VSCode

From a fresh laptop to a working data-creator environment: extensions installed, interpreter wired, first dbt model running, in under thirty minutes.

The organizing pattern behind a modern VS Code data-creator setup: the editor stays on the laptop, the compute stays in the cloud, and the extensions wire the two together invisibly.

Copilot, Claude Code, Cursor, Continue, Cline, Amazon Q: what each is good at, how they coexist, and how to avoid paying for four of them.

How extensions extend, where they live, how to pin and audit them, and why the marketplace is a supply-chain surface that deserves governance.

Reproducible dev environments, shipped in the repo, identical for every engineer. The fix for 'works on my machine' that actually holds.

What fast feedback actually looks like per stack — the 5-to-30-second write-run-inspect-fix cycle that divides productive data creators from slow ones.

Launch configs per workload, conditional breakpoints, data breakpoints, query-profile reading. The single highest-leverage skill for data engineers.

Give Claude Code, Cursor, or Copilot real tools: Databricks managed MCP, dbt Power User embedded MCP, GitHub, and the scoping rules that keep agents safe.

The `code` CLI, the command palette, the shortcuts that save seconds per iteration, and the Tasks that save minutes per day.

The Causeway rules for a data-creator repo's `.vscode/`, `.devcontainer/`, and agent configuration. Enforced in PR review.

What the paved-path VS Code environment must satisfy before a team calls it 'done,' and keep satisfying after. The operational gate platform owns.

Every extension ID the paved path ships, what it does, what settings it introduces, and the known sharp edges.

The settings.json, launch.json, and tasks.json recipes every data-creator workspace ships, plus the anti-patterns to avoid.