Options

There are a couple of ways to connect to Teradata from a Jupyter Notebook:

1. Use python or R libraries in a regular Python/R kernel notebook - this option works well when you are in a restricted environment that doesn't allow you to spawn your own Docker images. Also, it's useful in traditional datascience scenarios when you have to mix SQL and Python/R in a notebook. If you are proficient with Jupyter and have your own set of preferred libraries and extensions, start with this option.
2. Use the Teradata Jupyter Docker image - the Teradata Jupyter Docker image bundles the Teradata SQL kernel (more on this later), teradataml and tdplyr libraries, python and R drivers. It also contains Jupyter extensions that allow you to manage Teradata connections, explore objects in Teradata. It's convenient when you work a lot with SQL or would find a visual Navigator helpful. If you are new to Jupyter or if you prefer to get a currated assembly of libraries and extensions, start with this option.