Introduction
This module covers data modeling best practices and Microsoft Fabric features to design scalable semantic models. Large- or enterprise-scale data refers to table sizes from hundreds of thousands to millions of rows.
Scalability refers to the ability of a system, network, or process to handle a growing amount of work, or it's potential to accommodate growth in data volume and complexity without compromising performance or efficiency. Design your models to handle this growth by considering:
- Flexibility: Adapting to data volume changes while maintaining acceptable report performance.
- Reduced complexity: Ensuring models are less complex and manageable.
Scalable semantic models enable organizations to analyze and report on large, complex data sources with ease. Microsoft Fabric makes it possible to work with high volume and large-scale data with the right groundwork in place. A scalable semantic model allows for an optimal consumer experience in Power BI reports.
Imagine you're on the analytics team at a major e-commerce company, preparing for the biggest annual sales event. Previous reporting solutions were manual and didn't scale. Now you're tasked with improving performance using semantic models in Microsoft Fabric for downstream analytics and Power BI reports.
By the end, you'll be able to choose a model framework, design a star schema, and apply best practices to create a semantic model optimized for large-scale data analytics with Power BI.