How To

Implement Medallion Architecture
with Predictable Cost

on Microsoft Fabric?

Stop building messy data lakes.
Learn the proven Medallion pattern for enterprise-grade data and the best practices to managing Fabric capacity effectively from industry experts.

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1:00 PM – 2:00 PM AEST
27th November

Online

Speakers

Lead FHIR Trainer, ADHA | Ex-Microsoft National Health Lead

Data and AI Expert | Microsoft Certified Fabric Expert

On-Demand recording available after the event.

What we will cover?

  • Medallion Architecture: The Three-Layer Approach (Bronze, Silver, Gold)
  • Data Lineage and Auditability: establishing a transparent trail for every metric, making data governance simple and compliance checks straightforward.
  • Decoupling and Resilience: How Medallion Architecture breaks the dependency chain in pipelines.
  • AI-Readiness: The specific structure and quality standards that make your data automatically consumable by Fabric’s AI features, including Copilot.
  • Capacity Units (CUs) and Smoothing: Demystifying the core consumption metric and understanding how Fabric’s smoothing mechanism work.
  • Bursting and Throttling Your Capacity: Understanding when your capacity is temporarily exceeded (bursting) and the mechanisms that slow down workloads (throttling) to manage load, and, crucially, how to avoid them.
  • Storage vs. Compute Efficiency: Understanding how leveraging the Delta Lake format and proper data partitioning in your Medallion layers drastically reduces the compute CUs required to run queries and processes
  • TCO Optimization: overall impact of a well-architected Medallion pattern on your Total Cost of Ownership, demonstrating that investment in governance directly lowers operational spending.

Who should attend?

  • Data Architects & Engineers responsible for designing and implementing scalable data pipelines.
  • Data & Analytics Managers focused on delivering consistent, trustworthy reports and controlling team resources.
  • IT Directors & CIOs who require financial predictability and cost-effective utilization from their cloud data investments.
  • Business Intelligence Leads frustrated by data quality issues and seeking a standardized framework for data assets.

About Us