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Microstrategy | micronoodle
http://micronoodle.com/category/microstrategy
My bowl of intertwining threads on business intelligence tools. Star Schema issues and solutions. January 7, 2014. The one major difference between Star schema and Snow Flake schema is in the way they store Dimensions. Let us consider the example of Time Dimension for the purposes of this post. Select DT.QUARTER ID, max(DT.QUARTER DESC), sum(QF.REVENUE) as REVENUE from QUARTER FACT QF join DIM TIME DT on QF.QUARTER ID=DT.QUARTER ID group by QUARTER ID. YEAR would have YEAR ID, YEAR DESC. It needs to be a...
Star Schema issues and solutions | micronoodle
http://micronoodle.com/2014/01/07/star-schema-issues-and-solutions
My bowl of intertwining threads on business intelligence tools. Types of metrics in Microstrategy. Star Schema issues and solutions. January 7, 2014. The one major difference between Star schema and Snow Flake schema is in the way they store Dimensions. Let us consider the example of Time Dimension for the purposes of this post. Select DT.QUARTER ID, max(DT.QUARTER DESC), sum(QF.REVENUE) as REVENUE from QUARTER FACT QF join DIM TIME DT on QF.QUARTER ID=DT.QUARTER ID group by QUARTER ID. It needs to be an...
April | 2013 | micronoodle
http://micronoodle.com/2013/04
My bowl of intertwining threads on business intelligence tools. Monthly Archives: April 2013. Microstrategy: Guidelines for Performance : Intelligent Cubes. April 27, 2013. Microstrategy recommends enhancements in query performance by reducing the computational distance to the desired output. As per this methodology, aggregate tables, caching and cubes are some of the most effective ways to reduce query times. Below given are some guidelines in choosing reports to support using In-memory cubes: –. This o...
Uncategorized | micronoodle
http://micronoodle.com/category/uncategorized
My bowl of intertwining threads on business intelligence tools. Microstrategy: Guidelines for Performance : Intelligent Cubes. April 27, 2013. Microstrategy recommends enhancements in query performance by reducing the computational distance to the desired output. As per this methodology, aggregate tables, caching and cubes are some of the most effective ways to reduce query times. Below given are some guidelines in choosing reports to support using In-memory cubes: –. The report should be in frequent use.
About | micronoodle
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My bowl of intertwining threads on business intelligence tools. I am a Business Intelligence professional. Micronoodle is meant as an online notepad about the various BI tools I will be working with. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). You are commenting using your WordPress.com account. ( Log Out. You are commenting using your Twitter account. ( Log Out. Notify me of new comments via email.
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micronoodle | My bowl of intertwining threads on business intelligence tools
My bowl of intertwining threads on business intelligence tools. Star Schema issues and solutions. January 7, 2014. The one major difference between Star schema and Snow Flake schema is in the way they store Dimensions. Let us consider the example of Time Dimension for the purposes of this post. Select DT.QUARTER ID, max(DT.QUARTER DESC), sum(QF.REVENUE) as REVENUE from QUARTER FACT QF join DIM TIME DT on QF.QUARTER ID=DT.QUARTER ID group by QUARTER ID. YEAR would have YEAR ID, YEAR DESC. It needs to be a...
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