nb-fabric 0.6.0

Experimental. Commands and flags may change between versions. Pin your version if stability matters.

Install

cargo install nb-fabric

Or download a prebuilt binary from Releases.

Prerequisites

• Azure CLI (az) installed and authenticated (az login)
• Access to a Microsoft Fabric workspace

Quick Start

# Check auth
nb auth status

# List notebooks
nb list "My Workspace"

# Create a Python notebook with a lakehouse attached
nb create "My Workspace/ETL Pipeline" --kernel python --lakehouse MainLH

# Add a cell
nb cell add "My Workspace/ETL Pipeline" --code "print('hello')"

# Run code directly against a lakehouse (no notebook needed)
nb exec code "My Workspace/MainLH.Lakehouse" "print('hello')"
nb exec code "My Workspace/MainLH.Lakehouse" "spark.sql('SHOW TABLES').show()"

# Execute a notebook cell interactively
nb exec cell "My Workspace/ETL Pipeline" 0 --lakehouse MainLH

# Run as a batch job
nb job run "My Workspace/ETL Pipeline" --wait

Command Reference

Authentication

nb auth status                     Check Azure CLI authentication

Notebook CRUD

nb list <workspace>                List notebooks in a workspace
nb create <ws/name>                Create a new notebook
  --kernel python|pyspark            Kernel type (default: python)
  --lakehouse <name>                 Attach a lakehouse
  --warehouse <name>                 Attach a warehouse
nb export <ws/name> -o <path>      Export notebook to local .ipynb
nb open <ws/name>                  Open notebook in browser
nb delete <ws/name> --force        Delete a notebook

Cell Operations

nb cells <ws/name>                 List all cells (index, type, preview)
nb cell view <ws/name> <index>     View a single cell's source
nb cell add <ws/name>              Add a new cell
  --code <code>                      Cell content (required)
  --markdown                         Create markdown cell (default: code)
  --at <index>                       Insert at position (default: append)
nb cell edit <ws/name> <index>     Replace a cell's source
  --code <code>                      New content
nb cell rm <ws/name> <index>       Remove a cell

Execution

nb exec code <ws/lakehouse> <code>   Run code directly against a lakehouse (no notebook)

nb exec cell <ws/notebook> <index>   Execute a notebook cell via its attached lakehouse
  --lakehouse <name>                   Lakehouse (auto-detected from notebook metadata)

nb job run <ws/notebook>             Run notebook as batch job
  --wait                               Wait for completion
  --timeout <secs>                     Timeout in seconds (default: 3600)
nb job list <ws/notebook>            List recent job runs

exec code

Run Python code against a lakehouse without creating a notebook. The spark (SparkSession) variable is always available for querying lakehouse tables. Sessions are created, used, and cleaned up automatically (including on Ctrl+C). Pass - as the code arg to read from stdin.

$ nb exec code "My Workspace/MainLH.Lakehouse" "print(2+2)"
---- exec: Python ----
  Creating session...
  Waiting for idle...  (session a1b2c3d4)
  Session ready.
  Submitting code...
4
  Session cleaned up.
---- result ----
  session  a1b2c3d4-...
  runtime  Python
  duration 10.2s
  status   ok

Stdin piping for agents:

echo "spark.sql('SELECT count(*) FROM my_table').show()" | nb exec code "WS/LH.Lakehouse" -

exec cell

Execute a specific cell from a notebook. Auto-detects kernel type and lakehouse from notebook metadata.

nb exec cell "My Workspace/ETL Pipeline" 0
nb exec cell "My Workspace/ETL Pipeline" 3 --lakehouse MainLH

Sessions

nb session <ws/notebook>           Show active sessions for a notebook

Scheduling

nb schedule list <ws/notebook>     List schedules
nb schedule create <ws/notebook>   Create a schedule
  --type Cron|Daily|Weekly           Schedule type (default: Cron)
  --interval <n>                     Interval (minutes for Cron)
  --start <datetime>                 Start time (ISO 8601)
  --end <datetime>                   End time (optional)
  --timezone <tz>                    Timezone (default: UTC)
  --enable                           Enable immediately
nb schedule update <ws/notebook> <id>  Update a schedule
  --enable true|false                Enable or disable
  --interval <n>                     New interval
nb schedule delete <ws/notebook> <id>  Delete a schedule

How It Works

nb authenticates via Azure CLI (az account get-access-token) using the same credentials as fab and az. It calls the Fabric REST API for notebook management and interactive execution.

Authentication

No service principal required. nb uses your Azure CLI session:

az login        # One-time; opens browser
nb auth status  # Verify

Notebook Formats

nb create generates notebooks with the correct Fabric metadata for either kernel:

Use or Re-use

You do not have the license to copy and incorporate this into your own products, trainings, courses, or tools. If you copy this project or use an agent to rewrite it, you must include attribution and a link to the original project.