github/awesome-copilot/skills/fabric-lakehouse/SKILL.md
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.
Decision brief
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 Lake…
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/github/awesome-copilot --skill "skills/fabric-lakehouse"Inspect the Agent Skill "fabric-lakehouse" from https://github.com/github/awesome-copilot/blob/9933dcad5be5caeb288cebcd370eeeb2fc2f1685/skills/fabric-lakehouse/SKILL.md at commit 9933dcad5be5caeb288cebcd370eeeb2fc2f1685. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
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:
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:
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:
Delta Tables: Managed tables with ACID compliance and schema enforcement
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 store…
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 78/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 37,126 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Use this skill when you need to:
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:
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).
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 are stored under "Files" folder. Users can create folders and subfolders to organize their files. Any file format can be stored in Lakehouse.
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.
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.
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.
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.
Shortcuts create virtual links to data without copying:
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.
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.
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.
See PySpark code for details.
See Get data for details.
Alternatives
coreyhaines31/marketingskills
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program
event4u-app/agent-config
Grounded design brief from the adopted corpus — style, WCAG-checked color tokens, typography, layout pattern, anti-patterns. Use on ui-design-brief or any which-style/palette/font/chart decision.
event4u-app/agent-config
Use BEFORE writing or editing any non-trivial UI — inventories components, design tokens, shadcn primitives, and reusable patterns into state.ui_audit. Hard gate for the ui directive set.
event4u-app/agent-config
Use BEFORE writing/changing tests, adding mocks, or test-only methods on production classes — vs mocking-the-mock, production pollution, partial mocks, and overfit/tautological assertions