From 7128a4fdea8b776d56d84b1bb63ceab5d9a269e3 Mon Sep 17 00:00:00 2001
From: Mirna Wong <89008547+mirnawong1@users.noreply.github.com>
Date: Thu, 10 Oct 2024 16:05:42 +0100
Subject: [PATCH] Update website/docs/docs/dbt-versions/release-notes.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/docs/docs/dbt-versions/release-notes.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/dbt-versions/release-notes.md b/website/docs/docs/dbt-versions/release-notes.md
index 37fae011a9c..456d1bf0e82 100644
--- a/website/docs/docs/dbt-versions/release-notes.md
+++ b/website/docs/docs/dbt-versions/release-notes.md
@@ -20,7 +20,7 @@ Release notes are grouped by month for both multi-tenant and virtual private clo
 
 ## October 2024
 
-- **New**: The [dbt Semantic Layer Python software development kit](/docs/dbt-cloud-apis/sl-python) is now [generally available](/docs/dbt-versions/product-lifecycles). It provides users with easy access to the dbt Semantic Layer with Python and allows developers to interact with the dbt Semantic Layer APIs to query metrics/dimensions in downstream tools.
+- **New**: The [dbt Semantic Layer Python software development kit](/docs/dbt-cloud-apis/sl-python) is now [generally available](/docs/dbt-versions/product-lifecycles). It provides users with easy access to the dbt Semantic Layer with Python and enables developers to interact with the dbt Semantic Layer APIs to query metrics/dimensions in downstream tools.
 - **Enhancement**: You can now add a description to a singular data test in dbt Cloud Versionless. Use the [`description` property](/reference/resource-properties/description) to document [singular data tests](/docs/build/data-tests#singular-data-tests). You can also use [docs block](/docs/build/documentation#using-docs-blocks) to capture your test description. The enhancement will be included in upcoming dbt Core 1.9 release.
 - **New**: Introducing the [microbatch incremental model strategy](/docs/build/incremental-microbatch) (beta), available in dbt Cloud Versionless and will soon be supported in dbt Core 1.9. The microbatch strategy allows for efficient, batch-based processing of large time-series datasets for improved performance and resiliency, especially when you're working with data that changes over time (like new records being added daily). To enable this feature in dbt Cloud, set the `DBT_EXPERIMENTAL_MICROBATCH` environment variable to `true` in your project.
 - **New**: The dbt Semantic Layer supports custom calendar configurations in MetricFlow, available in [Preview](/docs/dbt-versions/product-lifecycles#dbt-cloud). Custom calendar configurations allow you to query data using non-standard time periods like `fiscal_year` or `retail_month`. Refer to [custom calendar](/docs/build/metricflow-time-spine#custom-calendar) to learn how to define these custom granularities in your MetricFlow timespine YAML configuration.