
Why we are called Sparkle
Before a good decision, an insight. Before an insight, a spark. The story behind the name on the door.

Before a good decision, an insight. Before an insight, a spark. The story behind the name on the door.

Microsoft Fabric unifies data engineering, analytics, and AI across the Microsoft ecosystem.
But many business problems like fraud detection, customer 360, supply-chain optimization, … depend not on single data points, but on how those points connect.

Most companies want AI in their workflows. Few have a data foundation that can actually run it. Sparkle’s framework: what to fix first, what can wait, and how to get governance in place before the AI lands.

Microsoft Fabric unifies data, analytics, and AI on a single platform, simplifying management and enabling real-time, AI-driven insights. Organizations can move from reporting to automated processes, unlocking efficiency, predictive insights, and smarter business decisions. Planning the transition now ensures a smooth, cost-effective adoption.

Organizations using Microsoft Fabric with Power BI increasingly need ways to capture user input, not just analyze existing data. Until recently, the main native options were Power BI custom visuals and Power Apps. Since May 2025, a third option- Fabric Translytical Flows- has become available. This tutorial compares these three approaches to help you choose the right solution for your scenario.

Data engineers spend too much time fixing pipelines.
With MLVs in Microsoft Fabric, orchestration, lineage & data quality come built-in.

Are you looking to integrate Artificial Intelligence (AI) into your business but don’t know where to start? Sparkle, an officially approved Start IA partner, is here to guide you.

Join us for an exclusive morning session where we’ll demonstrate how Copilot agents are revolutionizing productivity across industries.

Making the right financial decisions starts with accurately measuring price elasticity—how demand responds to price changes.

What is Data Mesh and is it still relevant? What are some of the common goals and challenges faced during the early days of Data mesh implementations at two different large organisations.