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Getting Started with Materialized Lake Views

Overview

Build a bronze-to-gold medallion pipeline using materialized lake views in a Fabric lakehouse. Create source tables, define materialized views with automatic refresh, and explore lineage tracking.

Class
Core
Type
Tutorial
Difficulty
Beginner
Deploy Time
~2 min
Complete Time
~15 min

Workloads

Data Engineering

Fabric Items Deployed

  • Lakehouse
  • Notebook

Scenarios

ModelingBatch Processing

This jumpstart deploys a complete bronze → silver → gold medallion pipeline using materialized lake views in a Microsoft Fabric lakehouse. Run the tutorial notebook to create source tables, define materialized views with automatic refresh, and explore lineage tracking.

Getting Started

After installing the jumpstart, open the mlv_get_started notebook and run each cell in order. The notebook is fully self-contained and creates all schemas, tables, and materialized views.

Architecture

The jumpstart implements the medallion architecture with three layers:

Layer Schema Object Description
Bronze bronze products, orders Raw source tables with sample data
Silver silver cleaned_order_data (MLV) Joins orders with products, calculates per-order revenue
Gold gold product_sales_summary (MLV) Aggregates by product — total quantity, revenue, avg order value

How It Works

  1. Source tables are created in the bronze schema with sample product and order data
  2. Change Data Feed (CDF) is enabled on source tables so Fabric can use incremental refresh
  3. Materialized lake views are defined in silver and gold schemas using CREATE MATERIALIZED LAKE VIEW syntax
  4. Automatic refresh keeps views up-to-date as source data changes — Fabric determines the optimal strategy (incremental, full, or skip) based on the lineage graph

Prerequisites

  • A workspace with a Microsoft Fabric-enabled capacity
  • A lakehouse with lakehouse schemas enabled and Fabric Runtime 1.3

Resources