# Install Semantic Link Labs
%pip install semantic-link-labs
     
# Required Library
import sempy_labs as labs

# Optional Libraries
import json
from notebookutils import fs

Parameters

All the parameters expect the name or the id of the object, except the import_tables parameter. This one requires a list of names of the tables that will be in Import mode.

# Required parameters
workspace = 'ws_demo'                           # Reference workspace

dataset_import = 'sm_import'                    # Source: Import model
dataset_directlake ='sm_directlake'             # Source: Direct Lake model
dataset_composite = 'sm_composite'              # Sink: Composite model

import_tables = ['table1', 'table2', 'table3']  # List of tables that will be set to Import mode

# Optional parameters
storage = 'lh_demo'                             # Lakehouse to ouput the Composite Model definition file

Model Definition

Process workflow:

1. Capture the original model definition (BIM) files
2. Remove from the Direct Lake file the tables that will be kept in Import mode
3. Add in the Direct Lake file the Import tables based on the Import file definition
4. Deploy the model

Existing relationships on the Direct Lake model are persisted. It will fail if the column names are different (between Direct Lake and Import models).

# Get Model Definition file (BIM) from source models
bim_import = labs.get_semantic_model_bim( dataset = dataset_import, workspace = workspace )
bim_directlake = labs.get_semantic_model_bim( dataset = dataset_directlake, workspace = workspace )

# Delete the tables that will change storage mode from the Direct Lake file
bim_directlake['model']['tables'] = [
    table for table in bim_directlake['model']['tables'] 
    if table['name'] not in import_tables
]

# Include the tables with changed storage mode to Import in the Direct Lake file
for table in bim_import['model']['tables']:
    if table['name'] in import_tables:
        bim_directlake['model']['tables'].append(table)

Deployment

The deployment is done via XMLA endpoint. It will create or update the model if it already exists. After the deployment, a new connection will be displayed in the Gateway and Cloud Connections section of the Semantic Model Properties. A rebind of the Import tables connection is required, since it cannot rely on Single Sign On as Direct Lake does (this is only required after the first deployment).

• If a connection already exists for the Import table, just check it on the Map to.
• Otherwise, create a new connection and then Map to it.

After the rebind the semantic model can be refreshed.

# Create/Update the Composite Model
try:
    labs.create_semantic_model_from_bim( dataset = dataset_composite, bim_file = bim_directlake, workspace = workspace )
except:
    labs.update_semantic_model_from_bim( dataset = dataset_composite, bim_file = bim_directlake, workspace = workspace )

# Retrieves the list of connections visible in the Fabric environment
connections = labs.list_connections()

# Retrieves the list of connection dependencies in the Semantic Model
model_connections = labs.list_item_connections( item_name = dataset_composite, item_type = 'SemanticModel', workspace = workspace )

# Connections that do not rely on AzureDataLakeStorage (Direct Lake on Onelake)
connection_ids = model_connections[ model_connections['Connection Type'] != 'AzureDataLakeStorage' ]['Connection Id']

for con in connection_ids:
    connection_path = model_connections[ model_connections['Connection Id'] == con ]['Connection Path'].iloc[0]

    if con:
        connection_name = connections[ connections['Connection Id'] == con ]['Connection Name'].iloc[0]
        print(f'The path {connection_path} has been mapped to the {connection_name} connection.')

    else:
        print(f'No connection found for path {connection_path}. Bind the connection before refreshing the semantic model.')

Output Model Definition File

# Save model definition to Lakehouse
path = f'abfss://{workspace}@onelake.dfs.fabric.microsoft.com/{storage}.Lakehouse/Files/{dataset_composite}.json'
file = json.dumps( bim_directlake, indent = 2 )

fs.put( path, file, overwrite = True )

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