Sandeep Pawar

Jul 16, 2025

Using Fabric CLI in Fabric Notebook

11 Minutes

Automation, Convenience and Flexibility

Power BI Core Visuals team led by Miguel Myers published a huge update last week : Organizational Themes. It’s been a long standing ask by the users. It allows Power BI admins to manage and distribute centrally stored themes to all developers in the organization. Read the blog post for details. Currently, as the above blog explains, org themes don’t update existing reports automatically, which makes sense. But what if you want to bulk update many puFirst I would like to thank Edgar Cotte (Sr PM Fabric CAT) for the inspiration for this blog. Edgar shared this recently in a workshop so I got curious and explored more. Fabric CLI, as the name suggests, allows you to interact with the Fabric environment using command line interface. Whether you are a Fabric admin or a developer, it’s essential for exploration and automation from a convenient interface. But if you work with external clients like I do where there may be restrictions on installing libraries, using Fabric CLI in Fabric notebook may be an easier option especially if you are already familiar with all the commands.

To learn more about Fabric CLI, refer to:

https://youtube.com/watch?v=y2rWzSFStZ8%3Ffeature%3Doembed

Installation:

In Fabric python notebook, first install fabric cli :CopyCopy

%pip install ms-fabric-cli --q

Set up Auth Token:

This will use the identity of the user executing the notebook. You can also use service principal authentication as shown here.CopyCopy

token = notebookutils.credentials.getToken('pbi')
os.environ['FAB_TOKEN'] = token
os.environ['FAB_TOKEN_ONELAKE'] = token

Commands:

There are two types of commands – command line and interactive. In the notebook, you should be able to run all commands supported in command line mode. The documentation does a great job of describing if a command can be used in command line, interactive or both.

There are two ways you can execute the supported commands – using magic or using a Python wrapper for the commands.

Using cell magic

Once installed and auth setup, use !fab magic to run the shell command. Below I get a list of all the workspaces along with the capacity attached and capacity id.

To get a list of all items in a workspace:

This works even if you have spaces in item/workspace names:

You can use JMESPath in the query or you can also use shell commands like below to filter the lines that contain word “Trial” and print the first column (i.e. workspace names)

If a command is not available, you can call an API inline as well:

To download items to an attached default lakehouse:

Multi-line

!fab allows only one line command. To create more complex multi line commands, you can use %%sh cell magic like below:CopyCopy

%%sh
echo "=== Exploring Reid Workspace ==="

# jump to workspace
fab -c "cd Reid.Workspace" || echo "Failed to navigate"

echo "Listing all items:"
fab -c "ls Reid.Workspace -l" || echo "No items or access denied"

echo ""
echo "Notebooks only:"
fab -c "ls Reid.Workspace" | grep "\.Notebook" || echo "No notebooks found"

echo ""
echo "Reports only:"
fab -c "ls Reid.Workspace" | grep "\.Report" || echo "No reports found"

echo "Exploration completed"

You can also pass Python variables to commands, making it very dynamic:

💡

It would be great if fabric-cli library is installed in the default Python runtime with the token set up so users can use !fab easily. That would be super handy.

Using Python

Above method is great for interactive exploration. For automation, you can create Python functions and use it like any other Python function. For example, I downloaded one notebook but for more complex patterns/automation, I can use subprocess.run() to execute the commands.

To download all notebooks from a workspace to a lakehouse location:CopyCopy

def download_item(workspace, item_name, local_path="/lakehouse/default/Files/tmp", force=True):
    """
    Download a Fabric item to local dir
    """
    try:

        os.makedirs(local_path, exist_ok=True)
        cmd = f"export {workspace}.Workspace/{item_name} -o {local_path}"
        if force:
            cmd += " -f"
        result = subprocess.run(["fab", "-c", cmd], capture_output=True, text=True)

        if result.returncode == 0:
            print(f"Downloaded {item_name} to {local_path}")
            return True
        else:
            print(f"Failed: {result.stderr}")
            return False

