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update databricks sql warehouse naming 

endpoint => warehouse
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canefeoglu 2023-03-20 13:36:17 -07:00 committed by GitHub
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14
_posts/2022-06-28-databricks-serverless.md
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@ -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