> For the complete documentation index, see [llms.txt](https://documentation.immuta.com/SaaS/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.immuta.com/SaaS/configuration/integrations/aws-lake-formation/reference-guides/accessing-data.md).
# Accessing Data
Learn how end users can access policy-enforced data using Amazon Athena, Amazon EMR Spark, or Amazon Redshift Spectrum
Once data is registered through the AWS Lake Formation connection, you will access your data in one of these AWS analytic engines as you normally would:
* Amazon Athena
* Amazon EMR Spark
* Amazon Redshift Spectrum
If you are subscribed to the data source, Immuta either directly grants you access to the resource through Lake Formation or generates and assigns a Lake Formation tag to that resource to grant you access. See the [Protecting data page](/SaaS/configuration/integrations/aws-lake-formation/reference-guides/protecting-data.md#protecting-data) for details about how policies are enforced.
When you submit a query, the analytic engine requests metadata from Glue Data Catalog, which then queries Lake Formation to determine what data you are allowed to see. Then, the analytic engine requests temporary access from Lake Formation, retrieves the data from S3, and filters the data to return policy-enforced data to you.
The diagram below illustrates how the analytic engine interacts with Glue Data Catalog and Lake Formation to access data.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://documentation.immuta.com/SaaS/configuration/integrations/aws-lake-formation/reference-guides/accessing-data.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
