# Amazon Athena workflow

> Amazon Athena workflow - Read in an Amazon Athena table to create a project and deploy a model to
> make predictions with a test dataset.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.573157+00:00` (UTC).

## Primary page

- [Amazon Athena workflow](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-athena.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [AI integrations and platforms](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_Athena_template/AWS_Athena_End_to_End.ipynb)

Being one of the largest cloud providers in the world, AWS has multiple ways of storing data within its cloud. Read more to find out how to integrate DataRobot with your data. In this accelerator integration with Athena, you will create a JDBC data source within DataRobot to connect to Athena and then pull data in via an SQL query.

This accelerator notebook covers the following activities:

- Read in an Amazon Athena table and upload it to DataRobot's AI Catalog
- Create a project with the dataset
- Deploy the top-performing model to a DataRobot prediction server
- Make batch predictions with a test dataset