Understanding Data Modeling in Power BI: Joins, Relationships, and Schemas Explained Using Kenya Crops Data

Data modeling is the foundation of building accurate and high-performing reports in Power BI. It helps organize raw data into structured tables, define relationships, and ensure that calculations, filtering, and aggregations are correct. Using the Kenya_Crops dataset, we can explore joins, relationships, schemas, and common modeling practices in a practical, real-world scenario. 1. What is Data Modeling? Definition: Data modeling is the process of designing how data is structured, connected, and used for analysis. Purpose: Ensures efficient reporting, accurate calculations, and easy data maintenance. Power BI components: Fact tables, dimension tables, relationships, calculated columns, measures, and hierarchies. Example: In Kenya crops data: Power Query showing fact table after transformation 2. Fact vs Dimension Tables Fact Tables: Contain numeric, transactional data. In Kenya crops: Kenya_Crops_Cleaned_Final with metrics: Yield (kg) and Price (KES). Contains foreign keys: CountyID, C