Static versus dynamic row-level security in Power BI.
Introduction
As a data analyst, you’ll often work with sensitive data that’s vital for your company’s success, from manufacturing stats to market trends. Securing this data properly is an important part of your role. In this reading, you’ll develop a strong understanding of row-level security (RLS) in Power BI, including the differences between static and dynamic RLS, which will help you control data access and protect sensitive information.
Static versus dynamic row-level security
Row-level security (RLS) protects sensitive data by limiting access based on user roles. There are two primary RLS methods: Static RLS and dynamic RLS.
Static RLS
Static RLS explicitly defines what data each role can access in Power BI. It ensures that users only access their assigned data sets and nothing more. Once these assignments are made, they remain fixed unless modified deliberately.
For example, Adventure Works can use static RLS to ensure that an administrative assistant in Adventure Works’ sales department can only view North American sales data tailored to their role. This provides them with a consistent workspace and prevents them from viewing other data unrelated to their role.
Dynamic RLS
Dynamic RLS adapts to the user’s needs, unlike static RLS, which has fixed rules. It checks the user’s attributes or roles and grants access accordingly.
For example, an assistant data analyst at Adventure Works often switches between sales and manufacturing roles. Dynamic RLS adjusts their data access as they change roles, ensuring they always have the right data for their current task.
Let’s explore these two variants of RLS and their key differences in more detail.
Setup and maintenance
Configuring static RLS involves creating precise DAX filters for each role to specify their data access. It’s a static setup, usually adjusted only when significant data or role changes occur. Once established, it runs smoothly with minimal intervention.
However, if the organization decides to add a new department or expand to a new region, it requires manual updates to reorganize the existing allocations.
Dynamic RLS automatically adapts using user attributes like USERNAME () or USERPRINCIPALNAME () in Power BI to apply the correct data filters based on the user’s profile. This reduces the need for manual adjustments, saving time.
Application scenarios
Static RLS works well when roles are clearly defined, like in Adventure Works’ HR department, which only deals with employee data. It keeps HR focused and efficient by only displaying data related to employees, like contact details. It excludes data unrelated to employees, like monthly sales results or product research.
However, for more complex roles like data analysts or multi-segment managers at Adventure Works, dynamic RLS is a better option. If Adventure Works combines sales and marketing, dynamic RLS adjusts data access for transitioning employees. For example, it might show these employees monthly sales data and marketing budgets, depending on what they have access to. This helps to keep workflows smooth and data analysis unified.
Scalability and expansion
In Power BI, adding new data categories or roles in static RLS typically requires manual adjustments, especially when these additions have unique data access requirements.
However, static RLS becomes more challenging to use when scaling. New data usually requires a manual review of existing RLS rules.
For example, Adventure Works is introducing a new bicycle range for the Australian market., Managers and analysts specific to that region and product need access to all data related to this new product. This means the data team must manually create and define new rules in Static RLS to grant access to the new data sets.
Unlike Static RLS, dynamic RLS can easily handle growth. When new data or roles emerge, it adjusts access permissions automatically once user attributes match the data categories.
So, if Adventure Works updates employee profiles for the Australian bicycle range, dynamic RLS grants them the right data access. This reduces IT work and makes expansion smoother.
Conclusion
As a data analyst, you play a vital role in uncovering data insights for the company’s success, whether using static RLS or dynamic RLS. Embrace your journey of discovery, as every piece of data has a story to tell, and you have the power to shape the future with the insights you uncover.
