Kudler Dimensional Model Hands-on-Project

Kudler Dimensional Model Hands-On-Project Erwin Martinez DBM-460 March 14, 2011 Daniel McDonald Kudler Dimensional Model Hands-On-Project Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information about all of its customers: their age, ethnicity, demographics, and shopping habits. The starting point will be a data warehouse containing a combination of internal data tracking all customers contact coupled with external market data about competitor activity. Background information on potential customers also provides an excellent basis for prospecting. ETL processes that populate the dimensional model database The (ETL) will extract data from the source and load it into the data warehouse. OLTP database transforms data extracted to match the data warehouse schema and loaded into the data warehouse database along with other non-OLTP data such as text files, legacy systems, and spreadsheets. ETL updates as required (monthly, daily, hourly). ETL operations performed on a relational database server separate from the source databases and the data warehouse minimizing the impact of the intense periodic ETL activity on source and data warehouse databases. Extraction To extract the data, each source’s distinct set of characteristics will need to be managed and integrated into the ETL system. The content of the logical data mapping document has been proven to be the critical element required to plan ETL process. The primary purpose of this document is to provide the ETL developer with a clear-cut blueprint of exactly what the expectations are from the ETL process. Data Discovery’s Phase key

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