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JupyterHub is an interactive development environment for running Python, PySpark, and Trino SQL workloads. It’s designed for data engineers, analysts, and data scientists who need a flexible workspace for exploration, testing, validation, and development of data workflows.

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Environments and runtime specification

  • Platform-managed environment with pre-installed packages
  • Python version: 3.x
NexusOne ships with the latest supported version.

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Capabilities

JupyterHub offers several core features and use cases that are popular today.

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Key features

  • A browser-based notebook interface that requires no local installation.
  • An integrated Spark session that’s available directly from Python notebooks.
  • Built-in support for Trino SQL queries.
  • Ability to mix documentation, code, and output in a single file.
  • Access to shared utilities, utils, and an Airflow DAGs folder, dags.
  • Optional terminal access for scripting and running batch workflows.

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Use cases

JupyterHub is often recommended for the following use cases:
  • Building or testing PySpark transformations
  • Data quality validation workflows
  • Rapid prototyping before building an Airflow DAG
  • Small-scale analytics and interactive SQL queries
  • Investigating raw data stored in Iceberg, Hive, and S3
  • Trying out Python libraries, utilities, or custom logic
  • Debugging table issues, lineage, or schema mismatches

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Authentication

JupyterHub uses NexusOne’s standard authentication flow:
  • Single Sign-On (SSO)
  • OAuth 2.0 or OpenID Connect
After navigating to the URL, you are automatically redirected to the configured login provider and granted access upon successful authentication.

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User access and permissions

NexusOne’s security layer manages access to the following:
  • IAM groups grant JupyterHub access.
  • Ranger policies grant access to datasets, Hive metadata, Iceberg tables, and S3 object storage.
  • IAM users have access to view or interact with the data sources and storage locations when authorized.

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Exploring the JupyterHub UI

This section explores the JupyterHub UI and how to navigate it. Before exploring these, you can launch JupyterHub using the following designated URL: 
https://jupyter.<client>.nx1cloud.com/
When you purchase NexusOne, you receive a client name. Replace client with your assigned client name.
When you launch JupyterHub using the previous URL, you should see an image similar to the following image.
01-jupyterhub-homepage

JupyterHub homepage layout

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File browser

As seen in the previous image, the top left panel displays your workspace directory. When you launch a JupyterHub environment in NexusOne, you should see the following folders:
  • dags/
    • This folder is automatically synced with Airflow.
    • Any .py file placed here becomes visible to the Airflow scheduler.
    • Use this location to develop, test, and maintain Airflow DAGs.
    • Don’t store temporary notebooks or test files here.
  • utils/
    • This is a shared utilities folder, mounted into every Jupyter session.
    • Contains common helper functions, shared logic, configuration parsers, and reusable modules.
    • You can import utilities from here directly into notebooks or DAGs: 
      from utils.common_helpers import clean_dataframe
Beyond the default folders, the file browser has additional features, such as:
  • Creating new folders
  • Uploading or downloading files
  • Managing files by renaming, copying, or deleting them

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Main launcher

As seen in the previous image, the center panel displays the main launcher. It lets you create notebooks, terminals, and other file types. The Launcher contains the following sections, Notebook, Console, and Other.

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Notebook section

This section displays the available notebook kernels.
  • Python 3 ipykernel: The primary kernel used for the following use cases:
    • Data exploration
    • ETL validation workflows
    • PySpark development
    • Trino queries through Python libraries
  • R Kernel: Used for R-based analysis and visualizations
Clicking one of these icons creates a new notebook associated with the clicked Kernel.

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Console section

An interface that sends commands directly to the Python or R kernel and returns output immediately. It does this without creating notebook cells or storing the interaction as a document. It’s useful for:
  • Quick variable checks
  • Running isolated code without creating notebooks
  • Testing Python or Spark one-liners

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Other section

This includes tools and file templates inside JupyterHub.
  • Terminal: Opens a real shell session on the Jupyter server for the following:
    • Navigating directories
    • Running scripts
    • Installing user-level Python packages
  • Text file: Creates an empty file with a .txt file type
  • Markdown file: Creates an empty file having a .md file type It’s useful for writing documentation or notes.
  • Python file: Creates an empty file with a .py file type
  • R file: Creates an empty file with a .r file type
  • Show contextual help: Opens a help panel with documentation for JupyterLab features

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Status bar

As seen in the previous image, the bottom panel provides information about the current status of the environment, such as:
  • Active terminal count
  • CPU usage
  • Current kernel
  • Memory usage
  • Notebook mode
This helps users confirm if they’re using the correct kernel and monitor memory during Spark-heavy notebooks. As seen in the previous image, the top left sidebar provides quick access to all major tools available in your environment. Each icon represents a specific feature, and understanding these helps you navigate and manage your work efficiently.

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File browser

You can view your workspace directory and all the files and folders available in your Jupyter environment. Some of the key features include the following:
  • Access mounted platform folders such as:
    • The Airflow DAGs folder, dags/
    • The shared helper library, utils/
  • Upload and download files
  • Create, rename, delete, or move files
  • Use the drag-and-drop to add files from your local machine or organize files into respective folders within JupyterHub.
Some of the typical uses are for:
  • Creating folders for your projects
  • Editing or placing DAG files
  • Opening notebooks

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Running terminals and kernels

You can display all currently running processes, such as:
  • Active kernels
  • Active notebooks
  • Open tabs
  • Open terminals

Running terminals and kernels sidebar
This allows you to stop unused kernels to free up memory, prevent accidental resource consumption, and close runaway Spark sessions or long-running Python cells. Some of the typical uses are for:
  • Restarting a stuck notebook kernel
  • Cleaning up forgotten terminals
  • Monitoring your active background jobs

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Table of contents

You can automatically generate a structured outline of your notebook based on markdown headers. Some of the key features include the following:
  • Navigating large notebooks easily
  • Jumping quickly between sections
  • Helps you maintain readable notebooks with proper documentation
Some of the typical uses are for:
  • Notebooks with heavy documentation.
  • Presentation-style notebook flows.

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Object storage browser for S3

You can access S3 buckets or your object store using the UI. Some of the key features include the following:
  • Browsing all authorized buckets
  • Downloading files
  • Previewing CSV, JSON, and text files
  • Uploading new objects
  • Navigating through large datasets
  • Copying full S3 URIs used directly in PySpark, Trino, or spark-submit jobs

Object storage browser for S3
Access control:
  • Ranger policies control access to the bucket.
  • If you don’t see a bucket/path, that means you aren’t authorized.
Some of the typical uses are for:
  • Checking raw vs. processed data
  • Inspecting files before running Spark jobs
  • Validating daily partitions or incremental loads

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Extension manager

You can turn on or turn off JupyterLab extensions, which are UI plugins. Some key sections include:
  • WARNING: Alerts for extensions that are incompatible, blocked, or require server-side changes
  • INSTALLED: Lists all extensions currently installed and enabled in your Jupyter environment
  • DISCOVER: Browse discoverable extensions that you can request for administrator approval

Extension manager
Some of the typical uses are for:
  • Enabling additional syntax themes
  • Enabling development helpers
  • Checking installed UI extensions

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Jupyter AI chat

You can chat with an AI assistant directly inside JupyterHub. Some of the key features include the following:
  • Generating code snippets
  • Explaining Python/Spark errors
  • Assisting with documentation
  • Helping with SQL queries
  • Providing inline suggestions while working

Jupyter AI chat
It doesn’t access your data unless you paste it.
Some of the typical uses are for:
  • Getting quick help or code explanations
  • Producing sample transformations

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Additional resource