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This page describes how to use the interactive analysis features of Terra. |
Terra supports versatile and powerful support for interactive analysis using Jupyer Notebooks, supporting both the Python and R languages.
The Jupyter Notebooks may be run on a variety of runtime infrastructures, including Spark clusters.
For an introduction to Jupyter Notebooks for those unfamiliar with them, see:
- ****Interactive statistics and visualization with Jupyter notebooks ****This article provides a quick, illustrated introduction to Jupyter Notebooks in Terra. For a more detailed and interactive introduction to the use Jupyter Notebooks, see the example workspace below. ****
- Jupyter Notebooks 101 ****This example Workspace provides an interactive tutorial for those new to the use of Jupyter Notebooks. A tutorial is presented in the Notebook itself.
The following articles provide important information about using Jupyter Notebooks effectively within Terra:
- Customizing your interactive analysis application compute This article describes adjusting the configuration of your virtual "application compute" to fit your computational needs.
- Terra's Jupyter Notebooks environment Part I: Key components This article covers the components and terminology of Terra-based Notebooks.
- Terra's Jupyter Notebooks environment Part II: Key operations This article covers some of the key operations enabled in Notebooks on Terra.
- Terra's Jupyter Notebooks environment Part III: Best Practices This article covers some suggested best practices for making sure your Notebook's environment stays up to date.
- Docker tutorial: Custom runtime environments for Jupyter Notebooks ****This is a step-by-step guide for building and publishing a custom Docker image, and running a Jupyter Notebook on Terra using a Docker image modified to include additional packages.