From Rain Drops to Playability: Scoring Real-Time Weather Insights for FIFA World Cup 2026⚽
In this blog, I will walk you through how we can support the FIFA World Cup 2026 by organizing teams and players by delivering real-time, actionable weather insights. By predicting critical weather patterns - such as high humidity, extreme heat, and sudden rainfall - teams can proactively adjust their game-day strategies, and organizers can ensure optimal player safety. To bring this vision to life, we will build a comprehensive, real-time weather monitoring dashboard covering all 16 FIFA World Cup stadiums, powered entirely by the real-time analytics capabilities of Microsoft FabricEventhouse vs Lakehouse - When to Use Which in Microsoft Fabric?
Microsoft Fabric offers multiple storage and analytical engines. This blog helps you make the right architectural choice early on and save them massive migration efforts later. In this blog, I’ll walk you through the differences between these two powerhouses, look at real-world challenges, map out concrete scenarios, and give you a definitive comparison framework so you can choose the right tool for the job.Building Real-Time Indian Railways Dashboard in Microsoft Fabric – A step-by-step guide
In this blog, I’ll walk you through how to build a Real-Time Indian Railways Train Running Status Dashboard using Microsoft Fabric. We’ll stream live railway data from a public API into Eventstream, store it in Eventhouse, transform it using KQL, and finally visualize it in a real-time dashboard.Building a Real-Time Weather Dashboard in Microsoft Fabric: Step-by-Step Guide
In this blog, I’ll walk you through how to build a Real-Time Weather Analytics Dashboard in Microsoft Fabric by streaming public weather feeds into Eventstream, storing them in Eventhouse, and finally visualizing them in a Real-Time Dashboard. For this example, I’m using Bengaluru, India and several of its suburban locations.
Comparing JSON in Eventhouse Without Defining Schema
When working with raw JSON data in Eventhouse, one common challenge is comparing two records to identify what changed—especially when the structure isn’t fixed. Fields may appear, disappear, or shift in type, making traditional column-based comparisons brittle or outright impossible. This post will walk you through how to compare two JSON arrays and find what changed without needing to define the schema first.KQL: First Steps to Analyze and Visualize your Data
So, you’ve started exploring real-time intelligence in Fabric and maybe even followed the Microsoft tutorial to implement a real-time solution. Now, you’re looking to analyze and visualize that data, and that’s where KQL (Kusto Query Language) comes in. When I started, I struggled a bit, so I thought it would be a good idea to provide some basic queries (and explain what they do) for people who want to get started but are unfamiliar with querying data.
