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Athena Preview #6044

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Sep 10, 2024
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Original file line number Diff line number Diff line change
Expand Up @@ -5,13 +5,7 @@ description: "Configure the Amazon Athena data platform connection in dbt Cloud.
sidebar_label: "Connect Amazon Athena"
---

# Connect Amazon Athena <Lifecycle status="beta" />

:::note beta

This is a beta feature with limited availability. A public preview will follow shortly, for wider early access. For more information, check out our [product lifecycle](/docs/dbt-versions/product-lifecycles#dbt-cloud) page.

:::
# Connect Amazon Athena <Lifecycle status="preview" />

Your environment(s) must be on ["Versionless"](/docs/dbt-versions/versionless-cloud) to use the Amazon Athena connection.

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Expand Up @@ -2,8 +2,8 @@
title: "Athena setup"
description: "Read this guide to learn about the Athena warehouse setup in dbt."
meta:
maintained_by: Community
authors: Community
maintained_by: dbt Labs
authors: dbt Labs
github_repo: 'dbt-athena/dbt-athena'
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pypi_package: 'dbt-athena-community'
min_core_version: 'v1.3.0'
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4 changes: 4 additions & 0 deletions website/docs/docs/dbt-versions/release-notes.md
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Expand Up @@ -18,6 +18,10 @@ Release notes are grouped by month for both multi-tenant and virtual private clo

\* The official release date for this new format of release notes is May 15th, 2024. Historical release notes for prior dates may not reflect all available features released earlier this year or their tenancy availability.

## September 2024

- **New:** The [Amazon Athena warehouse connection](/docs/cloud/connect-data-platform/connect-amazon-athena) is available as a public preview for dbt Cloud accounts that have upgraded to [`versionless`](/docs/dbt-versions/versionless-cloud).

## August 2024
- **Fix:** Fixed an issue in [dbt Explorer](/docs/collaborate/explore-projects) where navigating to a consumer project from a public node resulted in displaying a random public model rather than the original selection.
- **New**: You can now configure metrics at granularities at finer time grains, such as hour, minute, or even by the second. This is particularly useful for more detailed analysis and for datasets where high-resolution time data is required, such as minute-by-minute event tracking. Refer to [dimensions](/docs/build/dimensions) for more information about time granularity.
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