fabric-lakehouse▌
github/awesome-copilot · updated Apr 8, 2026
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Microsoft Fabric Lakehouse storage for unified tabular and non-tabular data with Delta Lake, SQL analytics, and fine-grained security.
- ›Combines data lake flexibility with data warehouse management through Delta Lake format, ACID transactions, versioning, and SQL endpoints for T-SQL querying
- ›Organizes data via schemas (folders under Tables), shortcuts (virtual links to internal/external sources), and materialized views for optimized query performance
- ›Supports multiple data formats: De
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: Unstructured/semi-structured data in the Files section
- SQL Endpoint: Auto-generated read-only SQL interface for querying
- Shortcuts: Virtual links to external/internal data without copying
- Fabric Materialized Views: Pre-computed tables for fast query performance
Tabular data in a Lakehouse
Tabular data in a form of tables are stored under "Tables" folder. Main format for tables in Lakehouse is Delta. Lakehouse can store tabular data in other formats like CSV or Parquet, these formats are only available for Spark querying. Tables can be internal, when data is stored under "Tables" folder, or external, when only reference to a table is stored under "Tables" folder but the data itself is stored in a referenced location. Tables are referenced through Shortcuts, which can be internal (pointing to another location in Fabric) or external (pointing to data stored outside of Fabric).
Schemas for tables in a Lakehouse
When creating a lakehouse, users can choose to enable schemas. Schemas are used to organize Lakehouse tables. Schemas are implemented as folders under the "Tables" folder and store tables inside of those folders. The default schema is "dbo" and it can't be deleted or renamed. All other schemas are optional and can be created, renamed, or deleted. Users can reference a schema located in another lakehouse using a Schema Shortcut, thereby referencing all tables in the destination schema with a single shortcut.
Files in a Lakehouse
Files are stored under "Files" folder. Users can create folders and subfolders to organize their files. Any file format can be stored in Lakehouse.
Fabric Materialized Views
Set of pre-computed tables that are automatically updated based on a schedule. They provide fast query performance for complex aggregations and joins. Materialized views are defined using PySpark or Spark SQL and stored in an associated Notebook.
Spark Views
Logical tables defined by a SQL query. They do not store data but provide a virtual layer for querying. Views are defined using Spark SQL and stored in Lakehouse next to Tables.
Security
Item access or control plane security
Users can have workspace roles (Admin, Member, Contributor, Viewer) that provide different levels of access to Lakehouse and its contents. Users can also get access permission using sharing capabilities of Lakehouse.
Data access or OneLake Security
For data access use OneLake security model, which is based on Microsoft Entra ID (formerly Azure Active Directory) and role-based access control (RBAC). Lakehouse data is stored in OneLake, so access to data is controlled through OneLake permissions. In addition to object-level permissions, Lakehouse also supports column-level and row-level security for tables, allowing fine-grained control over who can see specific columns or rows in a table.
Lakehouse Shortcuts
Shortcuts create virtual links to data without copying:
Types of Shortcuts
- Internal: Link to other Fabric Lakehouses/tables, cross-workspace data sharing
- ADLS Gen2: Link to ADLS Gen2 containers in Azure
- Amazon S3: AWS S3 buckets, cross-cloud data access
- Dataverse: Microsoft Dataverse, business application data
- Google Cloud Storage: GCS buckets, cross-cloud data access
Performance Optimization
V-Order Optimization
For faster data read with semantic model enable V-Order optimization on Delta tables. This presorts data in a way that improves query performance for common access patterns.
Table Optimization
Tables can also be optimized using the OPTIMIZE command, which compacts small files into larger ones and can also apply Z-ordering to improve query performance on specific columns. Regular optimization helps maintain performance as data is ingested and updated over time. The Vacuum command can be used to clean up old files and free up storage space, especially after updates and deletes.
Lineage
The Lakehouse item supports lineage, which allows users to track the origin and transformations of data. Lineage information is automatically captured for tables and files in Lakehouse, showing how data flows from source to destination. This helps with debugging, auditing, and understanding data dependencies.
PySpark Code Examples
See PySpark code for details.
Getting data into Lakehouse
See Get data for details.
How to use fabric-lakehouse on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add fabric-lakehouse
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches fabric-lakehouse from GitHub repository github/awesome-copilot and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate fabric-lakehouse. Access the skill through slash commands (e.g., /fabric-lakehouse) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★69 reviews- ★★★★★Pratham Ware· Dec 20, 2024
Solid pick for teams standardizing on skills: fabric-lakehouse is focused, and the summary matches what you get after install.
- ★★★★★Valentina Sharma· Dec 20, 2024
Keeps context tight: fabric-lakehouse is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Hassan Garcia· Dec 16, 2024
Keeps context tight: fabric-lakehouse is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Meera Singh· Dec 12, 2024
fabric-lakehouse has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Nia Torres· Dec 8, 2024
Registry listing for fabric-lakehouse matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hassan Farah· Dec 4, 2024
fabric-lakehouse is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Mei Torres· Dec 4, 2024
Useful defaults in fabric-lakehouse — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Harper Tandon· Nov 27, 2024
Useful defaults in fabric-lakehouse — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aisha Khan· Nov 27, 2024
fabric-lakehouse is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Hassan Harris· Nov 23, 2024
Registry listing for fabric-lakehouse matched our evaluation — installs cleanly and behaves as described in the markdown.
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