diff --git a/_posts/2022-06-28-databricks-serverless.md b/_posts/2022-06-28-databricks-serverless.md
index c3956a2..1007238 100644
--- a/_posts/2022-06-28-databricks-serverless.md
+++ b/_posts/2022-06-28-databricks-serverless.md
@@ -11,14 +11,14 @@ author: hamiltonh
 
 We recently migrated Looker to a Databricks SQL Serverless, improving our
 infrastructure cost and reducing the footprint of infrastructure we need to
-worry about! “Databricks SQL” which provides a single load balanced Endpoint
+worry about! “Databricks SQL” which provides a single load balanced Warehouse
 for executing Spark SQL queries across multiple Spark clusters behind the
 scenes. “Serverless” is an evolution of that concept, rather than running a SQL
-Endpoint in our AWS infrastructure, the entirety of execution happens on the
+Warehouse in our AWS infrastructure, the entirety of execution happens on the
 Databricks side. With a much simpler and faster interface, queries executed in
 Looker now return results much faster to our users than ever before!
 
-When we originally provisioned our “Databricks SQL” endpoints, we worked
+When we originally provisioned our “Databricks SQL” warehouses, we worked
 together with our colleagues at Databricks to ensure [the terraform provider
 for Databricks](https://github.com/databricks/terraform-provider-databricks) is
 ready for production usage, which as of today is Generally Available. That
@@ -26,7 +26,7 @@ original foundation in Terraform allowed us to more easily adopt SQL Serverless
 once it was made available to us.
 
 ```hcl
-resource "databricks_sql_endpoint" "endpoint" {
+resource "databricks_sql_warehouse" "warehouse" {
   name                      = "Looker Serverless"
   # ...
   enable_serverless_compute = true
@@ -44,9 +44,9 @@ place and setup correctly!
 
 The switch to Serverless reduced our infra configuration and management
 footprint, which by itself is an improvement. We also noticed a significant
-reduction in cold start times for the SQL Serverless Endpoint compared to the
-standard SQL Endpoint. The faster start-up times meant we could configure even
-lower auto-terminate times on the endpoint, savings us even more on
+reduction in cold start times for the SQL Serverless Warehouse compared to the
+standard SQL Warehouse. The faster start-up times meant we could configure even
+lower auto-terminate times on the warehouse, savings us even more on
 unproductive and idle cluster costs.
 
 On the Looker side there really wasn’t any difference in the connection