Open-source ETL/ELT on DuckDB. Write, wire, or draw one pipeline: 350+ components, 160+ connectors, dbt, CDC, data quality, a Python API, and MCP for AI agents.
Open-source ETL/ELT on DuckDB. Write, wire, or draw one pipeline: 350+ components, 160+ connectors, dbt, CDC, data quality, a Python API, and MCP for AI agents.
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An open-source ETL platform you run on your own infrastructure. Drag sources, transforms, validators and sinks onto a canvas, wire them together, and press Run. Duckle compiles the graph to SQL and executes it on a real columnar engine, with live previews, the generated SQL visible on every node, and no hidden state.
You build a pipeline on a laptop and deploy that same file to a server, where it runs on a schedule under a web console with roles, alerts and an audit log. Nothing is rewritten in between, and nothing is metered.
In short: a free, open-source, single-engine alternative to hosted, per-row-priced ETL platforms like Fivetran and Airbyte - one pipeline for ingest, transform, and load that runs anywhere, and can also run dbt on DuckDB inside the same tool.
Three things set it apart:
| Visual, never opaque | The canvas compiles to SQL you can read, and every node has a live preview tab. No black box. |
| An assistant with no API key | Runs in-process by default, or against your own OpenAI-compatible endpoint. Your prompts and your data stay inside your infrastructure either way. |
| Single-file binary, no bundled DB | 73 to 110 MB depending on platform (it embeds the headless runner + MCP server). DuckDB downloads on first launch with a guided step. AI engine is opt-in. |
| Native speed | Execution runs through DuckDB: vectorized, columnar, local. A clean-and-export job that crawls in a spreadsheet finishes in milliseconds. |
| Git-friendly by design | Pipelines, connections, contexts, and routines persist as plain files in a folder you pick. Diff them, branch them, review them. |
| 400+ components ready today | Files, databases, warehouses, lakehouses, object stores, SaaS APIs, NoSQL, streaming brokers, vector DBs, FTP, IMAP, SMTP. Each is covered by tests. |
| Honest about scope | Single-machine and embedded by design. Built to make local and small-team data work fast, not to replace a distributed warehouse. |
| 60 UI languages | Topbar, palette, chat assistant, properties panel, and common dialogs ship localized. English, Spanish, Chinese (Simplified + Traditional), Hindi, Arabic, Portuguese (Brazil), Bengali, Russian, Japanese, Punjabi, German, Korean, French, Vietnamese, Telugu, Marathi, Turkish, Tamil, Urdu, Persian, Polish, Italian, Ukrainian, Indonesian, Thai, Dutch, Hebrew, Swedish, Greek, Czech, Hungarian, Romanian, Filipino, Malay, Norwegian, Danish, Finnish, Catalan, Bulgarian, Slovak, Croatian, Serbian, Slovenian, Lithuanian, Latvian, Estonian, Khmer, Burmese, Sinhala, Nepali, Swahili, Afrikaans, Welsh, Irish, Icelandic, Albanian, Azerbaijani, Mongolian, Kazakh. RTL (Arabic, Hebrew, Persian, Urdu) supported. Switch languages from the topbar globe. |
| Open source | Dual-licensed MIT OR Apache-2.0. Yours to use, fork, and extend. |
Real pipelines, built and run in Duckle - not mockups.
A 5M-row pipeline: a CSV, a Parquet file, a DuckDB table, and a SQLite table enriched through one visual Map (3-way join), no SQL.
Left: the visual Map editor - main plus lookups, per-output expressions, an inline filter. Right: Parallelize fanning out aggregate, window, and top-N branches.
One run, many branches: 16 nodes finish in a few seconds. Concurrency auto-detects from CPU cores; branches write to Parquet, CSV, DuckDB, and SQLite at once.
