Power BI Data Modelling: Star Schemas, Relationships, and Joins Explained

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Data modelling in Power BI involves organizing tables, defining relationships, and structuring business data so it can be efficiently analyzed. Common data sources such as databases, Excel files, and cloud platforms often store related information across separate tables, requiring Power BI to understand how those tables connect. Microsoft recommends using dimension tables for filtering and grouping while reserving fact tables for summarization, following a star schema approach. Flat tables, which store all information in a single structure, are simple but cause data repetition issues at scale, making them unsuitable for large datasets. Key concepts covered include cardinality, filter direction, Power Query joins, and the distinction between joins and model relationships.

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A Practical Guide to Data Modelling, Relationships and Joins in Power BI

Microsoft Power BI is a business intelligence platform that connects, transforms, and visualizes organizational data through structured data models. Data modelling in Power BI involves organizing multiple tables and defining relationships between them to enable efficient analysis and reporting. The three primary modelling approaches are flat tables, star schema, and snowflake schema, each suited to different data complexity levels. A well-designed data model improves DAX calculation accuracy, reduces memory usage, and ensures dashboards remain scalable and maintainable over time. While flat tables are simple to build for small analyses, star schemas are preferred for larger datasets due to their predictable join paths and better compression performance.

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