skilld
All skills
github avatar

/fabric-lakehouse

@3b907f7 official
by githubgithub/awesome-copilot40k stars
5,040

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.

Ask your Agent

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/fabric-lakehouse

This session only. Nothing lands on disk.

referencesgetdata.md

≈268 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Data Factory Integration

Microsoft Fabric includes Data Factory for ETL/ELT orchestration:

  • 180+ connectors for data sources
  • Copy activity for data movement
  • Dataflow Gen2 for transformations
  • Notebook activity for Spark processing
  • Scheduling and triggers

Pipeline Activities

Activity Description
Copy Data Move data between sources and Lakehouse
Notebook Execute Spark notebooks
Dataflow Run Dataflow Gen2 transformations
Stored Procedure Execute SQL procedures
ForEach Loop over items
If Condition Conditional branching
Get Metadata Retrieve file/folder metadata
Lakehouse Maintenance Optimize and vacuum Delta tables

Orchestration Patterns

Pipeline: Daily_ETL_Pipeline
├── Get Metadata (check for new files)
├── ForEach (process each file)
│   ├── Copy Data (bronze layer)
│   └── Notebook (silver transformation)
├── Notebook (gold aggregation)
└── Lakehouse Maintenance (optimize tables)

Source: SKILL.md on GitHub

Third-party checks

1 warning16d5 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    This skill provides technical guidance and PySpark code templates for managing Microsoft Fabric Lakehouse environments. It covers data ingestion, optimization, and security best practices without any detected malicious behavior.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • Runlayer7mo

    3/3 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at 3b907f7. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 20 hours ago.

Activeupdated 8 months ago

Capability

metadata
{
  "author": "tedvilutis",
  "version": "1.0"
}

Topics

  • microsoft-fabric
  • lakehouse
  • data-warehouse
  • delta-lake
  • spark
  • pyspark
  • azure
  • data-management
  • onelake

README badge

README badge for github/awesome-copilot/fabric-lakehouse

What it does

Provides reference material on Microsoft Fabric Lakehouse architecture, including Delta table management, schemas, shortcuts to external data sources, and security controls. Use this skill to explain Lakehouse concepts, design data storage solutions, or optimize performance with V-Order and table compaction.

Generated from the current SKILL.md.

Frequently asked

What table formats does Fabric Lakehouse support?
Lakehouse primarily uses Delta format for managed tables with ACID compliance. It also supports CSV and Parquet formats for Spark querying, plus any file format in the Files section.
Can I reference data from outside Fabric without copying it?
Yes. Shortcuts create virtual links to external data sources including ADLS Gen2, Amazon S3, Google Cloud Storage, and Dataverse without duplicating the data.
How does row-level and column-level security work in Lakehouse?
Lakehouse supports fine-grained security through Microsoft Entra ID RBAC on OneLake, allowing you to restrict access to specific rows or columns in tables beyond workspace-level permissions.
What are Fabric Materialized Views and how do they differ from Spark Views?
Materialized Views are pre-computed tables automatically updated on a schedule, providing fast query performance for complex operations. Spark Views are logical virtual tables that don't store data but provide a query interface.
How can I improve query performance on Lakehouse tables?
Enable V-Order optimization on Delta tables for faster reads with semantic models, use the OPTIMIZE command to compact files and apply Z-ordering on specific columns, and run VACUUM to clean up old files.

Generated from the current SKILL.md. These answers refresh after source changes.