Best way to connect data from Fabric Notebook into Power Query

To pull data from a Microsoft Fabric notebook into Power Query for use in Power BI, you can follow these steps:

1. Save Data from Notebook to a Storage Location:

   • Save your dataframe from the notebook to a location that Power BI can access, such as a CSV file in Azure Blob Storage, OneDrive, or SharePoint.

2. Connect Power Query to the Storage Location:

   • Use Power Query to connect to the storage location where you saved the file.

Here’s a detailed guide to achieve this:

Step 1: Save Dataframe to a Storage Location

1. Save as CSV in Azure Blob Storage:

   • If you have Azure Blob Storage, you can save your dataframe directly to a blob container.

from azure.storage.blob import BlobServiceClient
import pandas as pd

# Your dataframe
df = pd.DataFrame(data)

# Save dataframe as CSV
csv_data = df.to_csv(index=False)

# Azure Blob Storage details
connection_string = "your_connection_string"
container_name = "your_container_name"
blob_name = "your_file_name.csv"

# Create the BlobServiceClient object
blob_service_client = BlobServiceClient.from_connection_string(connection_string)
blob_client = blob_service_client.get_blob_client(container=container_name, blob=blob_name)

# Upload the CSV data
blob_client.upload_blob(csv_data, overwrite=True)

Step 2: Connect Power Query to the Storage Location

1. Connect to Azure Blob Storage:

   • In Power BI Desktop, go to Home > Get Data > Azure > Azure Blob Storage.
   • Enter your storage account name and access key.
   • Navigate to your container and select the CSV file.

2. Connect to OneDrive or SharePoint:

   • In Power BI Desktop, go to Home > Get Data > SharePoint Folder.
   • Enter the SharePoint site URL and connect.
   • Navigate to the folder containing your CSV file and select it.

3. Load and Transform Data in Power Query:

   • Once you have connected to the storage location and selected your file, the data will be loaded into Power Query Editor.
   • Perform any necessary transformations and merge with other queries.
   • Load the transformed data into Power BI for further analysis.