Data Warehouse Technique Research And Implementation

This paper brings forward data warehouse (DW) according to technique development and business requirement. There is lots of difference between OLTP (Online Transaction Process) and OLAP (Online Analysis Process). OLTP mainly considers efficiency of transaction, while OLAP mainly processes how to acquire data quickly. Data warehouse is subject-oriented, integrated, contains historical data, time variant, and non-volatile. DW satisfies the requirement of accessing data frequently, and is the most practical technique of the solutions of DSS (Decision Support System).Traditional DSS solutions have some defections, such as hard in technique, difficult to implement, and these restrict its development. Appearance of DW brings vital force for DSS. The purpose of DW is to improve decision ability. DW Design should consider many details, such as physical storage, metadata, and technique of improving data access and methodology of software development.The development of DW architecture determines that enterprise must adopt appropriate architecture and physical implementation in design. From data mart in department to enterprise DW, it may take a long time to implement. There is difference between DW development method and SDLC (Software Development Life Cycle) in OLTP; SDLC is based on requirement, while DW on data. We know what we need in SDLC. But we must repeat to design and spire to determine what we need in DW, and its model usually adopts E-R model.Methodology provides reference for project implementation. The life cycle of DW development includes program plan, requirement analysis, design, construction, program deployment and implementation, technique training and maintenance. DW projects must combine with practice in design and development. As more experience accumulating in practice, we can success in DW project.As a practical project, we design a DW in a university, and focus on the analysis, requirement, and data model design, especially introduce how to select tools in DW designing. The design is simple, and the emphasis is to understand how to extract data from differentOLTP systems, importance of metadata, data partition and granularity decision.