Boosting Lakehouse Performance with Materialized Lake Views in Microsoft Fabric

Running repeated queries on large datasets can quickly become a performance bottleneck. Imagine a sales dashboard that recalculates totals across millions of rows every time it refreshes. That’s not only slow—it’s costly.

This is where Materialized Lake Views (MLVs) in Microsoft Fabric Lakehouse come in. They allow you to precompute query results and store them in the Lakehouse for reuse, delivering faster query execution, lower compute costs, and better governance.

What is a Materialized Lake View?

A materialized lake view is a persisted snapshot of a query’s result, stored as Delta tables in your Lakehouse. Unlike standard views, which run the query from scratch every time, an MLV saves the data and only needs refreshing when your source tables change.

Key benefits:

• Performance boost – Precomputed results mean faster queries.

• Data governance – Lineage, run history, and monitoring are built in.

• Data quality – You can enforce rules to keep bad data out.

Creating Your First Materialized Lake View

Let’s walk through a simple example.

Suppose you want a quick view of total orders per customer. Normally, you’d query the Orders table directly. But with a materialized lake view, you can persist this summary once and reuse it.

Example -

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

General Syntax -

CREATE MATERIALIZED LAKE VIEW [IF NOT EXISTS][workspace.lakehouse.schema].MLV_Identifier
[(
	CONSTRAINT constraint_name1 CHECK (condition_expression1) [ON MISMATCH DROP | FAIL],
	CONSTRAINT constraint_name2 CHECK (condition_expression2) [ON MISMATCH DROP | FAIL]
)]
[PARTITIONED BY (col1, col2, ...)]
[COMMENT "description or comment"]
[TBLPROPERTIES ("key1"="val1", "key2"="val2", ...)]
AS
select_statement;

Once created, you can query it just like a table and instead of recalculating totals each time, Fabric fetches results instantly.

Keeping Data Fresh: Refresh Options

Materialized views need updates when the underlying data changes.

Fabric supports:

• Manual Refresh -

REFRESH MATERIALIZED LAKE VIEW CustomerOrderSummary;

• Scheduled Refresh - You can configure refreshes from the Fabric UI, go to Manage materialized lake views → Schedule, turn On schedule and configure:

  • Repeat: by minute / hourly / daily / weekly / monthly

  • Every: choose frequency/date/time/month

  • Start date, End date/time, Time zone

Managing and Monitoring MLVs

Lineage

Fabric automatically maps out how your materialized views connect to their source tables and downstream reports. This gives you a clear picture of data flow—perfect for impact analysis and governance.

Run History & Operational Details

You can view the last 25 runs (or past 7 days, whichever comes first) for quick monitoring/troubleshooting from the lineage page dropdown. Possible run states:

• Completed – all nodes succeeded

• Failed – at least one node failed; children show Skipped

• Skipped – not executed due to upstream failures

• In Progress – running now

• Canceled – manually canceled in Monitoring hub

Data Quality

You can enforce quality rules when creating an MLV. For example, to ensure customer names are never null

CREATE MATERIALIZED LAKE VIEW ValidCustomers
(
	CONSTRAINT cust_name_not_null CHECK (CustomerName IS NOT NULL) ON MISMATCH DROP
)
AS
SELECT CustomerID, CustomerName, City
FROM Customers;

• FAIL (default): stop the refresh at the first violation.

• DROP: continue and remove violating rows; lineage shows count of rows dropped.

• If both are specified, FAIL wins.

This ensures only right data goes to the enriched layer.

Fabric also provides Data Quality Reports in Power BI, showing where issues occur. From Manage materialized lake views, select Data quality report. The first time, click Generate report—Fabric creates a semantic model and a Power BI report in your workspace and embeds it on the page.

Why Materialized Lake Views Matter

Here’s what makes MLVs powerful in real-world projects:

1. Faster dashboards and analytics – Queries hit precomputed data.

2. Lower costs – Refresh only what changed instead of recalculating everything.

3. Improved governance – Lineage, run history, and monitoring give visibility.

4. Trustworthy data – Integrated quality checks ensure accuracy.

Conclusion

Materialized Lake Views in Microsoft Fabric Lakehouse are more than just a performance hack—they’re a strategic tool for building reliable, governed, and high-performing data solutions.

By combining faster queries, governance features, and data quality checks, they simplify data engineering workflows and deliver trustworthy analytics at scale.

So the next time you’re designing a pipeline or optimizing a dashboard, consider asking: “Should this be a materialized lake view?”