- Access Vantage to run SQL and Python queries from a JupyterLab notebook
- Explore objects in a Teradata Vantage catalog
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's Included?||Docker Image Installation||Manual Installation into JupyterLab|
|Teradata SQL kernel||●||●|
|Teradata Python Package||●|
|Sample Teradata SQL notebooks||●||●|
|Sample Teradata Python 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 SQL object relational model
- Column metadata showing data type and indexes
- Row Count and Column Distribution menu options
The Teradata SQL extension module includes a Teradata SQL kernel. This allows you to run SQL commands on Teradata 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, pageable grid
- Execution history that stores execution metadata to recall SQL commands at a later time
- Visualization using VegaLite library to display charts, graphs, plots, and more
- Magic commands that provide additional custom kernel options to enhance your Teradata Vantage user experience
- Teradata Python Package User Guide
- Teradata Python Package Function Reference
Examples and Tutorials
Sample SQL and Python notebooks and additional information about using the SQL extension for Jupyter can be found at: https://teradata.github.io/jupyterextensions/.