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fabric-mlv

Automates Materialized Lake View creation, management and refresh in Microsoft Fabric using Spark/PySpark.

2|1|Updated May 5, 2026
Search Tags:#Automation#Spark SQL#PySpark#Fabric#Materialized Lake Views
One-click install
npx skills add https://github.com/wardawgmalvicious/claude-config --skill fabric-mlv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fabric-mlv
Source: https://github.com/wardawgmalvicious/claude-config/tree/main/skills/fabric-mlv
Command: npx skills add https://github.com/wardawgmalvicious/claude-config --skill fabric-mlv

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the creation, management, and refresh of Materialized Lake Views (MLVs) in Microsoft Fabric using Spark and PySpark, simplifying the process of materializing and refreshing declarative SQL/PySpark transformations that persist as Delta tables.

Core Features & Use Cases

  • MLV Creation: Automates the process of creating MLVs with Spark SQL and PySpark.
  • Management: Handles tasks like showing, altering, renaming, and dropping MLVs.
  • Refresh: Manages refresh strategies, including full, incremental, and skip refreshes.
  • Data Quality: Implements data quality rules and provides a data quality report.
  • Use Case: Streamline the creation of data pipelines and materialized datasets for reporting, without the need for complex orchestration.

Quick Start

Use the fabric-mlv skill to create a new MLV with the following Spark SQL command: CREATE MATERIALIZED LAKE VIEW silver.new_mlv AS SELECT * FROM bronze.source_table.

Frequently Asked Questions about fabric-mlv

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate Materialized Lake View creation in Microsoft Fabric?▼

Automate Materialized Lake View creation in Microsoft Fabric using Spark SQL and PySpark commands to materialize declarative transformations into persistent Delta tables, streamlining pipeline creation without complex orchestration.

Can I do an incremental refresh on a Materialized Lake View in Fabric?▼

Incremental refresh on a Materialized Lake View in Fabric is supported. You can manage full, incremental, and skip refresh strategies using PySpark to handle data updates efficiently.

Do I need a schema-enabled lakehouse for Materialized Lake Views?▼

A schema-enabled lakehouse is required for Materialized Lake Views. You also need Runtime 1.3 to execute the Spark SQL and PySpark scripts for creation, management, and lineage-driven scheduling.

How do I enforce data quality rules when refreshing Fabric Materialized Lake Views?▼

Enforce data quality rules during Fabric Materialized Lake View refreshes by implementing automated checks within PySpark, generating a data quality report to validate your declarative SQL transformations.

What is the best way to manage Materialized Lake View lifecycle in PySpark?▼

The best way to manage the Materialized Lake View lifecycle in PySpark is using automated scripts to handle showing, altering, renaming, and dropping views, alongside scheduled full or incremental refreshes.

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