How I Built Daily Monitoring Dashboard in Under 10 Minutes

In today’s data‑driven world, building monitoring dashboards in Power BI often feels repetitive: drag fields… configure axes… format markers… copy styles… fix errors… repeat. Creating even a handful of visuals can easily take hours.

But what if the entire process could be automated, consistent, and done in minutes?

Recently, I built a complete monitoring dashboard for our A3P Analytics platform using GitHub Copilot Agent Mode and the Power BI Project (.pbip) format—and what would normally take 2–3 hours was completed in ~10 minutes.

Here’s exactly how.

Understanding user behavior in learning systems, SaaS platforms, or product telemetry often starts with semi-additive calculations. 

But semi-additive metrics only tell what happened at a point in time. They do not reveal behavioral continuity. To address this, a solution requires sequence analysis, specifically using run-length encoding through DAX or SQL window functions.

When you are working on Power BI, one of the fundamental aspects of building a robust data model is, defining how different tables are related to each other. This relationship determines how filters and slicers affect the data, and one of the key settings you will encounter is the Cross Filter Direction. But what does this actually mean, and how does changing it impact your report’s behaviour?

I will walk you through in this blog the feature of Cross Filter Direction in Power BI. the types of filtering relationships available, the potential effects of changing these directions, and the advantages and considerations of each approach.

Ever wondered how to replicate SQL APPLY clauses in DAX ?

In the following article ,I propose a ingenious method to replicate the APPLY behavior in DAX.

Welcome to Our November 2025 Newsletter!

We’re excited to bring you fresh insights this month as we begin a brand-new series on Scenario Analysis in Power BI.

Starting with this edition, I’ll be sharing a collection of articles that explore different approaches to scenario analysis and their practical applications in real business cases.

In this first article, we’ll focus on Simple Scenario Analysis — integrating What-If Parameters to dynamically adjust key variables and visualize how changes impact outcomes directly within Power BI.

Resources and files for this article are available for download here: https://1drv.ms/f/c/357fb5c8090fe1b2/EraKe-FIlR1EpTL1HYTpAREBYFALs9GKTMh7MnWebL6mWg?e=05Qg4J

In this analysis, let’s create a group (using Home → Enter Data) to organize all the measures we’ll be using.

Have you ever tried explaining a Power BI issue and immediately realized you can’t share the dataset behind it?

Maybe the data is confidential, maybe it’s messy, or maybe you just don’t want to hand-craft a new example every time. Before you know it, you’re exchanging files, fixing paths, and losing momentum...

My solution? Generate a sample dataset using Power Query only!

While developing a BI report, sometimes information is spread across multiple columns and other times it is packed into rows. There are three powerful Power Query transformations come into the picture, which are Pivot, Unpivot and Transpose.

In this blog, l will walk you through what they are, why they are useful and how they different.

Power Query functions are the backbone of efficient and reusable transformations in Power BI.

In this hands-on post, we’ll explore three practical examples, from simple text cleanup to a dynamic pivot and automatic INR currency conversion.

Wanting some accountability for studying for your Fabric certification exam.  Read on for details on how to join a study group.

When you start building a report on Power BI platform, you can quickly see lines connecting tables in the Model View. It looks very simple but behind those lines lies the secret of how Power BI understands your data. What do these actually mean?

The Translytical Task Flow feature marks a significant evolution in Power BI, From a read-only analytics tool to an interactive, action-oriented platform. By combining Fabric UDFs with new UI elements like Text and List Slicers, you can now build seamless workflows that empower users to act on insights instantly.

If you are exploring Power BI, you might already have come across the Drill Down and Drill Through options. At first, both look similar in this way they help you explore data in detail. But in real time, they work differently and are used for different purposes.

This post demonstrates how to build a synthetic, large-scale EdTech dropout prediction pipeline using Microsoft Fabric’s Data Engineering and Machine Learning workloads.
It implements the Medallion Architecture (Bronze → Silver → Gold) to produce ML-ready features that predict student dropout risk.

As a Fabric User, I've witnessed the evolution of data platforms. We've all managed the chaos: a data lake here, a data warehouse there, and a labyrinth of ETL pipelines connecting them. It's complex, costly, and slows us down.

Microsoft Fabric introduces a feature that fundamentally changes this paradigm: OneLake.

The full live schedule for Fabric Data Days. Sessions also available on-demand. Read on for more details.