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White Paper: Advances in Data Warehouse Performance
WinterCorp analyzes IBM's DB2 Warehouse and how it addresses twin challenges facing enterprises today: improving the value derived from the torrents of information processed every day, while lowering costs at the same time.
Discover why WinterCorp believes the advances in data clustering strategies and intelligent software compression algorithms in IBM's Data Warehouse improves performance of business intelligence queries by radically reducing the I/O's needed to resolve them.
Read this paper to learn how to reduce query response time from hours to seconds, support real time data warehousing, optimize your storage and lower operating costs.
| Data Warehousing |
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A study conducted by IBM identified that the typical company utilizes only 2% to 4% of the data they collect in operational systems. These operational systems, such as online transaction processing (OLTP), Enterprise Resource Planning, legacy and e-Commerce systems, provide a high level of automation and organization of corporate data, yet they provide limited access and analytical capabilities for business users. A Data Warehouse provides a repository of integrated information from operational systems across the enterprise, supporting the complex information needs of corporate decision makers. Data and information are extracted and integrated from heterogeneous operational systems, and made available to business users for analysis and query. A data mart is a repository of information gathered from operational systems that is designed to serve a particular community of business users. This data may derive from an enterprise-wide database or data warehouse or be more specialized. The emphasis of a data mart is on meeting the specific demands of a particular group of knowledge users in terms of analysis, content, presentation, and ease-of-use. DataClarity offers comprehensive end-to-end solutions that start with your business goals, through design and implementation to knowledge transfer. These solutions are designed to fit into your existing technical and information architecture. The result is an information delivery infrastructure that enables business users to assess, enhance and optimize business performance and operations. Data Extraction, Transformation and LoadingAn important component of a data warehouse or data mart solution is the Data Extraction, Transformation and Loading (ETL) process. Data Extraction, Transformation and Loading is the process of capturing data from one or many sources and transforming and loading that data into a data warehouse or data mart. This ETL process involves such tasks as data cleansing, purging not useful data and combining data from different sources. These tasks can be extremely time consuming and could take 30-85% of the implementation time. DataClarity can significantly reduce this traditionally manual process through the use of third party tools designed to meet most ETL requirements. More importantly, this allows an organization to be able to adapt to a new applications and operational systems in the most effective way. In an effort to provide our customers comprehensive data warehousing solutions, DataClarity has partnered with Cognos Corporation. DataClarity combines best of breed tools from Cognos and other software technology providers with its own installation, configuration, design, development, deployment and Cognos training services to build very powerful data warehouse and data mart solutions for multiple industries and technologies. |
















