lack of responsiveness due to data latency and deployment bottlenecks Inability to update demand plan with greater than monthly frequency Increasing Data Volumes Lack of business transparency Sales & Operations Planning based on Information subsets of highly aggregated information, Calculation Speed Latency being several days or weeks outdated. Type and # of Data Sources 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. www.xpress-analytics.com Ph: 8775734486
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 www.xpress-analytics.com Ph: 8775734486
Vision: In-Memory ComputingLeapfrogging Current Technology Constraints Future State Flexible Real Time Analytics Real-time customer profitability Effective marketing campaign spend based on large-volume data analysis TeraBytes of Data Improve Business Performance In-Memory IT rapidly delivering flexible solutions enabling business 100 GB/s data Speed up billing and reconciliation cycles Real Time througput for complex goods manufacturers Planning and simulation on the fly based on Freedom from the data source 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. www.xpress-analytics.com Ph: 8775734486
In-Memory Computing – The Time is NOW Orchestrating Technology Innovations The elements of In-Memory computing are not new. However, dramatically improved hardware economics and technologyinnovations in software has now made it possible for SAP to deliver on its vision of the Real-Time Enterprise with In-Memory business applications HW Technology Innovations SAP SW Technology Innovations Multi-Core Architecture (8 x 8core CPU Row and Column Store per blade) Massive parallel scaling with many blades Compression 64bit address space – 2TB in current servers Partitioning 100GB/s data throughput Dramatic decline in price/performance No Aggregate Tables Real-Time Data Capture Insert Only on Delta www.xpress-analytics.com Ph: 8775734486
Using main memory as the data storeThe most obvious reason to use main memory as the data store for a database isspeed of access The main memory (RAM) is the fastest storage type. Data in main memory can be accessed more than a 100,000 times faster than data on a spinning hard disk. flash technology storage is 1000 slower than main memory. Main memory is connected directly to the processors through a very high-speed bus, whereas hard disks are connected through a chain of buses (QPI, PCIe, SAN) and controllers (I/O hub, RAID controller or SAN adapter, and storage controller). www.xpress-analytics.com Ph: 8775734486
Minimizing data movementEven though today’s memory capacities allow keeping enormous amounts of datain-memory, compressing the data in-memory is still desirable. The goal is to compressdata in a way that does not use up performance gained, while still minimizing datamovement from RAM to the processor. www.xpress-analytics.com Ph: 8775734486
Columnar storageRelational databases organize data in tables, which contain the datarecords. The difference between row-based and columnar Row-based storage stores a table in a sequence of rows. Column-based storage stores a table in a sequence of columns. www.xpress-analytics.com Ph: 8775734486
Row or Column ?www.xpress-analytics.com Ph: 8775734486
Pushing application logic to the databaseAn application executing the applicationlogic on the data has to get the data fromthe database, process it, and possiblysend it back to the database to store theresults. Leads to network over heads andlatency How will it be to process the data where it is, at the database ??? www.xpress-analytics.com Ph: 8775734486
Data partitioning & Parallelizationon a 10-core processor the time neededis one-tenth of the timethat a single core would needservers available today can hold terabytes ofdata in memory and provide up toeight processors per server with up to 10 coresper processorTo accommodate the memory andcomputing power requirements that gobeyond the limits of a single server, data canbe divided into subsets and placed across acluster of servers, forming a distributeddatabase (scale-out approach). www.xpress-analytics.com Ph: 8775734486
In a recent independent benchmark HANA raced through a 100TBtest database with 100 billion records. First, HANA achieved a 20xdata compression level, which was remarkable. More impressive,though, was that with no caching, indexing, or materializing of thequery results, the query responses were a mere 300 to 500milliseconds. Compare this to some Oracle documentation thathas claimed it was "lightning fast" at processing 100 million recordsin one second. HANA, then, can run 1,000 times more data in lessthan one-half the time than Oracle. www.xpress-analytics.com Ph: 8775734486
Beyond benchmarks, in the real world of Wall Street, oneHANA application is using Sybase CEP (Complex EventProcessing) to feed more than 2.1 million updates per secondinto the database. In a retail environment in Japan, onecustomer achieved 400,000 times performance improvementover its previous database environment. Adobe uses HANA toanalyze customer data in real time and T-Mobile runs threeHANA databases to analyze and reduce customer churn. Itsstories like these that make HANA the fastest growing productin SAP history. www.xpress-analytics.com Ph: 8775734486
