Business Intelligence with SAP Business Objects 4.3

VISUAL INTELLIGENCE FEATURES AND EVOLUTION A new BI general need  about a fast data visualization that enables  interactive data visualization and self consumption is now covered by SAP with SAP VISUAL INTELLIGENCE. Through  Visual Intelligence SAP aims to answer the growing demand for self-service data visualization in order to realize an analytic dashboard tool. SAP Visual Intelligence was originally released during the May 2012 SAPPHIRE NOW event, and a new version, was released as Feature Pack 3 for BI BusinessObjects 4 . Concerning  power and complexity, SAP Visual Intelligence is positionated between the dashboards such as SAP BusinessObjects Dashboards (a.k.a Xcelsius) and SAP Zen, and the self-service web and mobile tools of SAP BusinessObjects Explorer. SAP Visual Intelligence is part of the SAP BusinessObjects Explorer family, but while BusinessObjects Explorer allows for data exploration and visualization via a web service or through a mobile app, SAP Visual Intelli

The new Visual BI Interface Tap into your data - big and small - and discover answers with SAP Visual Intelligence software.  Fast data manipulation and engaging visualizations allow you to combine and analyze data from a variety of enterprise and personal sources and quickly discover unique insight – no scripts, predefined queries or reports required. With SAP Visual Intelligence, you can: Deliver faster time to insight in a repeatable, self-service way Maximize business knowledge with a combination of big picture insights and granular details Accelerate decision making with immediate, fact-based answers to complex business questions Increase self-service data usage without adding to your IT department's workload Visualize any amount of data in real time – with SAP HANA For detailed information visit the official SAP site: SAP Visual Intelligence See Also: Using SAP Visual Intelligence Connect SAP Visual Intelligence to data sources

Sizing the RAM needed Sizing an SAP HANA system is mainly based on the amount of data to be loaded into the SAP HANA database, because this determines the amount of main memory (or RAM) needed in an SAP HANA system. To size the RAM, the following steps have to be performed: 1. Determine the information that has to be transferred (either by replication or extraction) to the SAP HANA database. Note that typically customers will only select a sub-set of information from their ERP or CRM database, so this has to be done at the table level. The sizing methodology is based on uncompressed source data size, so in case compression is used in the source database, this has to be taken into account as well. The information required for this step can be acquired with database tools. SAP Note 1514966 contains a script supporting this process for several database systems, for example, DB2 LUW and Oracle. The current size of all the tables (without DB indexes) storing the required information

T-shirt sizes for SAP HANA SAP defined so-called T-shirt sizes for SAP HANA to both simplify the sizing and to limit the number of hardware configurations to support, thus reducing complexity. SAP’s hardware partners provide configurations for SAP HANA according to one or more of these T-shirt sizes. In addition to the T-shirt sizes listed in Table, you might come across the T-shirt size XL, which denotes a scale-out configuration for SAP HANA. The T-shirt sizes S+ and M+ denote upgradable versions of the S and M sizes: S+ delivers capacity equivalent to S, but the hardware is upgradable to an M size. M+ delivers capacity equivalent to M, but the hardware is upgradable to an L size. These T-shirt sizes are used when relevant growth of the data size is expected. For information about RAM sizing : Sizing the RAM for SAP HANA

SAP HANA database The heart of the SAP HANA database is the relational database engines. There are two engines within the SAP HANA database: - The column-based store, storing relational data in columns, optimized holding tables with huge amounts of data, which are aggregated and used in analytical operations. - The row-based store, storing relational data in rows, as traditional database systems do. This row store is more optimized for write operation and has a lower compression rate, and query performance is much lower compared to the column-based store. The engine used to store data can be selected on a per-table basis at the time of creation of a table. Tables in the row-store are loaded at startup time, whereas tables in the column-store can be either loaded at startup or on demand, during normal operation of the SAP HANA database. Both engines share a common persistency layer, which provides data persistency consistent across both engines. There is page manag

When talking about SAP HANA , these terms are used: SAP In-Memory Appliance (SAP HANA) SAP HANA is a flexible, data source agnostic appliance that allows you to analyze large volumes of data in real time, without the need to materialize aggregations. It is a combination of hardware and software, and it is delivered as an optimized appliance in  cooperation with SAP’s hardware partners for SAP HANA. SAP in-memory database , also referred to as the SAP HANA database The SAP in-memory database is a hybrid in-memory database that combines row-based, column-based, and object-based database technology, optimized to exploit the parallel processing capabilities of current hardware. It is the heart of SAP offerings like SAP HANA. For more detailed information see Sap Hana Database SAP HANA architecture See Also: In memory key concepts SAP HANA - Data persistence Sap Hana introduction SAP HANA - Additional Components

Minimizing data movement The second key to improving data processing performance is to minimize the movement of data within the database and between the database and the application.  This section describes measures to achieve this target. Compression Even though today’s memory capacities allow keeping enormous amounts of data in-memory, compressing the data in-memory is still desirable. The goal is to compress data in a way that does not use up performance gained, while still minimizing data movement from RAM to the processor. By working with dictionaries to be able to represent text as integer numbers, the database can compress data significantly and thus reduce data movement, while not imposing additional CPU load for decompression, but even adding to the performance. On the left-hand side of this  figure  the original table is shown containing text attributes (that is, material and customer name) in their original representation. The text attribute

Data persistence in SAP HANA Keeping data in main memory brings up the question of what will happen in case of a loss of power. In database technology, atomicity, consistency, isolation, and durability (ACID) is a set of  requirements that guarantees that database transactions are processed reliably: A transaction has to be atomic. That is, if part of a transaction fails, the entire transaction has to fail and leave the database state unchanged. The consistency of a database must be preserved by the transactions that it performs. Isolation ensures that no transaction is able to interfere with another transaction. Durability means that after a transaction has been committed it will remain committed. While the first three requirements are not affected by the in-memory concept, durability is a requirement that cannot be met by storing data in main memory alone. Main memory is volatile storage. That is, it looses its content when it is out of electrical power. To make data

SAP HANA:The future of database technology The capacity of main memory in servers has continuously increased over the years, whereas prices have dramatically dropped. Today, a single enterprise class server can hold several  terabytes of main memory. At the same time, prices for server main memory dramatically  dropped over the last few decades. This increase in capacity and reduction in cost makes it a  viable approach to keep huge amounts of business data in memory. This section discusses  the benefits and challenges. In few words you can understand SAP HANA plus  analyzing this evaluation: Typical access speeds Disk     4,000,000 ns Memory         0.4 ns Speed   10,000,000 Falling prices move processing from Disk/SSD to In-Memory In-Memory Computing 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