Have you ever considered incremental refresh for a large fact table but weren’t sure which date field to anchor it on? A sales record, for instance, might only be considered “closed” and ready for archiving when it’s logged in the accounting books. The problem? The AccountingDate field is only populated at that moment—until then,
Have you ever woken up to find your Power BI refresh failed overnight—again? If the culprit is duplicate keys, you’re not alone. This common issue can silently sneak into your model, especially when working with user-maintained data sources like Excel. The good news? You can automate the detection and notification process so that stakeholders
Imagine you’re analyzing a company’s sales data. You have a sale date, a delivery date, and an invoice date. You want to compare trends based on when sales occurred versus when orders were delivered, and you need filters and aggregations for both. What’s the best way to model this? The Problem with a Single Calendar
Introduction: Why Cost Optimization Matters Power BI Premium provides dedicated capacity for large datasets, but inefficient refresh strategies can waste resources, leading to higher costs. Incremental Refresh (IR) helps reduce memory usage, processing time, and compute costs by refreshing only new and modified data instead of reloading the entire dataset. Additionally, IR reduces the load
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
By default, Power BI incremental refresh partitions only include data up to the current period, leaving future-dated entries excluded. This can be problematic for scenarios requiring forward-looking data. In this article, we demonstrate how to modify the partitioning logic to include future dates within the most recent partition. While this example focuses on day-level refresh
“Work smart, not hard” is a powerful aphorism of universal application. In this article, I share what I have learned about increasing productivity in Power BI. I won’t be sharing tips or tricks on speeding up queries or creatively formatting visuals. My focus is to share high-level lessons with intermediate and advanced Power BI professionals
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
