Introduction

Data engineers often face performance challenges when querying large datasets repeatedly. Running the same transformations or aggregations each time a query is executed consumes resources and increases query time.

Microsoft Fabric introduces Materialized Views in the Lakehouse to address this challenge.

A materialized view stores the results of a query physically in the Lakehouse, so future queries can fetch precomputed data instead of recalculating it. This approach ensures:

• Faster query performance
• Efficient resource usage
• Simplified workflows for analytics and reporting.

What is a Materialized View

A materialized view is a persisted version of a query’s results. Unlike a standard SQL view, which acts as a logical layer on top of base tables, a materialized view stores its results in Delta tables inside the Lakehouse.

Key Characteristics:

• Precomputed results – Results are stored instead of recalculated on every query.
• Refresh mechanism – Keeps data updated through manual or scheduled refresh.
• Integration with monitoring and lineage – Provides transparency into data dependencies and refresh health.

Use Cases:

• Aggregations on large transaction tables (e.g., daily sales summaries).
• Frequently accessed reference datasets.
• Scenarios where incremental refresh improves efficiency.

How Materialized Views Work

When a materialized view is created:

1. Fabric executes the defining query.
2. The results are stored in Delta tables inside the Lakehouse.
3. Users query the view just like a table.
4. To stay current, the view must be refreshed.

Refresh Modes:

• Full Refresh – Recomputes the entire view based on the source data.
• Incremental Refresh – Updates only changed data, reducing overhead.

Example

Imagine a large Sales table with billions of rows. Instead of running a query like this repeatedly:

SELECT ProductID, SUM(SalesAmount) AS TotalSales
FROM Sales
GROUP BY ProductID;
You create a materialized view:
CREATE MATERIALIZED VIEW SalesSummary
AS
SELECT ProductID, SUM(SalesAmount) AS TotalSales
FROM Sales
GROUP BY ProductID;

Now, queries against SalesSummary return results instantly because the aggregation is precomputed.

Creating a Materialized View

Prerequisites

• A Microsoft Fabric Lakehouse workspace.
• Access to the SQL Analytics endpoint of your Lakehouse.

Steps to Create

Step 1: Open SQL Analytics Endpoint
Navigate to your Lakehouse and select SQL Analytics.

Step 2: Write the CREATE Statement

CREATE MATERIALIZED VIEW CustomerOrderSummary
AS
SELECT CustomerID, COUNT(OrderID) AS TotalOrders, SUM(OrderAmount) AS TotalSpent
FROM Orders
GROUP BY CustomerID;

Step 3: Verify Creation
The materialized view appears under Materialized Views in your Lakehouse Explorer.

Step 4:Verify Creation

SELECT * FROM CustomerOrderSummary;

This query is fast because results are pre-stored.

Managing Materialized Views

1. Refreshing Views

• Manual Refresh – Run:

REFRESH MATERIALIZED VIEW CustomerOrderSummary;

• Scheduled Refresh – Configure in Fabric to keep the view updated automatically

2. Linage Tracking 

Fabric provides a lineage view to trace relationships between materialized views and their source datasets.

• Helps identify dependencies.
• Useful when debugging data issues.
• Ensures compliance by showing data flow.

3. Scheduling Lineage Runs

You can schedule lineage refreshes to automatically capture dependencies at regular intervals. This is critical in environments with frequent schema or data changes.

4. Run History

Every refresh is logged with details:

• Status – success or failure.
• Duration – execution time.
• Errors – failure reasons.

This history enables auditing and troubleshooting.

5. Operational Run Details

Detailed execution logs are available for each refresh.

• Check number of rows updated.
• Identify performance bottlenecks.
• Track incremental vs. full refresh usage.

Data Quality in Materialized Views

Ensuring data quality is critical in analytics pipelines. Fabric integrates data quality checks with materialized views.

Features:

• Validation – Data in views can be validated against defined quality rules.
• Reports – Fabric generates Data Quality Reports, highlighting issues like nulls, duplicates, or schema mismatches.
• Governance – Ensures downstream analytics are built on reliable, clean data.

Monitoring Materialized Views

Fabric provides monitoring tools to oversee materialized views:

• Refresh Progress – Check if a refresh is running or completed.
• Health Status – Alerts for failed refreshes.
• Performance Metrics – Analyze execution times and optimize queries.

Monitoring ensures views remain up-to-date and efficient, reducing risk of stale or inconsistent data.

Advantages of Materialized Views in Fabric

1. Performance Gains
   Precomputed results mean queries run much faster.
2. Cost Efficiency
   Incremental refresh reduces compute overhead.
3. Simplified Analytics
   Reusable materialized views minimize repetitive SQL transformations.
4. End-to-End Governance
   Lineage, run history, and monitoring ensure transparency.
5. Reliable Data Quality
   Integrated validation and reporting provide trustworthy datasets.

Best Practices

• Use incremental refresh wherever possible to save resources.
• Regularly check lineage before making schema changes.
• Schedule refreshes during low-traffic hours to avoid resource contention.
• Monitor run history and data quality reports to ensure accuracy.

Conclusion

Materialized views in Microsoft Fabric Lakehouse are a powerful feature for improving query performance, ensuring data quality, and simplifying engineering workflows.

With built-in capabilities for lineage, monitoring, scheduling, and data validation, they are an essential tool for modern data engineering teams.

By incorporating materialized views into your Lakehouse strategy, you enable faster analytics, stronger governance, and more efficient data operations.

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