Data Science Community Blog

Data preparation is one of the most time-consuming steps in any data analytics workflow. Fortunately, Data Wrangler in Microsoft Fabric simplifies this process by offering a graphical, no-code interface for exploring and transforming data directly inside notebooks. In this article, we will explore how Data Wrangler helps clean and transform data efficiently without writing complex code.

Scaling data science from pilot to production isn’t just about models - it’s about platforms. Learn how we leveraged Microsoft Fabric to transform a complex optimization problem into a fast, scalable, and business ready solution.

In this blog, we explored how to build a simple yet effective machine learning workflow using Microsoft Fabric together with MLflow. By using MLflow within Microsoft Fabric, it becomes much easier to organize experiments, compare model performance, and maintain a clear history of training runs. This approach helps ensure that machine learning experiments remain reproducible, transparent, and easier to manage, especially when multiple models and configurations are involved. We also saw how experiment tracking enables us to retrieve runs, analyze results, and visualize model performance to identify the best-performing algorithm. Once the optimal model is identified, it can be saved and integrated into downstream analytics workflows, helping organizations turn data into actionable insights.

In this edition, I’ll walk you through why ingesting data into a Microsoft Fabric lakehouse matters, how Apache Spark plays a pivotal role in this process, and ultimately, how you can do it yourself. Whether you’re an experienced data scientist, data engineer or a data analyst wanting to expand your toolkit - this guide is for you.