Analyst in Action

Last blog for the year! And before leading into 2026, let's talk about one of the hot topics that is always tricky to understand while working with Power BI and Fabric. On most days, your reports are running perfectly, and suddenly, you get a notification that you have consumed more than 80% of the capacity.  A report that usually opens in seconds starts taking ages to open. Visuals take longer to load. Sometimes a refresh fails with a capacity-related error. When you check the metrics, there is a sharp spike in capacity usage that wasn’t there before. What makes this frustrating is that nothing obvious changed. No new reports were deployed. No large datasets were added. Yesterday worked fine, so today  should  work fine too. The natural response is to assume the capacity is too small. If usage spiked and things broke, it feels logical to conclude that more capacity would solve the problem. This conclusion makes sense. Capacity feels like a fixed limit, and hitting that l...

Another week another blog!! This week we will be focusing on the optimising the bad performing measures and how to tackle the measures generating Call back ID. Before starting, this blog is intended for Users who are a bit familiar with DAX Studio if not then you can do a quick read on our most read blog on our website ( link ). Performance of report is a great factor in improving in the user experience and to check how well your visuals and DAX formulas are performing you can utilise the benefits of Performance Analyser. In my experience Performance analyser along with the DAX Studio is a match made in heaven. You can copy the query from Performance analyser and evaluate in DAX Studio. To know more about how performance analyser works do visit this interesting article ( link ). Let's get our hands dirty and see how you can evaluate your DAX formulas. To do so I have created a basic DAX with help of Filter function  Now, we have included this measure in a table along with the year ...

Are you still struggling with slow reports and a complex data model and you have tried every approach to optimise it? One of the major reason behind your slow reports are high cardinality fields. What is this cardinality now? The simplest definition for it would be the unique values present in a particular column.  Do note we are talking about the unique values not distinct values there is a slight difference between these two. Want to learn more about cardinality here is a great article from Arno. If you have a background in data analysis then the term cardinality may not be alien for you. When we talk about optimising the data model then it is always advised to reduce the cardinality as more the unique values available in a particular column  more it will take the memory and impacts the performance. In this article, we will focus on a few easy steps to reduce the cardinality of the data model. The very fundamental and the simplest step will be to only include the fields tha...