Fix-links (#4748)

this pr fixes broken links per [slack community user
flag](https://getdbt.slack.com/archives/C0441GSRU04/p1705243581636869)
for the following pages:
-
https://docs.getdbt.com/docs/running-a-dbt-project/using-threads#related-docs
-
https://docs.getdbt.com/best-practices/materializations/2-available-materializations
- https://docs.getdbt.com/blog/announcing-materialized-views

website/blog/2023-08-01-announcing-materialized-views.md

@@ -21,7 +21,7 @@ and updates on how to test MVs.
 
 The year was 2020. I was a kitten-only household, and dbt Labs was still Fishtown Analytics. A enterprise customer I was working with, Jetblue, asked me for help running their dbt models every 2 minutes to meet a 5 minute SLA.
 
-After getting over the initial terror, we talked through the use case and soon realized there was a better option. Together with my team, I created [lambda views](https://discourse.getdbt.com/t/how-to-create-near-real-time-models-with-just-dbt-sql/1457%20?) to meet the need.
+After getting over the initial terror, we talked through the use case and soon realized there was a better option. Together with my team, I created [lambda views](https://discourse.getdbt.com/t/how-to-create-near-real-time-models-with-just-dbt-sql/1457) to meet the need.
 
 Flash forward to 2023. I’m writing this as my giant dog snores next to me (don’t worry the cats have multiplied as well). Jetblue has outgrown lambda views due to performance constraints (a view can only be so performant) and we are at another milestone in dbt’s journey to support streaming. What. a. time.
 
@@ -32,8 +32,8 @@ Today we are announcing that we now support Materialized Views in dbt. So, what
 Materialized views are now an out of the box materialization in your dbt project once you upgrade to the latest version of dbt v1.6 on these following adapters:
 
 - [dbt-postgres](/reference/resource-configs/postgres-configs#materialized-views)
-- [dbt-redshift](reference/resource-configs/redshift-configs#materialized-views)
-- [dbt-snowflake](reference/resource-configs/snowflake-configs#dynamic-tables)
+- [dbt-redshift](/reference/resource-configs/redshift-configs#materialized-views)
+- [dbt-snowflake](/reference/resource-configs/snowflake-configs#dynamic-tables)
 - [dbt-databricks](/reference/resource-configs/databricks-configs#materialized-views-and-streaming-tables)
 - [dbt-materialize*](/reference/resource-configs/materialize-configs#incremental-models-materialized-views)
 - [dbt-trino*](/reference/resource-configs/trino-configs#materialized-view)
@@ -227,4 +227,4 @@ Depending on how you orchestrate your materialized views, you can either run the
 
 ## Conclusion
 
-Well, I’m excited for everyone to remove the lines in your packages.yml that installed your experimental package (at least if you’re using it for MVs) and start to get your hands dirty. We are still new in our journey and I look forward to hearing all the things you are creating and how we can better our best practices in this.
+Well, I’m excited for everyone to remove the lines in your packages.yml that installed your experimental package (at least if you’re using it for MVs) and start to get your hands dirty. We are still new in our journey and I look forward to hearing all the things you are creating and how we can better our best practices in this.

website/docs/best-practices/materializations/materializations-guide-2-available-materializations.md

@@ -9,12 +9,12 @@ hoverSnippet: Read this guide to understand the different types of materializati
 
 Views and tables and incremental models, oh my! In this section we’ll start getting our hands dirty digging into the three basic materializations that ship with dbt. They are considerably less scary and more helpful than lions, tigers, or bears — although perhaps not as cute (can data be cute? We at dbt Labs think so). We’re going to define, implement, and explore:
 
-- 🔍 **views**
-- ⚒️ **tables**
-- 📚 **incremental model**
+- 🔍 [**views**](/docs/build/materializations#view)
+- ⚒️ [**tables**](/docs/build/materializations#table)
+- 📚 [**incremental model**](/docs/build/materializations#incremental)
 
 :::info
-👻 There is a fourth default materialization available in dbt called **ephemeral materialization**. It is less broadly applicable than the other three, and better deployed for specific use cases that require weighing some tradeoffs. We chose to leave it out of this guide and focus on the three materializations that will power 99% of your modeling needs.
+👻 There is a fourth default materialization available in dbt called [**ephemeral materialization**](/docs/build/materializations#ephemeral). It is less broadly applicable than the other three, and better deployed for specific use cases that require weighing some tradeoffs. We chose to leave it out of this guide and focus on the three materializations that will power 99% of your modeling needs.
 :::
 
 **Views and Tables are the two basic categories** of object that we can create across warehouses. They exist natively as types of objects in the warehouse, as you can see from this screenshot of Snowflake (depending on your warehouse the interface will look a little different). **Incremental models** and other materializations types are a little bit different. They tell dbt to **construct tables in a special way**.

website/docs/docs/running-a-dbt-project/using-threads.md

@@ -22,5 +22,5 @@ You will define the number of threads in your `profiles.yml` file (for dbt Core
 
 
 ## Related docs
-- [About profiles.yml](https://docs.getdbt.com/reference/profiles.yml)
+- [About profiles.yml](/docs/core/connect-data-platform/profiles.yml)
 - [dbt Cloud job scheduler](/docs/deploy/job-scheduler)

website/vercel.json

@@ -2,6 +2,11 @@
   "cleanUrls": true,
   "trailingSlash": false,
   "redirects": [
+    {
+      "source": "/reference/profiles.yml",
+      "destination": "/docs/core/connect-data-platform/profiles.yml",
+      "permanent": true
+    },
     {
       "source": "/docs/cloud/dbt-cloud-ide/dbt-cloud-tips",
       "destination": "/docs/build/dbt-tips",