Agent skill · Data & Analytics

fabric-lakehouse

Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.

GitHub68,948★ · +463/wk · 2 repos on radarProfile →
copilotMIT
Install
npx skills add github/awesome-copilot --skill fabric-lakehouse --agent copilot

Same command for any agent — swap --agent for claude-code, codex, cursor.

Facts
Files in the skill folder: 3
SKILL.md size: 6 KB
Bundled scripts: none
Version: 1.0
Declared author: tedvilutis
Path: skills/fabric-lakehouse/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 37,432 · +281 this week
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# When to Use This Skill Use this skill when you need to: - Generate a document or explanation that includes definition and context about Fabric Lakehouse and its capabilities. - Design, build, and optimize Lakehouse solutions using best practices. - Understand the core concepts and components of a Lakehouse in Microsoft Fabric. - Learn how to manage tabular and non-tabular data within a Lakehouse. # Fabric Lakehouse ## Core Concepts ### What is a Lakehouse? Lakehouse in Microsoft Fabric is an item that gives users a place to store their tabular data (like tables) and non-tabular data (like files). It combines the flexibility of a data lake with the management capabilities of a data warehouse. It provides: - **Unified storage** in OneLake for structured and unstructured data - **Delta Lake format** for ACID transactions, versioning, and time travel - **SQL analytics endpoint** for T-SQL queries - **Semantic model** for Power BI integration - Support for other table formats like CSV, Parquet - Support for any file formats - Tools for table optimization and data management ### Key Components - **Delta Tables**: Managed tables with ACID compliance and schema enforcement - **Files**: U

What's inside
Steps it walks through
  1. Core Concepts
  2. What is a Lakehouse?
  3. Key Components
  4. Tabular data in a Lakehouse
  5. Schemas for tables in a Lakehouse
  6. Files in a Lakehouse
  7. Fabric Materialized Views
  8. Spark Views
  9. Security
  10. Item access or control plane security
  11. Data access or OneLake Security
  12. Lakehouse Shortcuts
  13. Types of Shortcuts
  14. Performance Optimization
Ships with 2 files
  • references/getdata.md
  • references/pyspark.md
More from awesome-copilot
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About this skill
What does the fabric-lakehouse skill do?

Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.

How do I install it?

Run `npx skills add github/awesome-copilot --skill fabric-lakehouse --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going