How can I connect to a SQL Mirror Database?

[abhidotnet]

I’m working in a workspace in Microsoft Fabric where I’ve set up a notebook intended to connect to a SQL Mirror Database hosted in the same Fabric environment. However, when I try using the “Connect to Source” option in the notebook, the database doesn’t appear in the list—only Lakehouses from other workspaces show up.

Is there a recommended way to connect a Spark notebook to a SQL Mirror Database in Fabric?
Am I missing a configuration step, or is there a specific connection string or method I should use manually?

Appreciate any guidance or examples from those who’ve set this up successfully!

[Element115]

careful though with DFg2 as it is a lot more expensive in terms of CUs than doing the same with either SQL script or PySpark Notebook in a pipeline.

[BhaveshPatel]

Hi @abhidotnet 

Rather than writing programming code like python or scala in notebook, you should use UI/ UX in Dataflow Gen 2 and use Mirrored SQL Database. ( Click, Click and Click and you are done..)

[v-priyankata]

Hi @abhidotnet 

Thank you for reaching out to the Microsoft Fabric Forum Community.
@ToddChitt Thanks for your valuable inputs.
It's also a good idea to consider suggestions from the user, we can try that. adding another point below.

The connect to data option in fabric notebooks only shows Lakehouse & KQL databases, we are not able see SQL Mirrored Databases listed there, even if your in the same workspace. To work around this, we can create a Lakehouse and adding a One Lake shortcut that points to the mirrored SQL database. please refer below document.
Explore Data in Your Mirrored Database With Notebooks - Microsoft Fabric | Microsoft Learn


Please feel free to reach out here if you need further assistance.
Thanks.

[ToddChitt]

This may not be a real solution to your issue, but WHY do you want to connect a Notebook to a Mirrored SQL database? Typically, you use Notebooks to collect and process non-tabular data sources (Excel, JSON, XML, etc.) and from there dump it into tabular destinations (read: tables). 

If data is ALREADY in tables in the mirrored database, why not use T-SQL to access and process it?

[Element115]

because you can do things more easily and in a saner way in Python than with the antedeluvian 4GL language that SQL is.  That would be my reason #1.  

#2 you got a bunch of libs in Python to do data science and data engineering that do not exist in the SQL world.  

and on and on it goes.