Understanding Data Modeling in Power BI: Joins, Relationships, and Schemas Explained.

Introduction Working across fintech environments — raw data rarely comes in a shape that is ready for analysis. You are almost always dealing with multiple tables, spread across different systems, each holding a piece of the full picture. Before you can build a single meaningful report in Power BI, you need to understand how those pieces connect. That is what data modelling is about. It is the process of organising data into structured tables and defining how those tables relate to each other, so that reports are accurate, filters work correctly, and performance does not collapse under the weight of a large dataset. In Power BI, this modelling work sits between loading your data and building your visuals — and if you skip or rush it, everything downstream gets harder. This article walks through the key building blocks: joins, relationships, cardinality, schemas, and a few practical mistakes worth avoiding. 1. Joins: Combining Tables at the Query Level Before we get into how Power BI ma