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Get Started

This project contains examples of how to perform end-to-end analysis on mailing lists. Check out the project overview for an overview of this project.

Run the Notebooks

There are interactive notebooks for this project available for anyone to start using on the public JupyterHub instance on the Massachusetts Open Cloud (MOC) right now!

  1. To get started, access JupyterHub, select log in with moc-sso and sign in using your Google Account.
  2. After signing in, on the spawner page, please select the Mailing list analysis toolkit image in the JupyterHub Notebook Image section from the dropdown and select a Medium container size and hit Start to start your server.
  3. Once your server has spawned, you should see a directory titled mailing-list-analysis-toolkit-<current-timestamp>.
  4. Go to notebooks/ and to look into the data collection and pre-processing steps explore the notebooks in the 01_collect_data directory and to explore examples of analyses on mailing lists go through the notebooks in the 02_analyses directory.
  5. Here’s a video that that can help familiarize you with the project.

If you need more help navigating the Operate First environment, we have a few short videos to help you get started.

Project Assumptions

This repository assumes that you have an existing Open Data Hub deployed on OpenShift that includes, JupyterHub, Argo, Ceph, Hive, Cloudera Hue and Apache Superset.


The primary output and user interface for this application is a Superset dashboard. This tool allows us to define certain data visualization elements from our analysis that we would like to publish and share with others, while also including enough flexibility and interactivity to allow users to explore the data themselves.

Our application is designed to automatically re-run the analyses on regular basis and ensure that the dashboard and all its plots are current and up to date.

  • Current Superset Dashboard can be found here. This is currently accesible internally at Red Hat. However, there are also plans to make the analysis publicly accessible on Operate first (see issue)

fedora dashboard

Automated Argo workflows

If you’d like to automate your Jupyter notebooks using Argo, please follow the steps outlined in this guide. By following the steps in the document, your application can be fully set and ready to be deployed via Argo CD.