Hi @Ayush05-gateway
Thanks for the extra detail. The exact call you are using:
from notebookutils import mssparkutils df = mssparkutils.fabric.sql("<DWH name>", sql_query) is not part of the documented API. Microsoft has been moving from the old mssparkutils namespace to notebookutils. The rename is official and backward compatibility exists for many helpers, but there is no published fabric.sql method. See the rename notice and current surface here: NotebookUtils (former MSSparkUtils) for Fabric and the companion note on the MSSparkUtils page. Undocumented members can change between runtime updates, which likely explains why the call worked 2 weeks ago and now fails.
Supported ways to query or write a Warehouse from a notebook
Option 1 - Python notebook: connect and query with notebookutils.data
The official Python-notebook experience includes data utilities to connect to a Warehouse and run T-SQL. Microsoft documents this workflow here: Use Python experience on Notebook (see the sections on Warehouse interaction and notebookutils.data; they also show the notebookutils.data.help() output and the connect_to_artifact function).
import notebookutils as nbu # Connect by name or ID (optionally pass workspace ID and artifact type) conn = nbu.data.connect_to_artifact("<Warehouse name or ID>") df = conn.query("SELECT TOP 10 * FROM dbo.YourTable;") display(df) Docs: Python notebook data utilities.
Option 2 - Python notebook: use the %%tsql cell magic
You can execute T-SQL directly in a Python notebook cell and bind to a specific Warehouse.
%%tsql --dw "<Warehouse name>" SELECT COUNT(*) AS rows_in_table FROM dbo.YourTable;
Docs: Run T-SQL code in Fabric Python notebooks and Connect to Fabric Data Warehouse.
Option 3 - PySpark notebook: Fabric Spark connector for Warehouse
For Spark notebooks, use the Warehouse connector. It supports reading and, with current GA runtimes, writing via a two-phase process that uses COPY INTO under the hood.
# Read from a Warehouse table df = spark.read.synapsesql("MyWarehouse.dbo.SourceTable") # Write a Spark DataFrame to a Warehouse table df_to_write.write.mode("append").synapsesql("MyWarehouse.dbo.TargetTable") Docs: Spark connector for Microsoft Fabric Data Warehouse.
Why you did not find fabric.sql in docs
- The supported NotebookUtils areas cover files, environment, chaining notebooks, secrets, and, in Python notebooks, notebookutils.data for Warehouse connections. There is no published mssparkutils.fabric.sql or notebookutils.fabric.sql.
- The rename to NotebookUtils is official; new features land under notebookutils while the mssparkutils namespace will be retired.
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