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Data source:

Before you start

    1. Getting started

    2. Model refactoring

    3. Advanced metrics

    4. Use your data

    The data model

    Now it’s time for a little theory but we’ll keep it short.

    The way that we achieve this level of power with simplicity is through data modeling.

    You had to do only one thing: give us a hint about which columns can be averaged/summed (we call them facts or measures) and which can be used to group the results (attributes). If your data includes date columns, we need to know it too so we can automatically give you an option to group or filter the numbers by weeks, months, quarters, or other time frames.

    Once you annotated columns of a file (or multiple connected files) as measures and attributes, optionally with additional information such as human readable names, you have a logical data model (LDM). For advanced modeling, you can use our visual desktop modeling tool. Another approach is using our APIs and SDKs - in a later step of the tutorial, we will show you how to extend and refactor your data model using a script we’ve prepared for you.

    For a comprehensive overview of our modeling tools and capabilities in action, you may want to watch our technical webinar on modeling, MAQL, and Analytical Designer (registration required).

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