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.
| What is Included? | Docker Image Installation | Manual Installation into JupyterLab |
|---|---|---|
| Navigator module | ● | ● |
| Teradata Connections module | ● | ● |
| Teradata SQL kernel | ● | ● |
| Teradata Package for Python | ● | |
| Teradata Package for R | ● | |
| Sample Teradata SQL notebooks | ● | ● |
| Sample Teradata Package for Python notebooks | ● | |
| Sample Teradata Package for R notebooks | ● | |
| JupyterLab and other libraries helpful to data scientists | ● |
Navigator Module
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
- Ability to add connections
- Column metadata showing data type and indexes
- Row Count, Show DDL, Refresh, Sample Data, and Column Distribution menu options
- Data profiling information including Values, Statistics, Frequency and Histogam, if the Vantage Analytics Library is installed on SQL Engine.You can download the Vantage Analytics Library from https://downloads.teradata.com/download/database/analytics-library.
Connection Manager
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
SQL Kernel
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 and view cells as text or image
- 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 500k rows
- Preference settings allow users to modify logging and other options for the SQL Kernel
Python
- Teradata Package for Python User Guide
- Teradata Package for Python Function Reference
R
- Teradata Package for R User Guide
- Teradata Package for R 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/.