Attach multiple lakehouse to a single notebook.

KrishnaMoola you can add multiple lakehouses to a notebook. 

In your notebook, click the add data items button: 

then you can choose an existing datasource and add another lakehouse: 

THe default lakehouse is still the one with the pin beside it, so to reference the other lakehouse you need to use the full identifier: 
GatewayLogs.dbo.table1, not just dbo.table1 

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You have bronze as default lakehouse, you can use the abfs path to get on silver lakehouse schema and inster data.

This implies a naming convention error. What notation do you get if you drag and drop the tables into your notebook?

Hi KrishnaMoola ,

Are both of the lakehouses schema enabled?

May be this will help?

Lakehouse schemas in notebook
When you look at a schema enabled lakehouse in the notebook object explorer, you see tables are in schemas. You can drag and drop table into a code cell and get a code snippet that refers to the schema where the table is located. Use this namespace to refer to tables in your code: "workspace.lakehouse.schema.table". If you leave out any of the elements, the executor uses default setting. For example, if you only give table name, it uses default schema (dbo) from default lakehouse for the notebook.

Referencing nonschema lakehouses
When you set a schema-enabled lakehouse or no lakehouse as the default in your notebook, Spark code uses schema-enabled referencing for tables. However, you can still access lakehouses without enabled schemas within the same code by referencing them as "lakehouse.table."

Additionally, it’s possible to join tables from different types of lakehouses.

SQL
SELECT *
FROM workspace.schemalh.schema.table as schematable
INNER JOIN workspace.nonschemalh.table as nonschematable
ON schematable.id = nonschematable.id;

To help transition and refactor existing code to schema-enabled lakehouses, four-part naming is supported for lakehouses without schemas. You can reference tables as "workspace.lakehouse.dbo.table", where "dbo" serves as the schema name—even though it doesn’t exist yet in a lakehouse that isn’t schema-enabled. This approach lets you update your code with no downtime before enabling schema support in your lakehouses.

I created a shortcut to the table in Silver lakehouse but not sure if this is the correct approach, can anyone suggest on this ?

Hi KrishnaMoola ,

we wanted to check in as we haven't heard back from you. Did our solution work for you? If you need any more help, please don't hesitate to ask. Your feedback is very important to us. We hope to hear from you soon.

Hi KrishnaMoola 

In Fabric, the spark catelog only keeps the default lakehosue tables and schemas. Thus you are not able to access the other lakehouse(silver lakehouse) tables. The most appropriate option would be to use ABFS path. 
Other than that if you are using spark.sql  you can use first convert the dataframe in VIEW and then perform sql operations. 


Hope this helps. Let me know if you have any futher question , we can discuss !!

Hi KrishnaMoola ,

Kindly let us know if your issue is resolved or if you need any additional information.

Thank you.

Because only the default attached Lakehouse is used automatically in a Fabric notebook, Spark can’t find the silver schema in the other Lakehouse , use fully qualified table name (LakehouseName.silver.table) or switch the catalog/schema explicitly.