Can one platform simplify, scale, and standardize the entire analytics process? That’s the question we asked ourselves at EFQ by exploring Microsoft Fabric, which is a cloud-based data platform designed to unify analytics workflows. With strong built-in integration with Power BI, support for standardization, and scalable architecture, Fabric offers a potential shift in how organizations manage their data.
This is why the EFQ team partnered with three Computer Science students from Vrije Universiteit Amsterdam—Oliwer Dembicki, Franco Marcon, and Manav Duarcadas, under the supervision of Sieuwert van Otterloo, to take a closer look and try to understand where it fits in our own analytics strategy. Their mission was to apply Microsoft Fabric in a real-world analytics environment.
Project 1: Evaluating Microsoft Fabric’s preprocessing capabilities
Oliwer Dembicki explored how Microsoft Fabric can support and improve data preprocessing. He evaluated its ability to detect data quality issues and integrate feedback loops within EFQ’s existing analytics process. Proven by the results, Fabric is fast and easy to use for simpler preprocessing tasks. For more complex challenges, tools like Python, SQL, and Power BI are still more effective. The best approach? Integrate Fabric to your existing tools to get the best of their synergy.
💡On the practical side, we pinpointed common data quality issues like duplicates, missing values, and tricky open-text feedback, and made sure to share those insights across the team. To make error reporting more effective, we added a feedback form to our process, making it easier for everyone to flag issues as they come up.
Project 2: Microsoft Fabric vs. Python in building data pipelines
Franco Marcon compared the development and execution of data pipelines using Microsoft Fabric and Python, while also exploring briefly other cloud-based platforms. He focused on performance, development speed, and ease of use, especially for people with diverse technical expertise. The results showed that Fabric offers a more complete and integrated solution for complex pipelines, while Python is still faster and more effective for simpler tasks. The ideal strategy is to tailor tools for your specific needs.
💡Practically, complex pipelines involving multiple collaborators are better suited for integration with Fabric, while simpler processes, like handling distinct files, are more efficient to keep in Python.
Project 3: Enhancing Power BI dashboards with AI tools in Fabric
The final project by Manav Duarcadas explored the integration of AI tools within Power BI dashboards. The study focused on three features: key influencers, decomposition trees, and Q&A, evaluating their performance and ease of use. Each tool showed strengths and limitations, making them suitable for different use cases. When combined with traditional visuals, these AI-driven features have the potential to make dashboards more insightful, interactive, and easier to explore.
💡This hands-on experience really helped us identify which AI visuals are worth adding to our dashboards based on specific use cases. For instance, decomposition trees work great for internal analysis, while key influencers can boost insights and engagement for clients.
Conclusion
With this study EFQ gained a new perspective to approach the capabilities and limitations of Microsoft Fabric, in combination with a constant evaluation of the tools used. While this technology isn’t a one-size-fits-all solution, it offers benefits when used alongside existing tools, creating a powerful synergy. By strategically integrating Fabric, EFQ will deliver scalable analytics to clients, empower teams with flexible tools, and build an innovative, future-ready environment. This project has helped EFQ stay ahead in analytics by combining new cloud technology with proven tools.
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