Reinventing the Gold Layer Using Materialized Lake Views

[pallavi_r]

1. Foundations of the Medallion Architecture

As we know, modern analytics platforms must balance scale, reliability, and governance to process data from source to target. The Medallion Architecture addresses this by organizing data into bronze, silver, and gold layers, each with a well-defined responsibility. This layered approach separates raw ingestion from transformation and business-ready data, making analytics solutions methodical and easier to manage, govern, and scale across the enterprise.

2. Why Traditional Gold Tables Break Under Scale

As business reporting requirements evolve over time, the reporting layer expands to support multiple teams. This makes it increasingly difficult to manage and maintain the gold layer, especially as dependencies on gold tables and warehouses grow.

Refresh schedules, orchestration, and interdependencies become tightly coupled, meaning changes in source systems or upstream data can directly impact the gold layer. This complexity makes lineage harder to track and often leads to confusion around data ownership and trust.

And, as the number of data sources increases, the operational effort rises significantly affecting deployment speed, testing, monitoring, and the long-term maintainability of the analytics platform.

Over time, the gold layer became difficult to manage and govern. Teams spent more time handling complexity than delivering insights. This highlights the need for the gold layer to evolve into a more flexible and reusable consumption layer—one that can scale with growing business needs while keeping operational effort under control.

3. Materialized Lake Views in Microsoft Fabric

Microsoft Fabric introduces Materialized Lake Views (MLVs) as a built-in capability to simplify how curated data is prepared and consumed. Materialized Lake Views store precomputed results of SQL queries on top of Lakehouse data, combining the flexibility of views with the performance benefits of materialized data. They can be refreshed automatically and reused across multiple analytical workloads, making them well suited for enterprise-scale analytics.

4. How Materialized Lake Views Address This

Materialized Lake Views reduce complexity by centralizing business logic into reusable, precomputed views instead of multiple physical gold tables. Rather than creating separate tables for each reporting requirement, teams can define standardized aggregations once and reuse them across dashboards and analytical workloads.

When business rules change, updates can be made in a single place and automatically reflected wherever the view is used. This significantly reduces maintenance effort, minimizes deployment cycles, and lowers the risk of inconsistent metrics across reports.

5. Where Materialized Lake Views Fit in the Medallion Architecture

In the Medallion Architecture, Materialized Lake Views (MLVs) fits best between the silver and gold layers. Silver tables provide clean, conformed, and trusted data, while MLVs use SQL to precompute and shape this data for consumption—without creating extra physical gold tables.

Instead of building a separate gold table for every business requirement, MLVs act as reusable, precomputed consumption layers on top of silver data. They deliver the performance of gold tables while staying closely aligned with Silver, reducing duplication and simplifying governance.

6. Materialized Lake Views in a Metadata-Driven Design (MDD) Approach

In a metadata-driven design, ingestion and transformation processes are controlled through configuration rather than hard-coded logic. Metadata tables typically define source systems, load patterns, schedules, and target locations for bronze and silver layers. Materialized Lake Views do not replace this ingestion metadata; instead, this extends the metadata-driven approach into the consumption layer.

By standardizing business logic and aggregations at the view level, Materialized Lake Views allow teams to manage analytical outputs in a consistent and reusable manner. While ingestion metadata determines what data is loaded and where it lands, Materialized Lake Views define how curated data should be consumed.

In large, multi-source environments, this separation of concerns simplifies maintenance. Changes to source systems continue to be managed through ingestion metadata, while changes to business logic or aggregations are handled centrally through Materialized Lake Views. This makes the overall platform easier to scale, govern, and evolve as analytical requirements grow.

Design Approach for MDD-MLV driven medallion architecture 

Start from Bronze ingestion (with metadata tables), refine to Silver (with mdd), then aggregate to Gold MLVs for optimized Delta persistence. Schedule full/incremental modes via Fabric's UI, integrating data quality rules for governance.

MDD–MLV Implementation

A sample materialized view

CREATE MATERIALIZED LAKE VIEW IF NOT EXISTS ecommerce_lh.gold.mv_monthly_salesbycategory

(

CONSTRAINT chk_amt_positive

    CHECK (total_amount > 0) ON MISMATCH DROP

)

AS

SELECT

    category,

    MONTH(order_date) AS month,

    SUM(total_amount) AS total_amount

FROM ecommerce_lh.silver.cleaned_ecommerce

GROUP BY

    category,

    MONTH(order_date)

ORDER BY

    category,

    month;

7. Gold Aggregation Design Patterns with Materialized Lake Views

Materialized Lake Views (MLVs) support multiple proven design patterns for creating Gold-level aggregations without the need to build and maintain numerous physical tables. Instead of duplicating data and logic, commonly used aggregations are defined once and reused across reports and analytical workloads.

• Time-Based Aggregations: Daily, monthly, yearly summaries precomputed once and reused across reports.
• KPI & Metrics: Centralized enterprise KPIs (revenue, orders, growth) for consistent, fast reporting.
• Domain-Specific Views: Sales, Finance, Supply Chain, Marketing views summarized by key dimensions for cross-team analysis.

8. Materialized Views vs Gold data warehouse

Here’s a quick comparison between Materialized Views and Gold Tables:

Materialized Views	Gold Tables
Best suited for fast-changing business metrics	Best suited when schema stability is required
Help reduce Gold table sprawl	Support lifecycle management
Simplify pipelines and reduce orchestration complexity	Require dedicated pipelines and orchestration
Well suited for Power BI and Fabric-native analytics	support external BI tools or applications
Flexible and agile	Enforce strict dimensional modelling

9. Benefits

Materialized Lake Views optimize analytics by simplifying architecture, reducing operational overhead, and accelerating insights while ensuring governed, up-to-date data.

• Reduced Gold tables: Minimizes the need for multiple physical Gold tables by centralizing transformations in MLVs.
• Lower maintenance effort: Minimizes pipelines and absorbs business or source changes in a single place.
• Automatic lineage: Provides built-in, end-to-end lineage without additional governance overhead.
• Faster analytics: Delivers quicker dashboard and report performance using precomputed results.
• Fresh data: Keeps analytics up to date through managed, incremental refreshes.
• Better performance & scalability: Serves optimized data directly from the Lakehouse for responsive, scalable Power BI workloads.
• Provides inbuilt monitoring, quality reports, visual monitoring of runs

10. Conclusion

Materialized Lake Views (MLVs) extend the Medallion architecture by simplifying the design and consumption of the gold layer. When combined with a metadata-driven approach, MLVs enable faster development, easier change management, and high-performance analytics at scale. This makes it a practical, scalable choice for organizations using Microsoft Fabric to meet evolving business needs while maintaining strong governance and performance.

By calculating transformations once during refresh, MLVs eliminate repeated, expensive queries across BI reports or notebooks—shifting compute from on-demand CU usage to predictable, scheduled processing.