Whether you’re just getting started with version control or looking to scale automated deployments in Power BI and Microsoft Fabric, this full-day workshop will equip you with the skills and mindset to implement a professional CI/CD pipeline in your data platform projects.

In the first half of the day, we’ll guide you from zero to a fully functional CI/CD setup for Power BI projects using GitHub. You’ll learn how to:

• Use Power BI project files (.PBIP) for version control.
• Set up repositories and branching strategies.
• Collaborate on features, resolve merge conflicts, and revert to previous versions.
• Implement quality checks using Best Practice Analyzer and other tools to improve governance.

In the second half, we’ll shift our focus to other Microsoft Fabric ittems. You’ll:

• Learn how to configure source control for Fabric workspaces.
• Explore the Fabric CI/CD experience and Fabric CLI to script deployments.
• Implement automated testing, approvals and post-deployment housekeeping to support enterprise-level workflows.
• Expect a balanced mix of hands-on labs, real-world scenarios, and strategic insights to help you scale your data development processes with confidence.

Who should attend?

This workshop is for Power BI and Fabric developers, BI architects, and data platform engineers interested in:

1. Adopting version control in their data workflows.
2. Setting up CI/CD pipelines from scratch.
3. Moving toward enterprise-grade automation in Fabric.

No prior CI/CD experience is required — we start from the basics and gradually dive into advanced use cases.

Organizations are increasingly adopting lakehouse architectures to unify analytics and machine learning workloads. However, many implementations suffer from poor foundational decisions that become costly to remediate later. This full-day, hands-on workshop provides practical guidance for architecting, deploying, and operationalizing a robust lakehouse on Microsoft Fabric—ensuring scalability, security, and maintainability from day one.

We begin with architectural planning, where participants learn to define business requirements, design governance and security models, select data storage strategies, and plan for scalability, performance, and cost optimization. The focus is on building blueprints and frameworks that prevent common pitfalls and support long-term success.

Next, we explore Infrastructure as Code (IaC) using Terraform to create repeatable, version-controlled deployments. Participants will set up CI/CD pipelines, build reusable modules, and configure Fabric workspaces for Git integration to ensure traceability and collaboration.

The implementation phase takes these designs into practice: deploying the lakehouse foundation, configuring ingestion pipelines for batch and streaming workloads, applying cataloging and metadata management, and validating data quality and security controls. We also cover Fabric’s variables, environments, and deployment rules, showing how to promote changes safely from development to production with CI/CD.

The day concludes with an interactive Ask Me Anything session, where participants revisit core concepts, raise outstanding technical or strategic questions, and clarify how the lessons apply in their own environments.

Key takeaways: attendees will leave with production-ready templates, architectural blueprints, and operational playbooks they can immediately apply within their organizations.

Prerequisites: Basic cloud and data engineering knowledge, with familiarity in IaC and CI/CD concepts. Terraform or Git experience is helpful but not required.

DAX is a simple language (but not easy at all). Its five pillars, though, need to be clearly understood in order not to get confused when they, as inevitably happens, interact between them in your measures, calculated columns or calculated tables.

In this session the five DAX pillars (filter context, row context, context transition, iterations and expanded tables) will be explained, with examples, one by one first and then in interaction between them. Examples will be in increasing complexity order.