DAX: Create User-Defined Functions

Key insights

• Summary: This is a concise recap of a YouTube video introducing user-defined functions (UDFs) in DAX.
  It highlights how UDFs let you write custom, reusable logic directly in DAX without copying formulas.
• What UDFs are: UDFs let you define named functions with parameters and reuse them across measures, columns, and visuals.
  They behave like standard programming functions and return values based on their input.
• Syntax: Use the new FUNCTION keyword to declare a function, for example: FUNCTION MyFunc = (A, B) => A + B.
  Functions can accept scalars or tables and can call other functions.
• How to enable: Turn the feature on in Power BI Desktop via File > Options and settings > Options > Preview features, then restart.
  After enabling, define functions in the DAX Query or model views and find them under the Functions node.
• Benefits: UDFs improve modularity, reduce duplicated logic, and make models easier to maintain and debug.
  Type checking and parameter support help authors write safer DAX code.
• Caveats: The feature is in preview, so behavior and tooling may change before final release.
  Community libraries and examples already exist, but test functions carefully before relying on them in production.

Introduction: a new chapter for DAX in Power BI

The SQLBI video introduces user-defined functions (UDFs) in DAX, a preview feature that Microsoft began rolling out in September 2025. The presenter explains that UDFs let authors define custom, reusable functions with parameters directly in the DAX language, which marks a notable change for Power BI modelling. As a result, analysts and developers can finally encapsulate repeated business logic into named units, and then call those units from measures, calculated columns, visuals, or other functions. Consequently, this reduces duplication and aims to make models easier to maintain over time.

How UDFs look and where to author them

The video shows the new syntax using a FUNCTION keyword followed by a name and a parameter list, then a DAX expression that returns a value. For example, a simple addition could be written as FUNCTION SumTwoNumbers = (A, B) => A + B, and then invoked like any other DAX expression. Moreover, functions accept scalar values, tables, or references as parameters, and they can call other functions so authors can build layered logic. The presenter also points out that you author these functions in Power BI Desktop's DAX Query View or TMDL View and that they appear as first-class objects under the Functions node in Model Explorer.

Benefits: modularity, reuse, and safer authoring

First, UDFs make it easier to centralise business rules so teams do not copy and paste the same formula into many measures, which improves consistency and reduces errors. In addition, parameterised functions support type checking helpers that guide authors during authoring, and that helps prevent common mistakes. Furthermore, because functions can be composed and referenced by other functions, complex scenarios become easier to manage and the model evolves in a more controlled way. As a result, models should become more readable and simpler to debug when rules live in one place.

Tradeoffs and performance considerations

However, the SQLBI video also cautions that this power carries tradeoffs, and teams must evaluate them carefully before widespread adoption. For instance, while functions reduce duplication, overusing them can increase indirection and make it harder to trace logic, which may slow down debugging when something goes wrong. Moreover, although the feature provides typed parameters, complex table-valued parameters and nested function calls could have performance impacts depending on filter contexts and evaluation plans. Therefore, authors should balance modularity with transparency, and always test performance impacts on representative datasets.

Challenges and practical best practices

The presenter discusses common challenges such as defining the right parameter shapes, managing dependencies across functions, and ensuring predictable evaluation in row and filter contexts. To address these issues, SQLBI recommends practical steps: start with small, well-documented functions, and avoid deep nesting until performance and correctness are validated. In addition, teams should adopt naming conventions, keep a shared library of validated functions, and include tests or example queries that demonstrate expected outputs. By doing so, groups can gain the benefits of reuse without sacrificing maintainability.

Adoption, governance, and future evolution

Finally, the video frames UDFs as a preview feature that will likely evolve, which means organisations must plan for change and version control. For example, models created with preview functionality may need attention as Microsoft refines syntax, semantics, or tooling, so teams should track feature updates and maintain backward-compatible practices. Moreover, governance matters: centralised review, documentation, and performance checks help prevent an uncontrolled proliferation of functions. The community is already responding with shared repositories and libraries, which can accelerate adoption, but teams should carefully vet any external code before importing it into production models.

In summary, SQLBI’s coverage shows that UDFs in DAX offer a promising way to make Power BI models more modular, reusable, and maintainable, while also bringing new design decisions and operational responsibilities. Consequently, teams that approach the feature deliberately — by testing performance, documenting functions, and governing reuse — are best positioned to capture its advantages and limit the risks as the language and tooling continue to mature.

Keywords

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