Hi @jaryszek ,
Power BI Desktop and Power Query Online serve distinct purposes within the Power BI ecosystem, each with specific strengths and limitations. Power BI Desktop is a full featured offline authoring tool designed for advanced data modeling, complex transformations, integration with Python/R, and support for custom connectors. It is ideal for building and testing reports locally before publishing to the Power BI Service. In contrast, Power Query Online used in the Power BI Service, Fabric, Dataflows, and other Microsoft cloud services is a web based, cloud-native environment focused on centralized data preparation, collaborative workflows, and shared transformation logic.
Power BI Desktop often provides access to preview or experimental functions, which may not be supported in the online environment. Attempting to use such functions in published reports or scheduled refreshes may lead to errors in Power BI Service or Fabric. Additionally, context-sensitive functions, particularly those related to date and time (e.g., DateTime.LocalNow, DateTimeZone.LocalNow, DateTime.FixedLocalNow), exhibit different behaviors: while they return the local time in Desktop, they always return UTC time in Power Query Online, regardless of the user’s local time zone.
Further, resource-level functions (like Resource.Access) and certain authentication-dependent features—such as Active Directory Federation Services (ADFS) authentication or guest account data access are supported in Desktop with proper configuration but are not supported in the cloud-based Power Query environments. These discrepancies can affect data refresh reliability and query performance when transitioning from desktop development to service deployment.
Therefore, while Power BI Desktop remains the best environment for development and complex modeling, it is crucial to validate compatibility with Power Query Online before deploying to production, especially when using advanced or time sensitive functions. Careful planning ensures that transformations behave consistently across environments and reduces the risk of runtime errors during automated data refreshes.
Best Regards,
Chaithra E.
