From dcb93cff4155ed89709b6853061360675b17e525 Mon Sep 17 00:00:00 2001
From: Ly Nguyen <107218380+nghi-ly@users.noreply.github.com>
Date: Tue, 1 Oct 2024 14:12:15 -0700
Subject: [PATCH] Update website/blog/2024-10-05-snowflake-feature-store.md

Co-authored-by: Matt Shaver <60105315+matthewshaver@users.noreply.github.com>
---
 website/blog/2024-10-05-snowflake-feature-store.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/blog/2024-10-05-snowflake-feature-store.md b/website/blog/2024-10-05-snowflake-feature-store.md
index 1e4dde50b35..f51a0137d43 100644
--- a/website/blog/2024-10-05-snowflake-feature-store.md
+++ b/website/blog/2024-10-05-snowflake-feature-store.md
@@ -88,7 +88,7 @@ Window aggregations play an important role in the creation of features. Because
 
 ```
 
-Now, we use this macro in our feature table to build out various aggregations of customer transactions over the last day, 7 days, and 30 days. Snowflake has just taken significant complexity away in generating appropriate feature values and dbt has just made the code even more readable and repeatable. While the follwing example is built in SQL, teams can also build these pipelines using Python directly. 
+Now, we use this macro in our feature table to build out various aggregations of customer transactions over the last day, 7 days, and 30 days. Snowflake has just taken significant complexity away in generating appropriate feature values and dbt has just made the code even more readable and repeatable. While the following example is built in SQL, teams can also build these pipelines using Python directly. 
 
 ```sql