22-25 April 2026 
Reid Havens
havensconsulting.net/blog-and-media
Reid is the Founder of Havens Consulting Inc, Microsoft MVP, and an avid Business Intelligence evangelist.
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SQLBits 2025
Well-Architected Fabric Solution: A Medallion First Approach - Part 2
Transform your data solutions with a streamlined Medallion Architecture using Microsoft Fabric. This session is tailored for professionals familiar with Power BI and basic dataflows, providing a step-by-step guide to implementing Bronze, Silver, and Gold layers for a scalable and maintainable pipeline (Part 2).
Transform your data solutions with a streamlined Medallion Architecture using Microsoft Fabric. This session is tailored for professionals familiar with Power BI and basic dataflows, providing a step-by-step guide to implementing Bronze, Silver, and Gold layers for a scalable and maintainable pipeline.
Learn how to evolve an unstructured dataflow and semantic model into a comprehensive architecture. Familiarity with Python or SQL is a bonus but not required.
Part 1:
• Overview of Medallion Architecture: Gain a clear understanding of Warehouses and Lakehouses, their role within Microsoft Fabric, and how they enable Medallion Architecture.
• Understanding OneLake: Dive into OneLake and explore its foundational storage structure, including Delta tables and Parquet. Understand key features like columnar storage, Delta optimizations, and performance enhancements through simple, no-code explanations.
Part 2:
• Feature Showdown: Compare and contrast key tools in Microsoft Fabric, such as SQL vs. Spark, Notebooks vs. Dataflows vs. Pipelines, and Warehouses vs. Lakehouses, to determine the best fit for your scenarios.
• Well-Designed Architecture & Best Practices:
Learn about GIT integration, monitoring techniques, and actionable best practices for designing scalable and maintainable data architectures.
Audience:
• BI/SQL Professionals looking to broaden their knowledge of data engineering.
• Data Analysts seeking to deepen their understanding of data architecture.
• Aspiring Data Engineers aiming to master scalable data pipeline architectures.
Prerequisites:
Basic familiarity with Power BI and fundamental data engineering principles. Knowledge of Python or SQL is helpful but not mandatory.
Learning Goals:
1. Step-by-step guidance on converting unstructured dataflows into a robust Medallion Architecture.
2. Clear decision-making insights on tool selection and building well-designed architectures.
3. Practical strategies for scaling and maintaining pipelines with best practices in data engineering.
Learn how to evolve an unstructured dataflow and semantic model into a comprehensive architecture. Familiarity with Python or SQL is a bonus but not required.
Part 1:
• Overview of Medallion Architecture: Gain a clear understanding of Warehouses and Lakehouses, their role within Microsoft Fabric, and how they enable Medallion Architecture.
• Understanding OneLake: Dive into OneLake and explore its foundational storage structure, including Delta tables and Parquet. Understand key features like columnar storage, Delta optimizations, and performance enhancements through simple, no-code explanations.
Part 2:
• Feature Showdown: Compare and contrast key tools in Microsoft Fabric, such as SQL vs. Spark, Notebooks vs. Dataflows vs. Pipelines, and Warehouses vs. Lakehouses, to determine the best fit for your scenarios.
• Well-Designed Architecture & Best Practices:
Learn about GIT integration, monitoring techniques, and actionable best practices for designing scalable and maintainable data architectures.
Audience:
• BI/SQL Professionals looking to broaden their knowledge of data engineering.
• Data Analysts seeking to deepen their understanding of data architecture.
• Aspiring Data Engineers aiming to master scalable data pipeline architectures.
Prerequisites:
Basic familiarity with Power BI and fundamental data engineering principles. Knowledge of Python or SQL is helpful but not mandatory.
Learning Goals:
1. Step-by-step guidance on converting unstructured dataflows into a robust Medallion Architecture.
2. Clear decision-making insights on tool selection and building well-designed architectures.
3. Practical strategies for scaling and maintaining pipelines with best practices in data engineering.
