Teradata Jupyter Docker image

The Teradata Jupyter Docker image builds on quay.io/jupyter/datascience-notebook Docker image. It adds the Teradata SQL kernel, Teradata Python and R libraries, Jupyter extensions to make you productive while interacting with Teradata. The image also contains sample notebooks that demonstrate how to use the SQL kernel and Teradata libraries.

The SQL kernel and Teradata Jupyter extensions are useful for people that spend a lot of time with the SQL interface. Think about it as a notebook experience that, in many cases, is more convenient than using Teradata Studio. The Teradata Jupyter Docker image doesn't try to replace Teradata Studio. It doesn't have all the features. It's designed for people who need a lightweight, web-based interface and enjoy the notebook UI.

The Teradata Jupyter Docker image can be used when you want to run Jupyter locally or you have a place where you can run custom Jupyter docker images. The steps below demonstrate how to use the image locally.

1. Run the image:

docker volume create notebooks
docker run -e "accept_license=Y" -p 8888:8888 \
  -v notebooks:/home/jovyan/JupyterLabRoot \
  teradata/jupyterlab-extensions

1. Docker logs will display the url that you need to go to. For example, this is what we've got:

   Starting JupyterLab ...
   Docker Build ID = 3.2.0-ec02012022
   Using unencrypted HTTP
   
   Enter this URL in your browser:  http://localhost:8888?token=96a3ab874a03779c400966bf492fe270c2221cdcc74b61ed
   
   * Or enter this token when prompted by Jupyter: 96a3ab874a03779c400966bf492fe270c2221cdcc74b61ed
   * If you used a different port to run your Docker, replace 8888 with your port number
   

2. Open up the URL and use the file explorer to open the following notebook: jupyterextensions -> notebooks -> sql -> GettingStartedDemo.ipynb.

3. Go through the demo of the Teradata SQL Kernel: