Hi @seefoods!

I’ve worked with Databricks Connect and VSCode in different projects, and although your question mentions Docker, Justfile and .env, the “best practices” really depend on what you’re trying to do. Here’s what has worked best for me:

1.- Databricks Connect → for local development inside VSCode

I mainly use Databricks Connect to:

• run Spark code locally
• get autocompletion and type hints
• debug transformations
• quickly test code before sending it to a Databricks cluster

It’s perfect for the development loop inside VSCode.

2.- Databricks VSCode extension → for running real workloads on Databricks

For me, this is the cleanest workflow when you want to run things on the platform:

• run scripts/notebooks on a cluster
• browse catalogs, tables, repos
• switch profiles
• see job logs and outputs

So the combination works really well:

Databricks Connect → local dev experience

VSCode extension → execute remotely on Databricks

3.- Use .databrickscfg for VSCode authentication

What is the Databricks extension for Visual Studio Code?

The Databricks VSCode extension expects credentials in: ~/.databrickscfg

[DEFAULT]
host = https://<workspace-url>
token = <PAT> 

Or multiple profiles:

[dev] 
host = https://dev-workspace 
token = <token-dev> 

[prod] 
host = https://prod-workspace 
token = <token-prod> 

DATABRICKS_HOST=https://<workspace-url> 
DATABRICKS_TOKEN=<token> 
MODEL_SERVING_ENDPOINT=/serving-endpoints/my-model/invocations 

run: 
  uvicorn app.main:app --host 0.0.0.0 --port 8080 --env-file .env 

from dotenv import load_dotenv
import os

load_dotenv()

host = os.getenv("DATABRICKS_HOST")
token = os.getenv("DATABRICKS_TOKEN")
endpoint = os.getenv("MODEL_SERVING_ENDPOINT") 

run:
  uvicorn app.main:app --host 0.0.0.0 --port 8080 

Hello Mrs @Gecofer , 

Thanks for your response. 

Cordially,