setup databricks connect on VsCode and PyCharm

seefoods — Thu, 04 Dec 2025 13:20:08 GMT

Hello Guyz,

Someone Know what's is the best pratices to setup databricks connect for Pycharm and VsCode using Docker, Justfile and .env file

Cordially,
Seefoods

Re: setup databricks connect on VsCode and PyCharm

Gecofer — Sun, 07 Dec 2025 17:55:53 GMT

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>

VSCode reads this automatically. So for the plugin, .databrickscfg is definitely the best practice.

4.- When building external services (FastAPI, Uvicorn, etc.), .env is useful

For microservices calling Databricks Model Serving, I do use .env, for example:

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

Two options to load the .env:

1) Use --env-file directly with Uvicorn (supported natively). This works perfectly inside a Justfile:

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

2. Load .env inside your FastAPI app

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")

In this case, your Justfile stays:

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

And your service reads the .env programmatically.

At the end of the day, both approaches are valid and in my experience, which one you use really depends on the specific use case. For local development and running things directly on Databricks, I prefer the VSCode plugin + .databrickscfg. For external services (FastAPI, Uvicorn, Justfile deployments), .env works perfectly.

So depending on what you want to build, you will naturally choose one approach or the other.

Hope it helps!

Gema 👩‍💻

Re: setup databricks connect on VsCode and PyCharm

seefoods — Wed, 17 Dec 2025 14:40:15 GMT

Hello Mrs @Gecofer ,

Thanks for your response.

Cordially,

topic setup databricks connect on VsCode and PyCharm in Data Engineering

setup databricks connect on VsCode and PyCharm

Re: setup databricks connect on VsCode and PyCharm

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

3.- Use .databrickscfg for VSCode authentication

4.- When building external services (FastAPI, Uvicorn, etc.), .env is useful

Re: setup databricks connect on VsCode and PyCharm