From fedc9fe3333a21dec29e3640c53348272944ffed Mon Sep 17 00:00:00 2001
From: lanonacat <79870708+lanonacat@users.noreply.github.com>
Date: Wed, 4 Oct 2023 10:27:57 -0700
Subject: [PATCH] Update overview.md

---
 website/docs/docs/about/overview.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/docs/about/overview.md b/website/docs/docs/about/overview.md
index e34866fa3fe..4681b0e41e9 100644
--- a/website/docs/docs/about/overview.md
+++ b/website/docs/docs/about/overview.md
@@ -31,7 +31,7 @@ There are many other optimizations in the dbt to help you work quickly: macros,
 
 It does. Here’s how:
 
-- Writing SQL frequently involves a lot of copy-paste, which leads to errors when logic changes. With dbt, analysts don’t need to copy-paste. Instead, they build reusable data models that then get pulled into subsequent models and analysis. Change a model once and everything that relies on it reflects that change.
+- Writing SQL frequently involves a lot of copy-paste, which leads to errors when logic changes. With dbt, analysts don’t need to copy-paste. Instead, they build reusable data models that  get pulled into subsequent models and analysis. Change a model once and everything that relies on it reflects that change.
 - dbt allows subject matter experts to publish the canonical version of a particular data model, encapsulating all complex business logic. All analysis on top of this model will incorporate the same business logic without needing to understand it.
 - dbt plays nicely with source control. Using dbt, analysts can use mature source control processes like branching, pull requests, and code reviews.
 - dbt makes it easy and fast to write functional tests on the underlying data. Many analytic errors are caused by edge cases in the data: testing helps analysts find and handle those edge cases.