diff --git a/website/blog/2020-07-01-how-to-create-near-real-time-models-with-just-dbt-sql.md b/website/blog/2020-07-01-how-to-create-near-real-time-models-with-just-dbt-sql.md
index 944d6fdd3f9..cdfd4da5f5d 100644
--- a/website/blog/2020-07-01-how-to-create-near-real-time-models-with-just-dbt-sql.md
+++ b/website/blog/2020-07-01-how-to-create-near-real-time-models-with-just-dbt-sql.md
@@ -13,6 +13,13 @@ date: 2020-07-01
 is_featured: false
 ---
 
+:::caution More up-to-date information available
+
+Since this blog post was first published, many data platforms have added support for [materialized views](/blog/announcing-materialized-views), which are a superior way to achieve the goals outlined here. We recommend them over the below approach. 
+
+
+:::
+
 Before I dive into how to create this, I have to say this. **You probably don’t need this**. I, along with my other Fishtown colleagues, have spent countless hours working with clients that ask for near-real-time streaming data. However, when we start digging into the project, it is often realized that the use case is not there. There are a variety of reasons why near real-time streaming is not a good fit. Two key ones are:
 
 1.  The source data isn’t updating frequently enough.
diff --git a/website/docs/docs/dbt-versions/release-notes/04-Sept-2023/ci-updates-phase2-rn.md b/website/docs/docs/dbt-versions/release-notes/04-Sept-2023/ci-updates-phase2-rn.md
index 2a1976883f8..fd2d163b748 100644
--- a/website/docs/docs/dbt-versions/release-notes/04-Sept-2023/ci-updates-phase2-rn.md
+++ b/website/docs/docs/dbt-versions/release-notes/04-Sept-2023/ci-updates-phase2-rn.md
@@ -3,7 +3,7 @@ title: "Update: Improvements to dbt Cloud continuous integration"
 description: "September 2023: dbt Cloud now has two types of jobs -- deploy jobs and CI jobs -- with streamlined setup and improved efficiency. "
 sidebar_label: "Update: Improvements to dbt jobs"
 tags: [Sept-2023, CI]
-date: 2023-09-15
+date: 2023-09-11
 sidebar_position: 10
 ---