Metadata-Version: 2.4
Name: fabric-jumpstart
Version: 0.1.3
Summary: Get started quickly with pre-built solutions, tutorials, demos, and accelerators—automated, high-quality, and open-source.
Project-URL: Repository, https://github.com/microsoft/fabric-jumpstart.git
Author: Microsoft Corporation
License-Expression: MIT
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: <=3.13,>=3.10
Requires-Dist: fabric-cicd==0.1.34
Requires-Dist: ipython>=8.20.0
Requires-Dist: pyyaml>=6.0.3
Description-Content-Type: text/markdown

# ⚡️Fabric Jumpstart CLI

[![PyPI version](https://img.shields.io/pypi/v/fabric-jumpstart)](https://pypi.org/project/fabric-jumpstart/)
[![Python](https://img.shields.io/pypi/pyversions/fabric-jumpstart)](https://pypi.org/project/fabric-jumpstart/)

Fabric Jumpstart accelerates Microsoft Fabric adoption with ready-to-run accelerators, demos, and tutorials that install directly into your workspace in minutes via [fabric-cicd](https://microsoft.github.io/fabric-cicd/latest/).

## Install the Library

Requirements: Python 3.10–3.12 and access to a Microsoft Fabric workspace.

```bash
pip install fabric-jumpstart
```

## List and Install a Jumpstart

Run inside a Fabric notebook (or any Python environment with Fabric credentials):

```python
import fabric_jumpstart as jumpstart

# Renders an interactive catalog
jumpstart.list()

# Copy the install command from the catalog, past in another cell and run!
jumpstart.install("spark-structured-streaming")
```

Notes
- `workspace_id` is optional when you run in a Fabric notebook; it auto-detects the current workspace. Specify to deploy to another target workspace.
- `install()` accepts extras like `item_prefix` and `unattended=True` if you prefer console logs over HTML output.

## Handling Name Conflicts

If items with the same name already exist in your workspace, Fabric Jumpstart will detect conflicts and provide resolution options:

1. **Overwrite existing items**:
   ```python
   jumpstart.install("spark-structured-streaming", overwrite=True)
   ```

2. **Auto-generate a prefix** to avoid conflicts:
   ```python
   jumpstart.install("spark-structured-streaming", auto_prefix_on_conflict=True)
   ```
   This generates a prefix like `js3_sss__` (jumpstart ID + abbreviated name) and applies it to all deployed items.

3. **Provide a custom prefix**:
   ```python
   jumpstart.install("spark-structured-streaming", item_prefix="demo_")
   ```

The prefixing strategy:
- Renames item directories (e.g., `MyNotebook.Notebook` → `js3_sss__MyNotebook.Notebook`)
- Updates all references to renamed items within configuration files
- Uses word-boundary matching to avoid double-prefixing if you re-run the same install
- Reuses existing prefixes from previous attempts to prevent `js3_sss__js3_sss__` patterns

## Contributing

See the [root contributing guide](../../CONTRIBUTING.md) for shared guidelines (commit conventions, issue workflow, PR process), then follow the [Python library contributing guide](CONTRIBUTING.md) for development setup, quality checks, and the new jumpstart workflow.
