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Principal Program Manager, Microsoft Fabric CAT helping users and organizations build scalable, insightful, secure solutions. Blogs, opinions are my own and do not represent my employer.
We are working on migrating a bunch of models but are trying not to lose reporting content already built in the process. I tried to use this in the Service in a notebook, but I get an error on line 1 that it doesn't recognize "sempy_labs". Is this something for purchase or am I missing something?
Great read! Migrating semantic models across workspaces has always been a challenge, especially when maintaining consistency and governance. Semantic Link Labs seems like a game-changer in simplifying this process by enabling seamless connections between models without manual rework.
One key advantage is the ability to reuse curated metrics and definitions across multiple workspaces, reducing duplication and ensuring data consistency. This is especially useful for organizations managing multiple tenants or environments.
It would be interesting to see how Semantic Link Labs handles version control and model dependencies during migration. Does it support incremental updates or automated synchronization between workspaces?
Looking forward to more insights on best practices and real-world use cases! 🚀
This article provides a detailed guide on migrating a semantic model across workspaces using Semantic Link Labs in Microsoft Fabric. https://www.webdevelopmentcompanylahore.com/
Last week Microsoft released an open-source text embedding model called Harrier in three sizes- 270M, 0.6B and 27B. I have been testing it in my RAG pipeline and so far it has crushed all my metrics.
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Here is a quick script to copy a semantic model from one workspace to another in the same tenant, assuming you are contributor+ in both the workspaces. I tested this for a Direct Lake model but should work for any more other semantic model. This just copies the metadata (not the data in the model) so be sure to set up other configurations (RLS members, refresh schedule, settings etc.). That can also be changed programmatically, thanks to Semantic Link Labs, but I will cover that in a future post.
If it’s a Direct Lake model, the model will point to the lakehouse of the source model. You can point to another lakehouse with the same schema using this function. If you need to migrate an import model including the data in the semantic model, use backup and restore functions (needs ADLSGg2).
Steps:
Install semantic link labs
specify the source and target workspace and dataset ids
Configure data sources, parameters, RLS, other options as required
#!pip install semantic-link-labs --q #install SLL
import sempy_labs as labs
import sempy.fabric as fabric
import time
def migrate_semantic_model(source_workspace_id, source_dataset_id, target_workspace_id, target_dataset_name):
"""
Sandeep Pawar | fabric.guru | 02-10-2025
Migrates/copies a semantic model from a source workspace to a target workspace.
Parameters:
source_workspace_id (str): The ID of the source workspace.
source_dataset_id (str): The ID of the source dataset.
target_workspace_id (str): The ID of the target workspace.
target_dataset_name (str): The name of the target dataset.
"""
# current bim
bim_file = labs.get_semantic_model_bim(dataset=source_dataset_id, workspace=source_workspace_id)
# see if the model with the same name exists
datasets = fabric.list_datasets(workspace=target_workspace_id)
if not any(datasets['Dataset Name'].isin([target_dataset_name])):
# first a blank model
labs.create_blank_semantic_model(dataset=target_dataset_name, workspace=target_workspace_id)
# update blank with above bim
labs.update_semantic_model_from_bim(dataset=target_dataset_name, bim_file=bim_file, workspace=target_workspace_id)
print(f"Semantic model '{target_dataset_name}' has been created and updated in the target workspace.")
else:
print(f"Semantic model with the name '{target_dataset_name}' already exists in the target workspace.")
datasets.query('`Dataset Name` == @target_dataset_name')
display(datasets.query('`Dataset Name` == @target_dataset_name'))
source_workspace_id = "d70c1ed7--911f-3300a13145ff"
source_dataset_id = "368628e2--b089-87b3b84d93ec"
target_workspace_id = "e127c20c--a8d0-0947b7b87337"
target_dataset_name = "migrated_dataset"
migrate_semantic_model(source_workspace_id, source_dataset_id, target_workspace_id, target_dataset_name)
Update : 2/11/2025
You can also use labs.deploy_semantic_model() method in SLL. Thanks Amund.
Gerhard Brueckl (MVP) reminded me that Fabric Studio can do this with a simple drag and drop. Watch his session below to learn:
Thanks!