Left: DuckLake CDC change-feed mirrored via upsert + delete propagation (100k rows). Right: watermark incremental load over 5M rows, advancing state only on a fully successful run.
samples/orders.csv, hit Autodetect schema. Drag a Filter, wire it up. Drag a Parquet sink with an output path. Press Run, watch the nodes light up.That's a real, native ETL pipeline built and run in under a minute. CSV is just the easiest first node; swap in Parquet, JSON, S3, Snowflake, MongoDB, or Stripe the same way.
Pick the binary for your OS from the latest release:
| OS | Asset | How to run |
|---|---|---|
| Windows | Duckle-windows-x64.exe |
Double-click. Unsigned binary - Windows SmartScreen will warn the first time; click "More info" -> "Run anyway". |
| macOS (Apple Silicon) | Duckle-macos-arm64 |
chmod +x Duckle-macos-arm64 && ./Duckle-macos-arm64. Right-click -> Open the first time to bypass Gatekeeper. |
| Linux (x86_64) | Duckle-linux-x64 |
chmod +x Duckle-linux-x64 && ./Duckle-linux-x64. Requires WebKitGTK 4.1 (libwebkit2gtk-4.1-0 on Debian / Ubuntu). |
The single-file binary above is all you need for Build Pipeline too: the headless runner is embedded into the app at build time, and exporting a pipeline produces ONE self-contained executable (the engine, the DuckDB CLI, any needed extensions, and the resolved pipeline are all inside that one file). Copy that single file to your server and run or schedule it - no separate runner download required.
One command, nothing installed: it scaffolds sample data and a pipeline, compiles it to SQL, runs it on DuckDB, and shows you the rows.
uvx duckle quickstart
Paste this into Claude Code, Cursor, or Codex:
Run
uvx duckle quickstartto build my first pipeline and run it
Nothing to install first. The agent fetches Duckle and the DuckDB engine on demand, runs a real pipeline, and shows you the rows.
If you do not want the desktop studio, install just the headless runner. It is about 27 MB rather than 100 MB or more, has no GUI dependency, and is what a build step actually needs.
pip install duckle
That is the whole install. It brings the DuckDB CLI with it (via the duckdb-cli package published by the DuckDB Foundation), so there is nothing else to fetch and it works offline. Wheels ship for Linux, macOS and Windows on x86-64 and arm64.
It also gives you a Python API, where pipelines are built as code and executed by DuckDB rather than by Python:
import duckle
from duckle import col
(duckle.read_csv("orders.csv")
.where(col.amount >= 20)
.derive(total="round(amount * 1.2, 2)")
.write_parquet("out.parquet")
.run())
Python expressions compile to vectorized SQL at plan time, so no rows pass through the interpreter. See the PyPI page for the full API.
The same package provides the duckle command-line runner for CI, cron, and containers - it bundles the headless runner and the MCP server per platform:
pip install duckle # or run ad hoc, no install: uvx duckle --help
Pipelines execute as SQL on the DuckDB CLI, so the runner needs a duckdb on PATH or DUCKLE_DUCKDB_BIN set (pip install duckdb-cli is the quickest route). Validation does not:
duckle validate # compile-check every pipeline under ./pipelines
duckle validate --json # machine-readable, for a CI step
duckle --pipeline my.json # run one
validate opens no source and writes no sink, so it needs no engine, no credentials and no network. Exit codes are stable: 0 clean, 1 a real finding (a pipeline failed or did not compile), 2 the runner could not start (bad usage, unreadable file, missing engine).
The binary is 73 to 110 MB depending on platform (it embeds the headless runner and the bundled MCP server). On first launch you'll be guided through downloading two engines into your app-data directory:
| Engine | Size | Required? | What it powers |
|---|---|---|---|
| DuckDB CLI | ~30 MB + extensions | Yes - cannot run pipelines without it | Every source / transform / sink that runs as SQL |
| Duckie AI Assistant | ~1.1 GB (llama-server + Qwen 2.5 Coder 1.5B GGUF) | Optional | The chat sidebar that generates pipelines from natural language |
App-data location:
%APPDATA%\io.duckle.app\engines\No open issues yet, or sync has not completed.