SAP HANA Use CasesAgile Data Mart In this scenario, SAP HANA acts as the central hub to collect data from a few SAP and non-SAP source systems and then display some fairly simple and focused analytics in a single-purpose dashboard for usersSAP Business Suite Accelerator The second major scenario where SAP HANA is being used is to accelerate transactions and reports inside the SAP Business Suite. Again, SAP HANA is being set up as a stand-alone system in the landscape, side-by-side with the database under the SAP Business Suite applications. In this scenario, however, SAP HANA is being used to “off load” some of the transactions or reports that typically take a long time (hours or days) to run, but it is not being used as the primary database under the application. www.xpress-analytics.com Ph: 8775734486
SAP HANA Use CasesPrimary Database for SAP NetWeaver Business WarehouseIn this scenario a company replaces the previously underlying database for their SAPBW system with SAP HANA. The IT team can perform a standard DB migration over toSAP HANA and then enable specific objects to be in-memory optimized as necessarydepending on the company’s requirements.Custom Applications for SAP HANAAs stated earlier, SAP HANA is a full-blown, do-just-about-anything-you-wantapplication platform. It speaks pure SQL, and it includes all of the most common APIs,so you can literally write any type of application you want on top of it. www.xpress-analytics.com Ph: 8775734486
Real Time Enterprise: Value Proposition Addressing Key Business Drivers1. Real-Time Decision Making There is a significant interest from business to get agile There is a significant interest from business to get agile analytic solutions. analytic solutions. • Fast and easy creation of ad-hoc views on business „In a down economy, companies focus on cash protection. „In a down economy, companies focus on cash protection. The decision on what needs to be done to make The decision on what needs to be done to make • Access to real time analysis procurement more efficient is being made in the procurement more efficient is being made in the procurement department“. procurement department“.1. Accelerate Business Performance CEO of a multinational transportation company CEO of a multinational transportation company • Increase speed of transactional information flow in areas such as planning, forecasting, pricing, offers… Flexibility to analyse business missed by LoB. Flexibility to analyse business missed by LoB.1. Unlock New Insights „First performance, and the other is flexibility on a „First performance, and the other is flexibility on a business analyst level, who need to do deep diving to business analyst level, who need to do deep diving to • Remove constraints for analyzing large data volumes - better understand and conclude. The second would be better understand and conclude. The second would be that also front-end tools are not providing flexibility“. that also front-end tools are not providing flexibility“. trends, data mining, predictive analytics etc. Executive of a global retail company Executive of a global retail company • Structured and unstructured data1. Improve Business Productivity Traditional data warehouse processes are too complex Traditional data warehouse processes are too complex • Business designed and owned analytical models and consume too much time for business departments. and consume too much time for business departments. „„The companies […] were frustrated with usual The companies […] were frustrated with usual • Business self-service reduce reliance on IT problems […] difficulty to build new information views. problems […] difficulty to build new information views. These companies were willing to move data […] into These companies were willing to move data […] into • Use data from anywhere another proprietary file format […]. ““ another proprietary file format […]. Analyst Analyst1. Improve IT efficiency • Manage growing data volume and complexity efficiently • Lower landscape costs www.xpress-analytics.com Ph: 8775734486
Real Time Enterprise: Value PropositionThe Value Blocks Value Elements In-Memory Enablers New business models based on real-time Run performance-critical applications in-memory information and execution Combine analytical and transactional applications Improved business agility Dramatically improve No need for planning levels or aggregation levels planning, forecasting, price optimization and other processes Multi-dimensional simulation models updated in one step New business opportunities faster, more accurate Internal and external data securely combined business decisions based on complex, large data Batch data loads eliminated volumes High performance “real-time” analytics Sense and respond faster Apply analytics to internal and external data in real-time to trigger Support for trending, simulation (“what-if”) actions (e.g., market analytics) Business-driven data models Business-driven “What-If” Ask ad-hoc Support for structured and un-structured data questions against the data set without IT Analysis based on non-aggregated data sets Right information at the right time Eliminate BW database Lower infrastructure costs server, storage, database Empower business self-service analytics – reduce Lower labor costs backup/restore, shadow IT reporting, performance tuning Consolidate data warehouses and data marts In-memory business applications (eliminate database for transactional systems) www.xpress-analytics.com Ph: 8775734486