Modeling Metrics
Metrics are an important part of model design. They not only identify the quantifiable data you want to analyze, but are also what AtScale needs to generate aggregates for a model at query runtime.
Metrics are sometimes referred to as measures in Design Center.
A metric is a numeric value representing a summarized (or aggregated) dataset metric (such as the sum of sales or average order quantity). Metrics always result from an aggregate calculation applied to one or more columns of a fact dataset.
🗃️ Types of Metrics
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📄️ About Metrics and Aggregates
AtScale's aggregate management system depends on the metrics you define in a model. Every model must have at least one metric. The metrics of a model provide the basis for analysis in a BI client application.
📄️ About Queries on Dimensions that are Unrelated to One or More Queried Metrics
You can use the Unrelated Dimensions Handling functionality to specify the behavior of the AtScale engine when all of the following conditions apply:
📄️ Add Additive or Non-Additive Metrics
You can add additive or non-additive metrics to a model by selecting a column in the fact dataset, as well as a supported aggregate calculation to apply to the data in that column.
📄️ Add Semi-Additive Metrics
In AtScale, creating a Semi-Additive metric allows you to choose dimensions over which the fact data should not be aggregated. Instead, you have the choice of returning the first or last non-empty value of a result set.
🗃️ Add Calculations
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🗃️ Add or Edit a Metric within a Dimension
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📄️ Bulk Create Metrics
You can create metrics in bulk from a fact dataset in a model.
📄️ Referencing Calculation Groups in Calculations
Metrics within calculations can be scoped by the member range defined in a calculation group, typically a quantity of time like Month-to-Date or Equivalent Period in the Last Year.