4维度建模方法详解.pptVIP

* Extraction Techniques You can extract data from different source systems to the warehouse in different ways: Programmatically, using procedural languages such as COBOL, C, C++, PL/ SQLor Java Using a gateway to access data sources: This method is acceptable only for small amounts of data; otherwise, the network traffic becomes unacceptably high. In-house developed tools that: Store a physical definition of the source and warehouse data Create data dictionaries Generate data conversion programs Clean and transform the data Allow selective retrieval Maintain metadata Note: In-house development is an ongoing activity that may become a resources black hole. Alternatively, though it is expensive, you can use a vendor’s data extraction tool. These extraction tools: Provide ongoing automation of the data extraction process Support data cleanup 在线抽取和离线抽取【根据实际业务情况考虑】 * Extraction Methods The extraction method that you choose is highly dependent on the source system and the business needs in the target data warehouse environment. In addition, the estimated amount of the data to be extracted and the stage in the ETL process (initial load or maintenance of data) may also impact the decision of how to extract, from a logical and a physical perspective. Basically, you have to decide how to extract data logically and physically. Logical Extraction Methods Full Extraction: The data is extracted completely from the source system. Since this extraction reflects all the data currently available on the source system, there’s no need to keep track of changes to the data source since the last successful extraction. The source data will be provided as-is and no additional logical information (for example, timestamps) is necessary on the source site. Incremental Extraction: At a specific point of time, only the data that has changed since a well-defined event back in history will be extracted. This event may be the last time of extraction or a more complex business event like the last