Simplifying Medallion Implementation with Materialized Views in Fabric | DEM566
AI Summary
This video introduces the feature called Materialized Lake View in Fabric, designed to simplify medallion architecture for big data management. It explains how this feature allows data engineers to declaratively define complex data transformations, data quality constraints, and actionable rules all in SQL, reducing the need for custom code and manual pipeline management. The demo showcases how data from multiple sources can be ingested into a lakehouse as delta tables, then transformed through different layers (bronze, silver, gold) using materialized views that automatically refresh based on tracked lineage and schedules. It also highlights integrated monitoring tools that provide lineage visualization, status indicators, data quality trends, and alerts to catch and address data issues quickly. Upcoming enhancements include incremental refresh, file source support, PySpark compatibility, cross-lakehouse support, CI/CD for deployments, and resource provisioning configurations. Overall, this feature promises to make big data pipelines more declarative, observable, resilient, and easier to manage for analytics at scale.