The Realization Of The Prototype Of A Data Warehouse Based On QM ERP

ERP systems and Data Warehousing are the hot spots in recent years, and they have become more closer: Data Warehouse can fit further demand of ERP systems - Data mining and analysis; and ERP systems can provide the most comprehensive data sources.The main purpose of this article is a study in building Data Warehouse based on ERP systems, and making a prototype. It contains the following:1. First part including introduce the concept of Data Warehouse.And also its history and current situation. And then the logic and physical components of Data Warehouse are described. And what is more how they work togerther. Then introduced two of the technologies and applications of Data Warehouse. Then point out the advantage of building Data Warehouse based on ERP combine the characteristics of ERP.2. Introducing the target of the prototype and the characteristics of the data source. Business requirements include: to meet with general Data Warehouse decision support functions; general data are phased into the Data Warehouse, data that is not real-time, but static, history. Of the Data Warehouse to meet the requirements of real-time demand; in the Data Warehouse to save the historical details of the ERP system data for operational staff took place not long history of providing data to support the inquiry, that is to have the history of the inquiry; Because the system is operational for automotive manufacturing industry, it has more detailed data, and the Data Warehouse will include Finance, Sales, Production, Cost, Inventory, HR and the development of several modules, we are faced with data from the data source and type of very large scale;The whole process from the ERP system to the final design of the physical model is very complex; A large amount of data.3. Listed Data Warehouse development process in general and giving the planning of my team to develop the Data Warehouse. Including the general demand for business process analysis, the choice of development tools, the overall structure of the specific development, deployment, maintenance and application.Analysis of the needs of business is to collect business requirements, including individual interviews, the promotion of collective talks and a similar case law inferred. Choice of development tools including Select database tools, ETL tools and options to choose to display the data tools.At the technical level, we treat the Data Warehouse as a set of data storage layer, data transfer layer, and the data application layer.The data of the data storage layer come from collention.Most of them will be used directly.Data transfer layer provide the data source for data storage layer. Application logic layer, including the main data analysis functions which including the WEB application system and the system of MicroStrategy 8 product. It is used to achieve a comprehensive inquiry, KPI indicators, OLAP analysis, dynamic dashboard functions.The main methord of Data Warehouse development is dimension modeling. Dimension modeling is a logic design technology, which attempts to adopt some kind of visual the framework of the standard structure to performance data, and allows for high-performance access. Dimension modeling consisted of the definition of the dimension table,view and the relationship between them. Modeling time to achieve the physical design phase of the design and structure of the table data dump. The final step is to develop the use of data showing the development of tools to display, where the use of MSTR's Desktop tool.4. In addition to the level of achievement of the program, this article gives the achieving process of the technical details . Which contains more specific we have demonstrated for the development of the written part of the financial documents, including the objective and target of the financial analysis, and also the realization . What is more is the data conversion process from ODS to ERP, the data preparation area, materialized view log and the Stream technology. Finally,including agents key technologies, conversion of ODS to Data mart ,metadata management and data backup .5. Show the result on a setp-to-step to make readers know Data Warehouse more directly.6. At last ,sum up my harvest in the project and giving my prospect. Experience, including the right choise of logic of division, choosing the right software and keep the data quality at any tome. Concluded: ERP system can provide the most comprehensive and detailed data for data analysis and data mining ,and Data Warehouse should be build on systems like that; Data modeling has not yet standardized , And look forward to a way of modeling in theoretical circles agree that such data in the warehouse, If ERP able to meet its own demand at the same time, the need of Data Warehouse, I believe that the Data Warehouse can be faster and better on the combined ERP.