SemPy offers many possibilities with Semantic Models in Microsoft Fabric, some of which are remarkably effective and simplify some tedious tasks. As Data Analytics Developers, we often debate the best naming conventions. However, business users expect us to set aside our fixation on naming conventions and create more readable outputs with a simple design. When we create our models for Power Users, Self-Service BI users, or Power BI designers, they need simple, explainable nam

Cloning a semantic model or report in Power BI isn’t always recommended—but sometimes, it’s the smartest move. Whether you’re juggling multiple environments or building templates for different domains, this step-by-step guide shows how to clone semantic models and reports with Python using sempy_labs, and rebind them with ease.

In my last post, I discussed how I've been using Sempy and SemPy Labs to convert column names to Snake Case . This post will explore the next important step: creating and managing measures. Having all measures simplifies processes significantly; it makes changes easier, makes smoother documentation, enhances AI productivity and facilitates deployment to a new semantic model. What I appreciate most is the ability to add descriptions and organise measures into folders effortles

Naming conventions are a small but crucial part of creating effective data solutions. In this post, I explore how to do in Microsoft Fabric

Understanding the relationships between datasets is crucial in data analytics, especially in the world of self-service BI. Sempy, a Python library unique…

Documentation is a critical and tedious part of every project. However, it is essential to review existing developments or document new…

Like many I am also playing with Fabric, many of my clients are also excited about Fabric and want to know…