Planning a Data Warehouse - System Integration
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The System Integration phase is started in conjunction with the design phase. This phase encompasses locating the source of the data in the operational systems doing analysis to understand what types of data transformations may be needed, and mapping the source data to the target data within the warehouse design. The challenge during this phase is to understand how to incorporate existing investments in platforms, technology, and know-how. Systems integration capabilities tie vendor systems together with existing data sources and existing and proposed access tools. Proper selection and evaluation of vendor supplied components and Metadata Management will prove invaluable in successfully integrating the disparate sources of data into the warehouse.
The main focus of this phase is developing procedures to extract and move data in a form that can then be loaded into the warehouse. Programs and transformation tools should be used to reduce the customized programming required to transform and integrate the data. Finally, the data must be analyzed to determine whether or not certain elements should be cleansed prior to putting it into the warehouse. Business rules should be established to standardize the data formats within the warehouse if possible.
Coming to the Best Practices, there are two things about data warehousing: One, data warehousing environments are not becoming simpler. They are complex undertakings and are likely to remain so. Two, data warehouse environments are becoming steadily more strategic to the success of the enterprise. They are not only being used for business intelligence but are also starting to take a key operational role as real-time integration technologies and real-time analytics are integrated into the mix to drive real-time decisions and actions. Consequently, it is more important than ever to implement proven best practices so as to avoid delays, excessive costs, and business disappointments as a project goes forward. Based on long experience, the following best practices are recommended to build data warehousing applications:
- Ensure that the data warehouse is business-driven, not technology-driven
- Define the long-term vision for the data warehouse in the form of an enterprise data warehousing architecture
- Avoid "stovepipe" data marts that do not integrate at the metadata level with a central meta-data repository, generated and maintained by a data integration platform
- Do not build "virtual" data warehouses that access data directly from source environments and have no target database
- Buy, don't build data warehousing components
- Create a hub-and-spoke architecture using a data integration platform to access data sources and populate the central data warehouse, data marts, operational data store, and analytic applications
And finally you can develop an application to access the information in the data warehouse using a selected platform of choice. You can develop a web application using .NET framework and SQL Server 2000 Analysis Services as demonstrated at http://www.aspfree.com/c/a/MS-SQL-Server/Accessing-OLAP-using-ASP-dot-NET/
Any suggestions or comments are welcome at jag_chat@yahoo.com
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