Forum Discussion
Reference current workspace with notebook without Spark
As far as I can tell it's impossible to write data to the Tables section of a datalake without starting a Spark session, so this approach will not work.
Hi DCELL ,Thanks for reaching out to the Microsoft fabric community forum
Thanks for your prompt response
DCELL ,
You're right getting the Lakehouse/workspace info inside a non-Spark notebook is currently not directly supported like it is with mssparkutils in Spark.
However, you can still achieve dynamic, environment-aware notebooks by parameterizing them and using Fabric Pipelines to inject those values at runtime this way, you avoid hardcoding, and your notebook stays Spark-free.
As a lightweight alternative, you could also read a small config.json file from the Lakehouse Files/ area that contains workspace/Lakehouse metadata this works fine in pandas’ notebooks.
So, while the feature isn’t natively exposed in non-Spark notebooks (yet), it’s still possible to design a dynamic, scalable workflow without requiring Spark sessions.
NotebookUtils (former MSSparkUtils) for Fabric - Microsoft Fabric | Microsoft Learn
The Microsoft Fabric deployment pipelines process - Microsoft Fabric | Microsoft Learn
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Best regards,
LakshmiNarayana.
The .json config file could work. Do you have a guide I can follow?
Hi DCELL ,
Thanks for the follow-up question
Here's a simple guide to help you set up and use a .json config file in your Fabric notebook (non-Spark) to make your workflows dynamic and environment-aware:
Step-by-Step: Using a config.json in Fabric (Pandas) Notebook
Create the config.json file
Place it in your Lakehouse Files/ area (e.g., Files/config/config.json). Example contents:
{
"lakehouse_name": "SalesLakehouse",
"environment": "dev",
"data_path": "Tables/sales_data",
"region": "East US"
}
Load the JSON in your notebook using Pandas or built-in file APIs
import json
config_path = "Files/config/config.json"
with open(config_path, "r") as f:
config = json.load(f)
print(config["lakehouse_name"])
If reading directly from the Lakehouse via Pandas:
import pandas as pd
import json
with open("/lakehouse/default/Files/config/config.json", "r") as f:
config = json.load(f)
print(config["environment"])
Use config values in your logic
data_path = config["data_path"]
region = config["region"]Solved: Parameterizing a notebook - Microsoft Fabric Community
Develop, execute, and manage notebooks - Microsoft Fabric | Microsoft Learn
Best Regards,
LakshmiNarayana
As far as I can tell it's impossible to write data to the Tables section of a datalake without starting a Spark session, so this approach will not work.
DCELL ,
Thanks for the clarification really appreciate the detailed explanation. That clears things up
Best Regards
Lakshmi Narayana
Hi DCELL ,
If your issue has been resolved, please consider marking the most helpful reply as the accepted solution. This helps other community members who may encounter the same issue to find answers more efficiently.
If you're still facing challenges, feel free to let us know we’ll be glad to assist you further.
Looking forward to your response.
Best regards,
LakshmiNarayana.The .json idea can work, since it will just require a one-time load to the datalake of each workspace showing the workspace id and lakehouse id.
Before I close this I'm checking if the non-spark read and write functions will work properly with Fabric datalakes.