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Chapter 15 - Part V Where to Now After the initial data...

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Part V: Where to Now? After the initial data warehouse project is completed, it may seem that the bulk of the work is done. In reality, however, the warehousing team has taken just the first step of a long journey. This section of the book explores the next steps by considering the following: Warehouse maintenance and evolution. This chapter presents the major considerations for maintaining and evolving the warehouse. Warehousing trends. This chapter looks at trends in data warehousing projects.
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Chapter 15. Warehouse Maintenance and Evolution With the data warehouse in production, the warehousing team will face a new set of challenges— the maintenance and evolution of the warehouse. Regular Warehous Loads New or updated data must be loaded regularly from the source systems into the data warehouse to ensure that the latest data are available to warehouse users. This loading is typically conducted during the evenings, when the operational systems can be taken offline. Each step in the back-end process—extract, transform, quality assure, and load—must be performed for each warehouse load. New warehouse loads imply the need to calculate and populate aggregate tables with new records. In cases where the data warehouse feeds one or more data marts, the warehouse loading is not complete until the data marts have likewise been loaded with the latest data. Warehouse Statistics Collection Warehouse usage statistics should be collected on a regular basis to monitor the performance and utilization of the warehouse. The following types of statistics will prove to be insightful. Queries per day. The number of queries that the warehouse responds to on any given day, categorized into levels of complexity whenever possible. Queries against summary tables also indicate the usefulness of these stored aggregates. Query response times. The time it takes for each query to execute. Alerts per day. The number of alerts or exceptions that are triggered by the warehouse on any given day, if an alert system is in place. Valid users. The number of users who have access to the warehouse. Users per day. The number of users who actually make use of the warehouse on any given day. This number can be compared to the number of valid users. Frequency of use.
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The number of times a user actually logs on to the data warehouse within a given time frame. This statistic indicates how much the warehouse supports the user's day-to-day activities. Session length. The length of time a user stays online each time he logs on to the data warehouse. Time of day, day of week, day of month. The time of day, day of week, and day of month when each query is executed. This statistic may highlight periods where there is constant, heavy usage of warehouse data.
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