Reverse ETL for the code-first data stack.

drt syncs data from your data warehouse to external services — declaratively, via YAML and CLI. Think dbt run → drt run. Same developer experience, opposite data direction.

pip install drt-core          # core (DuckDB included)
drt init && drt run

No cloud accounts needed — runs locally with DuckDB in about 5 minutes.

pip install drt-core

For cloud sources: pip install drt-core[bigquery], drt-core[postgres], etc.

mkdir my-drt-project && cd my-drt-project
drt init   # select "duckdb" as source

python -c "
import duckdb
c = duckdb.connect('warehouse.duckdb')
c.execute('''CREATE TABLE IF NOT EXISTS users AS SELECT * FROM (VALUES
  (1, 'Alice', 'alice@example.com'),
  (2, 'Bob',   'bob@example.com'),
  (3, 'Carol', 'carol@example.com')
) t(id, name, email)''')
c.close()
"

# syncs/post_users.yml
name: post_users
description: "POST user records to an API"
model: ref('users')
destination:
  type: rest_api
  url: "https://httpbin.org/post"
  method: POST
  headers:
    Content-Type: "application/json"
  body_template: |
    { "id": {{ row.id }}, "name": "{{ row.name }}", "email": "{{ row.email }}" }
sync:
  mode: full
  batch_size: 1
  on_error: fail

drt run --dry-run   # preview, no data sent
drt run             # run for real
drt status          # check results

See examples/ for more: Slack, Google Sheets, HubSpot, GitHub Actions, etc.

drt init                    # initialize project
drt list                    # list sync definitions
drt run                     # run all syncs
drt run --select <name>     # run a specific sync
drt run --dry-run           # dry run
drt run --verbose           # show row-level error details
drt validate                # validate sync YAML configs
drt status                  # show recent sync status
drt status --verbose        # show per-row error details
drt mcp run                 # start MCP server (requires drt-core[mcp])

Connect drt to Claude, Cursor, or any MCP-compatible client so you can run syncs, check status, and validate configs without leaving your AI environment.

pip install drt-core[mcp]
drt mcp run

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "drt": {
      "command": "drt",
      "args": ["mcp", "run"]
    }
  }
}

Available MCP tools:

Install the official Claude Code skills to generate YAML, debug failures, and migrate from other tools — all from the chat interface.

/plugin marketplace add drt-hub/drt
/plugin install drt@drt-hub

Tip: Enable auto-update so you always get the latest skills when drt is updated: /plugin → Marketplaces → drt-hub → Enable auto-update

Copy the files from .claude/commands/ into your drt project's .claude/commands/ directory.

Detailed plans & progress → GitHub Milestones Looking to contribute? → Good First Issues

Community-maintained Dagster integration. Expose drt syncs as Dagster assets with full observability.

pip install dagster-drt

from dagster import Definitions
from dagster_drt import drt_assets, DagsterDrtTranslator

class MyTranslator(DagsterDrtTranslator):
    def get_group_name(self, sync_config):
        return "reverse_etl"

defs = Definitions(
    assets=drt_assets(
        project_dir="path/to/drt-project",
        dagster_drt_translator=MyTranslator(),
    )
)

See dagster-drt README for full API docs (Translator, DrtConfig dry-run, MaterializeResult).

drt is designed to work alongside, not against, the modern data stack:

See CONTRIBUTING.md.

drt is an independent open-source project and is not affiliated with, endorsed by, or sponsored by dbt Labs, dlt-hub, or any other company.

"dbt" is a registered trademark of dbt Labs, Inc. "dlt" is a project maintained by dlt-hub.

drt is designed to complement these tools as part of the modern data stack, but is a separate project with its own codebase and maintainers.

Apache 2.0 — see LICENSE.