- Access Vantage in Python, R or SQL from JupyterLab notebooks
- Explore objects in a Teradata Vantage catalog
- Manage connections to Teradata Vantage
The Docker image includes JupyterLab, Teradata Vantage Modules for Jupyter, and other components to run as a Docker container on a client machine. A manual installation allows you to install components on a client machine that already has JupyterLab.
Teradata Vantage Modules for Jupyter is also available for installation as a fully Single Sign-On integrated AppCenter app. For information about Teradata Vantage Modules for Jupyter as an SSO integrated AppCenter app, contact your Teradata Account Representative.
|What is Included?||Docker Image Installation||Manual Installation into JupyterLab|
|Teradata Connections module||●||●|
|Teradata SQL kernel||●||●|
|Teradata Python Package||●|
|Teradata R Package||●|
|Sample Teradata SQL notebooks||●||●|
|Sample Teradata Python notebooks||●|
|Sample Teradata R notebooks||●|
|JupyterLab and other libraries helpful to data scientists||●|
The Teradata SQL extension module includes a navigator module for viewing and browsing objects in Teradata Vantage, regardless of the language you are using in your notebook (Python, R, SQL).
- Hierarchical display of database objects
- Column metadata showing data type and indexes
- Row Count, Show DDL and Column Distribution menu options
The Teradata SQL extension module includes a connection manager. This allows you to manage connections to Vantage for use by the Navigator and SQL notebooks.
The Connection Manager provides the following:
- Connection management to add, remove, edit, copy, list, and test connections
- User interface that is independent of the SQL notebook
- Connections that are shared with the Navigator and SQL notebooks
The Teradata SQL extension module includes a Teradata SQL kernel. This allows you to run SQL commands on Vantage from a JupyterLab notebook.
- Connection management to add, remove, connect, and list connections
- Query engine that uses embedded Teradata SQL driver
- SQL-aware notebook with SQL content assist and syntax checking
- Result set renderer that displays result data in an easy-to-read format, using scrollable grid with support to copy cells
- Execution history that stores execution metadata to recall SQL commands at a later time
- Visualization using Vega library to display charts, graphs, plots, and more
- Magic commands that provide additional custom kernel options to enhance your Teradata Vantage user experience
- Support for basic data import from a .csv file of up to 100k rows
- Preference settings allow users to modify logging options for the SQL Kernel
- Teradata Python Package User Guide
- Teradata Python Package Function Reference
- Teradata R Package User Guide
- Teradata R Package Function Reference
Examples and Tutorials
Sample SQL, Python and R notebooks and additional information about using Teradata Vantage Modules for Jupyter can be found at: https://teradata.github.io/jupyterextensions/.