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Data Hub doesn’t have a general-purpose data transformation or pipeline builder. Instead, calculated metrics are created as derived variables — a variable whose value is computed from a formula referencing other variables, rather than being fed directly by a data source.

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How derived variables work

A derived variable’s formula references other variables using a $variable_name notation, combined with standard arithmetic operators (+, -, *, /). For example:
Each variable — recorded or derived — has a unique Data Hub ID. When writing a formula in the interface, referenced variables are shown by their display name rather than their raw ID, to make formulas easier to read. A derived variable can reference:
  • One or more recorded variables (variables fed directly by a data source)
  • Other derived variables, allowing formulas to build on each other

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Recalculation behavior

Derived variables recalculate automatically whenever a variable they depend on receives a new value. Whether a partial update triggers recalculation depends on how the derived variable is configured:
  • If configured to allow partial calculation, the value recalculates as soon as any dependency receives a new value — dependencies without a value yet are treated as unavailable rather than blocking the calculation.
  • If configured to require all dependencies, the value only recalculates once every dependency has a new value.
Calculations run against the highest-resolution data available in Data Hub — minute-level data.

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Viewing a derived variable’s formula

Every variable’s Details view shows whether it’s a derived variable, and if so, its formula and the variables it depends on. See Core Concepts for more on variable types.
This page covers derived variables as they exist in Data Hub today. There is no separate data pipeline, join, or aggregation feature — if you need to combine or reshape data beyond what a formula-based derived variable supports, use the Data Downloader or Data API to export data for analysis in an external tool.