SQL Connection string shared accross all Onelakes

Hi jbshml 

 

You can think of SQL Connection string as a server name, and each workspace is equivalent to a server. Therefore, all SQL analytics endpoints, data warehouses, SQL databases and mirrored databases in the same workspace share the same SQL Connection string.

 

I connect to a SQL Connection string in SSMS, and it shows all of above Fabric items as databases on the server. Just like below. 

 

UPDATE:

Based on your python code, I used the following code, which was able to read data from the database specified in the connection string. I don't use Pandas here. 

 

import pyodbc
from azure.identity import InteractiveBrowserCredential, AzureCliCredential
import struct
from itertools import chain, repeat
import pandas as pd

sql_endpoint = "xxxxxxxxxxxxx.datawarehouse.fabric.microsoft.com"
database_name = "your_lakehouse_name"

# credential = AzureCliCredential() # replace by InteractiveBrowserCredential() if needed
credential = InteractiveBrowserCredential()

token = credential.get_token("https://database.windows.net/.default")
token_as_bytes = bytes(token.token, "UTF-8")
encoded_bytes = bytes(chain.from_iterable(zip(token_as_bytes, repeat(0))))
token_bytes = struct.pack("<i", len(encoded_bytes)) + encoded_bytes

connection_string = (
    f"Driver={{ODBC Driver 18 for SQL Server}};Server={sql_endpoint},1433;Database={database_name};Encrypt=Yes;TrustServerCertificate=No"
)

conn = pyodbc.connect(connection_string, attrs_before={1256: token_bytes})

cursor = conn.cursor()

cursor.execute("select * from sys.tables")

for row in cursor:
    print(row)

conn.close()

 

 

Using Pandas version:

connection_string = (
    f"Driver={{ODBC Driver 18 for SQL Server}};Server={sql_endpoint},1433;Database={database_name};Encrypt=Yes;TrustServerCertificate=No"
)

conn = pyodbc.connect(connection_string, attrs_before={1256: token_bytes})

query = "SELECT * FROM sys.tables"

df = pd.read_sql(query, conn)

conn.close()

print(df.head())

 

Best Regards,
Jing
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