Incremental Refresh (IR) optimizes performance and reduces data processing time by refreshing only new or changed data instead of reloading everything. However, after configuring IR, it’s essential to verify that it works correctly. There are cases where everything seems fine initially—only to later discover that data isn’t being partitioned as expected. This guide explains how
Incremental refresh in Power BI typically implements a rolling window for dates and data, as shown in this image from the official documentation: But what if you need to archive data starting from a specific fixed date? In other words, instead of a rolling window, you want the window to expand over time while preserving
Row-level security (RLS) in Power BI allows restricting what data users can see via boolean DAX expressions; this is done per dataset. I never thought much about what it meant for a user to be assigned multiple roles until it came up in a meeting, in which I quickly declared “it would be the AND
When retrieving data from a relational source like SQL Server, always use views to decouple reports from the underlying database table structure. Avoid embedding queries directly within reports. From a maintenance and development perspective, updating a view is more efficient and less error-prone than modifying a report query and republishing the report. Additionally, if possible,