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Project description
Fabric catalog accelerates Microsoft Fabric adoption with ready-to-run accelerators, demos, and tutorials that install directly into your workspace in minutes via
fabric-cicd.
Install the Library
Requirements: Python 3.10–3.13 and access to a Microsoft Fabric workspace.
pip install msfabric-solution-catalog
List and Install a catalog
Run inside a Fabric notebook (or any Python environment with Fabric credentials):
import msfabric_solution_catalog as catalog
# Renders an interactive catalog
catalog.list()
# Copy the install command from the catalog, past in another cell and run!
catalog.install("stateful-streaming-lakehouse")
Notes
workspace_idis optional when you run in a Fabric notebook; it auto-detects the current workspace. Specify to deploy to another target workspace.install()accepts extras likeitem_prefixandunattended=Trueif you prefer console logs over HTML output.- catalogs that include file upload configuration will automatically upload small data files to a Lakehouse's Files area after deployment — no extra arguments needed.
Handling Name Conflicts
If items with the same name already exist in your workspace, Fabric catalog will detect conflicts and provide resolution options:
-
Overwrite existing items:
catalog.install("spark-structured-streaming", overwrite=True)
-
Auto-generate a prefix to avoid conflicts:
catalog.install("spark-structured-streaming", auto_prefix_on_conflict=True)
This generates a prefix like
js3_sss__(catalog ID + abbreviated name) and applies it to all deployed items. -
Provide a custom prefix:
catalog.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
Testing a catalog Before Registration
Use _install_from_github() to test a catalog directly from a GitHub repo before adding it to the registry. This method builds a synthetic config from the arguments you provide and runs the same install pipeline as install().
import msfabric_solution_catalog as catalog
catalog._install_from_github(
logical_id="my-catalog", # sets name of root folder that items are deployed to
repo_url="https://github.com/my-org/my-repo",
repo_ref="v1.0.0", # tag or commit SHA — not a branch
workspace_path="my-catalog/", # defaults to "{logical_id}/"
entry_point="GettingStarted.Notebook",
items_in_scope=["Lakehouse", "Notebook"],
workspace_id="<guid>", # target workspace (auto-resolves to the current ws in Fabric)
)
Common optional parameters:
catalog._install_from_github(
logical_id="my-catalog",
repo_url="https://github.com/my-org/my-repo",
repo_ref="abc1234",
entry_point="GettingStarted.Notebook",
items_in_scope=["Lakehouse", "Notebook", "SQLEndpoint"],
workspace_path="my-catalog/", # defaults to "{logical_id}/"
name="My catalog", # display name (defaults to logical_id)
workspace_id="<guid>", # target workspace (auto-detected in Fabric)
files_source_path="my-catalog/data/", # upload binary/data files after deploy
files_destination_lakehouse="MyLakehouse", # target Lakehouse for file upload
files_destination_path="raw/", # destination path in Lakehouse Files
item_prefix="test_", # prefix all deployed item names
unattended=True, # console output instead of HTML
)
Once the catalog installs successfully, add a YAML file to msfabric_solution_catalog/catalogs/ and switch to the standard catalog.install() flow.
Contributing
See the root contributing guide for shared guidelines (commit conventions, issue workflow, PR process), then follow the Python library contributing guide for development setup, quality checks, and the new catalog workflow.
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