Merge pull request #127 from canefeoglu/patch-1

update databricks sql warehouse naming

Author: rtyler

_posts/2022-06-28-databricks-serverless.md

@@ -11,22 +11,22 @@ 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
 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