drt-init

Guide the user through initializing a new drt project.

Steps

1. Confirm drt is installed. Pick the install line for the user's source:

   pip install drt-core                 # core (DuckDB / SQLite / REST API source — no extras)
   pip install drt-core[bigquery]       # BigQuery source
   pip install drt-core[postgres]       # Postgres / Redshift source
   pip install drt-core[mysql]          # MySQL source
   pip install drt-core[clickhouse]     # ClickHouse source
   pip install drt-core[snowflake]      # Snowflake source
   pip install drt-core[databricks]     # Databricks source
   pip install drt-core[sqlserver]      # SQL Server source
   

   (Or uv add drt-core[...] if the user prefers uv.)

2. Pick the right starter shape. Two flows produce the same project skeleton:

   A) Template scaffolds (fastest, ~3 commands) — best when the user just wants something running:

   mkdir my-drt-project && cd my-drt-project
   drt init --template duckdb_to_rest        # DuckDB → REST API (no accounts needed, uses httpbin.org)
   

   Other templates:

   drt init --template list                  # see all available templates
   drt init --template postgres_to_slack     # operational alerts
   drt init --template duckdb_to_hubspot     # CRM-style upsert
   

   Each template prints next-steps for the env vars / source data it needs.

   B) Interactive wizard (guided) — best when the user wants to be walked through a real warehouse setup:

   mkdir my-drt-project && cd my-drt-project
   drt init
   

   Prompts for project name, source type (any of: bigquery / duckdb / sqlite / postgres / redshift / clickhouse / snowflake / mysql / databricks / sqlserver / rest_api), source-specific connection fields, and auth method.

   Either flow writes:

   my-drt-project/
   ├── drt_project.yml
   └── syncs/<name>.yml      # first sync, runnable
   

   plus credentials to ~/.drt/profiles.yml.

3. Set up auth for the chosen source (only the relevant bullet):

   • BigQuery: gcloud auth application-default login or export GOOGLE_APPLICATION_CREDENTIALS=/path/to/sa.json
   • Postgres / Redshift / MySQL / ClickHouse / Snowflake / SQL Server: set the password env var the profile references (e.g. export DRT_PG_PASSWORD=...)
   • Databricks: set DATABRICKS_TOKEN (personal access token from Workspace → User Settings)
   • DuckDB / SQLite: nothing — file path only
   • REST API source: set the bearer token env var the profile references (e.g. export REST_API_TOKEN=...)

4. Validate the setup:

   drt doctor       # env-level triage — catches missing env vars, malformed profile, etc.
   drt validate     # YAML schema check
   drt list         # confirm syncs are discovered
   

5. Offer to create a first sync using the /drt-create-sync skill.

Tips

• drt_project.yml selects which profile from ~/.drt/profiles.yml to use; override per-run with --profile <name> or DRT_PROFILE.
• Put each sync in a separate syncs/<name>.yml file (sync discovery is glob-based).
• drt sources --detailed and drt destinations --detailed print every connector's required env vars and a sample YAML stanza — useful when hand-authoring beyond the templates.
• drt run --dry-run runs through the engine without writing data; --dry-run --diff previews record-level changes for queryable destinations.
• drt status shows recent run results; drt status --output json is the CI-friendly form.
• For non-US BigQuery datasets, set location in profiles.yml (e.g. "EU", "asia-northeast1").
• Want to try drt without installing locally? Open the repo in GitHub Codespaces — the devcontainer ships drt + a seeded DuckDB warehouse + the duckdb_to_rest template pre-scaffolded.