If you’ve never used VSCode with Python, I strongly recommend at least the first half of Dan Taylor’s Get Productive with Python in Visual Studio Code talks.

quick switching b/w a model file and it’s compiled and run counterparts.graying out the text of files that have compiled code in them, to help prevent you from editing compiled SQL (as opposed to your actual model).syntax highlighting for SQL files with jinja in them.a stable, reproducible Python environment for running dbt.Goalsįollowing this guide will enable the following behavior in VSCode (some points lifted from Claire’s guide – linked above) I’m also going to add things to make it easier for working with Azure databases such as the Azure CLI and Azure Data Studio. I thought I’d consolidate some of this into a single article, and expand on it given the recent developments. Many folks commented on how they acheived similar productivity using VSCode. The post details how the dbt team uses Atom and iTerm 2 on macOS for an improved workflow.

When our team first started using the dbt CLI, we started with Claire’s well-loved discourse post, How we set up our computers for working on dbt project. For the most-up-to-date version, you might want to go here: Using VSCode with dbt | dbt-sqlserver-docs Intro

Visual studio code virtualenv