Get started with Weaver

Choose where to run Weaver and what to build first.

Install the same package for desktop Python or a Fabric Notebook. No separate Weaver platform, orchestration runtime or software licence is required.

Install the package

Weaver supports Python 3.13, as well as Python 3.11 and 3.12.

Install Weaver
python -m pip install --upgrade weaverstack
weaver --version

Check the installed version before running your first project.

Existing Fabric compute and workspace permissions still apply.

Installation details →

Work from your computer, Fabric Notebooks, or both

Desktop Python

Use your terminal, VS Code or any Python environment. Write locally and run Build, Load, Test and Health against Microsoft Fabric.

Fabric Notebook

Install the same Python package:

Notebook cell
%pip install weaverstack

Then:

Python
import weaver

Weaver can use the Notebook's existing workspace, Spark session and identity. No desktop installation is required.

For repeatable jobs, add Weaver to the attached Fabric Environment rather than relying on a session-scoped installation.

Both

Develop locally, run in Fabric, or move between the two. Your Weaver project doesn't need to be rewritten for a different execution environment.

Working between desktop and Fabric →

Choose an authentication method

Browser sign-in
For interactive development.
Azure CLI
When you're already authenticated with Azure.
Service principals
For unattended jobs and automation.
Fabric Notebook identity
When running inside your workspace.

No separate Weaver account or authentication service is required. Existing Fabric and Power BI permissions still apply.

Choose what you want to build

Each route below produces a working result for one Fabric item or native Power BI project.

Try the bundled Sales example

Use an existing disposable workspace in place of Analytics.

Terminal
weaver initialise --workspace Analytics --project-folder ./sales \
  --warehouse Curated --example Sales --non-interactive
cd sales
weaver workflow full

Lakehouse

Use Python or Spark SQL to turn files into managed Delta Tables.

Build your first Lakehouse pipeline →

Warehouse

Write ordinary T-SQL. Let Weaver create the Table and its loading machinery.

Build your first Warehouse pipeline →

Semantic Models

Deploy native TMDL, apply modelling conventions and validate calculations.

Deploy your first Semantic Model →

Reports

Deploy the same native Power BI Report project to different environments, with the correct Semantic Model binding.

Deploy a Power BI Report →

Start in a Power BI service workspace

With an existing Power BI service workspace and the appropriate licence and permissions, you can deploy native Semantic Models and Reports, apply modelling conventions and validate DAX calculations. No Fabric Lakehouse or Warehouse workloads required.

Use the native model or Report deployment route; Lakehouse and Warehouse work is optional.

Get started with your existing Power BI project →