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This is especially useful for getting the SQL Endpoint connection string
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.
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Notebookutils in Fabric now has getWithProperties() method to get the properties of a lakehouse. Earlier the get() method only returned abfs path and not the SQL endpoint.
By default it returns the JSON:
//sample output
{'id': '<>',
'type': 'Lakehouse',
'displayName': '<name>',
'description': '',
'workspaceId': '<>',
'properties': {'oneLakeTablesPath': 'https://onelake.dfs.fabric.microsoft.com/<>/<>/Tables',
'oneLakeFilesPath': 'https://onelake.dfs.fabric.microsoft.com/<>/<>/Files',
'sqlEndpointProperties': {'connectionString': '<>.datawarehouse.fabric.microsoft.com', 'id': '<>', 'provisioningStatus': 'Success'},
'abfsPath': 'abfss://<>@onelake.dfs.fabric.microsoft.com/<>'}}
Below script normalizes the JSON to a pandas df:
import pandas as pd
import sempy.fabric as fabric
def lakehouse_properties(lakehouse_name, workspace=None):
"""
Sandeep Pawar | fabric.guru
Returns properties of a lakehouse as a pandas df.
Default workspace is used if workspace is None.
"""
workspace = fabric.resolve_workspace_id(workspace) or fabric.get_workspace_id()
# Get the Lakehouse data
data = notebookutils.lakehouse.getWithProperties(name=lakehouse_name, workspaceId=workspace)
flattened_data = {
'lakehouse_id': data['id'],
'type': data['type'],
'lakehouse_name': data['displayName'],
'description': data['description'],
'workspaceId': data['workspaceId'],
'oneLakeTablesPath': data['properties']['oneLakeTablesPath'],
'oneLakeFilesPath': data['properties']['oneLakeFilesPath'],
'abfsPath': data['properties']['abfsPath'],
'sqlep_connectionString': data['properties']['sqlEndpointProperties']['connectionString'],
'sqlep_id': data['properties']['sqlEndpointProperties']['id'],
'sqlep_provisioningStatus': data['properties']['sqlEndpointProperties']['provisioningStatus']
}
df = pd.DataFrame([flattened_data])
# Column order
column_order = [
'lakehouse_id', 'type', 'lakehouse_name', 'description', 'workspaceId',
'oneLakeTablesPath', 'oneLakeFilesPath',
'sqlep_connectionString',
'sqlep_id',
'sqlep_provisioningStatus',
'abfsPath'
]
df = df[column_order]
return df
lakehouse_properties("<lakehouse_name>")
To get properties of all lakehouses in a workspace:
df = pd.concat([lakehouse_properties(lh) for lh in fabric.list_items(type="Lakehouse")['Display Name']])
