Power BI DAX UDFs Demystified

Key insights

• DAX User Defined Functions (UDFs): Introduce parameterized, reusable functions written in DAX that let you encapsulate logic once and call it from measures, calculated columns, visuals, or other functions.
  
• Parameters and return types: UDFs accept scalar, table, or reference parameters and return a defined type, enabling safer and more flexible logic than static calculation groups.
  
• Key benefits: Improve reusability, modularity, and maintainability by centralizing business rules and avoiding duplicated long expressions across your model.
  
• Creation and syntax: Define functions with the FUNCTION keyword, name, parameters, and return expression — for example: FUNCTION AddNumbers = (a: INT, b: INT) => a + b — and call them like native DAX objects.
  
• Integration in Power BI: UDFs appear as first-class objects in the model explorer, can call other functions, and work across measures and columns for consistent calculations.
  
• Preview and setup: This feature launched in the September 2025 update and is in preview; enable it in Power BI Desktop under File > Options and settings > Options > Preview features before use.
  

Video Overview and Context

In a recent YouTube video, Wyn Hopkins [MVP] explains the new DAX User Defined Functions that arrived with the September 2025 update to Power BI. The author walks viewers through what the feature does, why it matters, and how authors can begin experimenting while the feature remains in preview. Overall, the video provides a practical tour intended for modelers and analysts who write DAX regularly.

Hopkins positions the change as one of the most significant shifts to DAX authoring in years because it brings familiar programming patterns into the tabular model. He notes that DAX UDFs let authors create named, parameterized functions that can be reused across measures, columns, and visuals. Importantly, the video emphasizes that the feature must be enabled manually in desktop preview settings before it can be used.

What DAX UDFs Are and How They Work

The video defines DAX User Defined Functions as reusable, parameterized snippets of DAX that behave like functions in traditional programming languages. Hopkins demonstrates the new syntax and shows that functions are declared using the FUNCTION keyword with typed parameters and a clear return expression. In his examples, functions accept scalar and table inputs and can return a specific data type, which helps with predictability and authoring confidence.

He also highlights that functions become first-class objects in the model, appearing under a dedicated Functions node within the model explorer and accessible from DAX Query View or the TMDL tab. This integration makes it easier to build libraries of reusable logic and to call one function from another, enabling modular design. Consequently, the approach reduces duplication and simplifies updates when business logic changes.

To illustrate, Hopkins shows a simple declaration such as FUNCTION AddNumbers = (a: INT, b: INT) => a + b and then demonstrates calling that function from multiple measures. He points out that type annotations help catch errors during authoring and that functions can encapsulate complex logic into a single, testable unit. This shift brings DAX closer to common coding practices and supports larger, more maintainable models.

Impact on Modeling and Daily Workflows

The video argues that UDFs improve maintainability because a single, centralized function can replace repeated long expressions dispersed across measures. As a result, teams can enforce consistent business rules and reduce the risk of subtle calculation differences between visuals. Moreover, Hopkins suggests that modular functions ease debugging, since errors can be traced to the function level rather than scattered formulas.

Beyond maintainability, Hopkins explains that parametrization allows a single function to support variations without resorting to many similar measures or awkward calculation groups. This flexibility supports scenarios such as dynamic filters, normalized calculations across tables, and standardized time intelligence patterns. He notes, however, that achieving these benefits requires careful function design and naming to avoid confusion in large models.

Finally, the presentation stresses that UDFs encourage documentation and clearer model structure because functions show intent more explicitly than ad hoc DAX snippets. Analysts can build libraries of commonly used functions to onboard new team members faster and to share best practices across reports. Thus, UDFs can raise overall model quality when teams adopt disciplined patterns.

Tradeoffs and Practical Challenges

Hopkins also addresses tradeoffs, warning that bringing programming-style functions into DAX introduces complexity that authors must manage. In particular, there is a learning curve for authors unfamiliar with typed parameters, scoping rules, or how table-valued parameters behave during evaluation. As a tradeoff, the higher upfront design effort can pay off later, but it requires governance and testing practices that some teams may lack.

Performance is another important challenge discussed in the video, as overly generic or deeply nested functions can affect query plans and runtime behavior. Hopkins recommends testing functions on representative datasets and watching for unintended row context transitions or expensive table operations. He also cautions that because the feature is in preview, performance characteristics and tooling support may change before general availability.

Compatibility and versioning present a final practical concern because models that use UDFs might not open correctly in older tooling that lacks the feature. Hopkins urges teams to document the preview dependency and to control the rollout so that consumers do not encounter broken reports. Therefore, organizations must balance the benefits of modern authoring against the operational risk of introducing preview features into production flows.

Getting Started and Best Practices

To help viewers begin, Hopkins walks through enabling the feature and creating simple functions to replace common patterns. He recommends starting small, converting a few repeated expressions into functions, and validating results when used across measures or visuals. Additionally, he suggests employing clear, consistent naming and brief inline comments to make functions self-explanatory to others.

Regarding testing, the video encourages authors to build small, focused functions and to use sample DAX queries to confirm behavior before broad adoption. Hopkins also advises monitoring performance and incrementally refactoring larger calculations into functions to manage risk. Finally, he underscores the importance of team governance so naming conventions, parameter types, and reuse policies remain consistent as function libraries grow.

In summary, Wyn Hopkins’ video offers a practical and balanced introduction to DAX UDFs, showing both promise and real-world caveats. He frames the feature as a meaningful advance for DAX modeling while urging caution about preview limitations, performance testing, and governance. For teams that adopt UDFs thoughtfully, the payoff can include clearer models, less duplicated code, and faster maintenance over time.

Keywords

DAX User Defined Functions UDF Power BI, UDF in Power BI tutorial, create DAX functions Power BI, reusable DAX functions, Power BI custom DAX functions, DAX UDF examples, performance impact DAX UDF Power BI, DAX function best practices Power BI