    except Exception as e:
        print(f"Error: {e}")
        return False

For loop:CopyCopy

def download_all_notebooks(workspace, local_path="/lakehouse/default/Files/tmp"):
    """Download all notebooks from workspace"""
    try:

        result = subprocess.run(["fab", "-c", f"ls {workspace}.Workspace"], capture_output=True, text=True)
        notebooks = [line.strip() for line in result.stdout.split('\n') if '.Notebook' in line]

        success_count = 0
        for notebook in notebooks:
            if download_item(workspace, notebook, local_path):
                success_count += 1

        print(f"Downloaded {success_count}/{len(notebooks)} notebooks")
        return success_count

    except Exception as e:
        print(f"Error downloading: {e}")
        return 0

You can generalize this to create re-usable functions, e.g. below lists all workspaces:CopyCopy

import subprocess
import json
from typing import Optional, Dict, Any

def exec_fabcli(command: str, capture_output: bool = False, silently_continue: bool = False) -> Optional[str]:
    """
    Run a Fabric CLI command from within a notebook.

    Args:
        command: The fab command to run (without 'fab' prefix)
        capture_output: Whether to capture and return the output
        silently_continue: Whether to suppress exceptions on errors

    Returns:
        Command output if capture_output=True, otherwise None
    """
    try:
        result = subprocess.run(["fab", "-c", command], capture_output=True, text=True, timeout=60)

        if not silently_continue and result.returncode != 0:
            error_msg = f"Command failed with exit code {result.returncode}\n"
            error_msg += f"STDOUT: {result.stdout}\n"
            error_msg += f"STDERR: {result.stderr}"
            raise Exception(error_msg)

        if capture_output:
            return result.stdout.strip()
        else:
            # output
            if result.stdout:
                print(result.stdout)
            if result.stderr:
                print(f"Warning: {result.stderr}")

    except subprocess.TimeoutExpired:
        raise Exception("Command timed out after 60 seconds")
    except FileNotFoundError:
        raise Exception("Fabric CLI not found. Make sure 'fab-cli' is installed")

def list_workspaces(detailed: bool = False, show_hidden: bool = False) -> str:
    """List all workspaces"""
    flags = ""
    if detailed:
        flags += " -l"
    if show_hidden:
        flags += " -a"
    return exec_fabcli(f"ls{flags}", capture_output=True)

print(list_workspaces(detailed=True))

Semantic Link/Labs vs Fabric CLI

Semantic Link and Semantic Link Labs also provide similar capabilities but if you are already using CLI, this is a convenient way to reuse what you already know. This will especially be helpful for automating workflows. Different tools, different use cases. Fabric CLI can be usedf or CI/CD with Github and ADO automation. Jacob Knightley shared this excellent comparison at FabCon :

Here are additional resource to learn more:

Experiment : fabgpt

I can’t complete the blog without mentioning AI, can I ? 😀 I love how Fabric CLI allows you to query, automate so easily. I was wondering what if all of that could be achieved with natural language? In the below example, I used Open AI + notebook cell magic to wrap above Python functions in a cell magic which I am calling fabgpt 😀 It generates above Python code and executes it.

If you are a Power BI developer with no prior experience in notebooks and Python, I recommend checking out Kurt Buhler’s new notebook tutorial to get started. Also bookmark these example notebooks.

About the Author

Sandeep Pawar

Sandeep Pawar

Principal Program Manager @ Microsoft Fabric CAT

Reference:

Pawar, S (2025). Using Fabric CLI in Fabric Notebook. Available at: Using Fabric CLI in Fabric Notebook [Accessed: 14th July 2025].

Share this on...

You might also like ...

Jul 16, 2025

Programmatically Apply Organizational Theme To Multiple Power BI Reports Using Semantic Link Labs

Jan 28, 2026

Streaming External Events into Microsoft Fabric Eventhouse Using Custom Endpoints

Jan 27, 2026

Meet the Microsoft Fabric Data Agent: Are You Ready to Chat with Your Data?