Managing the Semantic Model Development Lifecycle with Git and Pipelines

As data landscapes grow, keeping your semantic models organized, version-controlled, and error-free becomes a critical challenge. Transitioning from manual updates to a structured development lifecycle is the key to scaling your data operations and ensuring trust in your analytics. This course equips you with the foundational knowledge to manage semantic models professionally from initial creation to production deployment. You will learn how to implement modern development practices, establish robust version control, and automate your release workflows to prevent breaking changes. What you'll learn: - Understand foundational semantic model concepts, architectures, and lifecycle stages. - Implement version control for your data models using Git to track changes and collaborate. - Validate and inspect model metadata using XMLA endpoints and SemPy. - Configure automated deployment pipelines to move models safely between environments. - Monitor model performance and perform impact analysis before making critical changes. - Apply modern CI/CD practices to ensure high-quality and reliable data delivery. The course begins with core definitions and lifecycle theory before guiding you through version control integration, programmatic validation, and pipeline deployment strategies. You will read clear explanations and review practical configuration examples to master these workflows. Designed for beginner data analysts, BI developers, and data engineers looking to adopt professional development workflows. No advanced programming or DevOps experience is required, as we start with foundational concepts. Start managing your data assets like a software engineer today.