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Tag archive for ‘Data Modelling’

In the world of data warehousing, the grain of a fact table defines the level of detail that is stored, and which dimensions are included make up this grain. Obviously, the higher the grain the better- although source systems and data volume/performance may intervene. Using the example in the Wikipedia article on fact tables, a […]

  • Data granularity- avoid going against the grain

    In the world of data warehousing, the grain of a fact table defines the level of detail that is stored, and which dimensions are included make up this grain. Obviously, the higher the grain the better- although source systems and data volume/performance may intervene. Using the example in the Wikipedia article on fact tables, a […]

    9/15/10 • (2)

  • Mystery or Junk data warehouse dimensions

    Sometimes, when you are designing a star schema model, you’ll find yourself in a dilemma. You’ve come up with a beautiful design, right out of the pages of a Ralph Kimball book with 5 dimensions, and 5 measures, and you are on your way to star schema heaven when suddenly the users start asking akward […]

    1/18/10 • (3)

  • Data migration Part 3- Mapping the legacy systems

    This is part three of an ongoing series that’s taking a look at data migration projects. In this part we’re going to talk about how important it is to know where you are starting from, before you head off on a new application journey. Understanding and mapping your legacy systems is a key success factor […]

    12/14/09 • (5)

  • Joining the Dimension Table to the Fact Table- Purchasing Data mart (Part 5)

    After we have created the dimension tables and the fact table and populated them with data the final step to getting a star schema is of course to actually join the dimension tables to the fact table. In the datamartist tool we do this with a Join block. Check out the first four parts of […]

    2/17/09 • (0)

  • Degenerate Dimensions in Datamarts

    Not all dimensions are created equal.  A typical dimension is defined by a table that holds the reference data that is being joined to the fact data.  So in the fact table, for example, we have the product ID, or the product code, and in the product dimension table we have a single row for […]

    12/27/08 • (2)

  • Dimensional Tables and Fact Tables

    One of the secrets to putting together a good set of data marts is the concept of dimensions.  There are two key steps being able to analyse your data, and to build a working data mart model.   Build a set of clean, consistent dimension tables that store reference information about your key dimensions like Product, […]

    10/30/08 • (2)

  • Data modelling Hierarchies- how to make a dimension

    One of the most useful data model structures in a data mart is a Hierarchy (also called a Tree structure).  Tree structures let us take a large number of things and organise them in a way that makes sense.  More importantly, a tree structure lets us “drill down” into information.   Hierarchy Rules In a simple tree […]

    10/22/08 • (1)