Steve Campbell

Power BI Desktop shines with the right tooling. This workshop dives into external tools, governance services, modern modeling features, and scalable self-service patterns so you can build reliable, high-quality semantic models end to end.

This session breaks down the practical decisions behind designing and implementing a AI ready modern data platform, without diving into heavy technical detail. A clear, non-technical roadmap from two Microsoft MVPs who run a data consultancy.

Build a Lakehouse using Microsoft Fabric and create a Medallion Architecture pipeline. Use Pipelines, Spark Notebooks, T-SQL, and create orchestration and monitor pipelines for optimal performance.

Learn which tools in Fabric to use and when. No coding knowledge required. This session will cover: - Delta, Parquet, and Onelake - SQL vs. Spark: - Notebooks vs. Dataflows vs. Pipelines - Warehouses vs. Lakehouses:

A showcase of how report-scoped objects like measures, calc groups, field parameters, custom calendars, and visual calcs boost flexibility and insight, and how composite models let you add this logic without polluting the core semantic model.

Learn how to design and maintain a clean, scalable Power BI semantic model that supports growth and self-service. We’ll cover what belongs in the core model vs. the report layer, how to extend safely with composite models, when to use UDFs vs. calculation groups, and how tools like Tabular Editor, Semantic Link, and the Power BI MCP server can automate refactoring and model governance.

This course teaches users to create, manage, and secure enterprise-grade Power BI models, covering semantic modelling, deployment pipelines, sharing strategies, and access control, enabling scalable, collaborative, and secure analytics.