# Hydra ETL

**Your data pipelines are described, not programmed.**

[![CI](https://github.com/bejaouibechir/Hydra/actions/workflows/ci.yml/badge.svg)](https://github.com/bejaouibechir/Hydra/actions/workflows/ci.yml)
[![PyPI](https://img.shields.io/pypi/v/hydra-etl.svg)](https://pypi.org/project/hydra-etl/)
[![Python](https://img.shields.io/pypi/pyversions/hydra-etl.svg)](https://pypi.org/project/hydra-etl/)
[![License: AGPL v3](https://img.shields.io/badge/license-AGPL--3.0--or--later-blue.svg)](LICENSE)
[![Status: beta](https://img.shields.io/badge/status-beta-orange.svg)](#project-status)
[![Glama quality score](https://glama.ai/mcp/servers/bejaouibechir/Hydra/badges/score.svg)](https://glama.ai/mcp/servers/bejaouibechir/Hydra)

**[Website](https://hydraetl.com)** · **[Docs & playground](https://hydraetl.com/playground)** · **[MCP server](docs/MCP.md)** · **[VS Code extension](#vs-code-extension)** · **[Changelog](CHANGELOG.md)**

Hydra is an open-source declarative ETL engine. You write what a pipeline *is* in YAML;
Hydra validates it before touching any data, then runs it. The same manifests run from
the terminal, a REST API, or a visual editor in your browser, with one `pip install` and nothing else to deploy.

## Try it without installing anything

Two sandboxes run Hydra in your browser. Nothing is installed on your machine,
and nothing is left behind when you close the tab.

### Guided labs — no account needed

**[killercoda.com/hydra-etl](https://killercoda.com/hydra-etl)**

Killercoda gives you a Linux terminal inside your browser, next to a short
lesson that tells you exactly what to type. Each lab takes ten to fifteen
minutes and checks your work as you go. There is nothing to sign up for.

Start with *Meet hdrctl* to see what a job is, then *Parameters and secrets* to
run one pipeline against two databases.

### A full environment, with the visual editor

[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/bejaouibechir/hydra-demo)

GitHub Codespaces starts a real machine in the cloud and opens an editor in your
browser. It needs a free GitHub account, and it runs within GitHub's free
monthly allowance.

This one comes with a MySQL database already running beside it and a working
pipeline. **Once it opens, type `./tour`** — a five-minute walkthrough that
shows you each command before running it. It is the only thing you have to type.

Unlike the labs, this environment includes **Hydra Studio**, the visual editor,
so you can see the same pipeline as a diagram and run it from there.

<!-- TODO: replace with a short GIF: build a job in Studio → hdrctl validate → hdrctl run -->

<img src="assets/hydrastudio.png" alt="Hydra Studio: visual editor for jobs and workflows" width="720">

---

## Quick start

```bash
pip install "hydra-etl[server]"
hdrctl serve --open            # Studio + API on http://localhost:5678
```

Or stay in the terminal:

```bash
hdrctl init my_job             # scaffold one of six templates
hdrctl validate my_job         # strict validation, no data touched
hdrctl run my_job              # execute
```

No Node, no build step, no database, no message broker. Python 3.9+ on Linux, macOS or Windows.

---

## Why Hydra

- **Validate before you run.** `hdrctl validate` checks sources, steps, types and destinations
  deterministically, before any data is read or written.
- **Declarative, visual, zero deployment.** YAML manifests, a browser editor that outputs the same YAML,
  and a single Python process. No cluster, no JVM, no container required.
- **Built for CI/CD.** Manifests are versioned and reviewed like code; `hdrctl` scaffolds, validates,
  tests and runs from a terminal or a CI job.
- **Built-in scheduler.** DAG workflows with dependencies, parallel branches, delayed retries, cron triggers,
  conditional guards and eleven actions (webhook, email, Bash, PowerShell, SSH, Python…).
- **One job, many environments.** `{{ param: }}`, `{{ env: }}` and `${SECRET:}` keep the manifest identical
  across dev, staging and production.
- **Two engines per operation.** pandas or DuckDB, chosen step by step, with optional Rust acceleration
  (see [Native acceleration](#native-acceleration-optional)).
- **AI-ready.** An MCP server lets Claude, Cursor or VS Code write pipelines that Hydra validates.

<p>
  <img src="assets/3/validate-before-run.svg" alt="Validation before execution" width="49%">
  <img src="assets/2/declarative-visual-zero-deploy.svg" alt="Declarative, visual, zero deployment" width="49%">
</p>

### How it compares

|                      | Hydra                    | Airflow                           | dbt                        | Airbyte             | NiFi / Apache Hop        |
| -------------------- | ------------------------ | --------------------------------- | -------------------------- | ------------------- | ------------------------ |
| Pipelines defined in | YAML                     | Python                            | SQL + YAML                 | UI / config         | UI flows                 |
| Scope                | Extract, transform, load | Orchestration                     | Transform in the warehouse | Extract & load      | Extract, transform, load |
| Visual editor        | Included                 | Monitoring UI                     | Not in dbt Core            | Included            | Included                 |
| To get started       | `pip install`            | Scheduler, webserver, metadata DB | A data warehouse           | Docker / Kubernetes | JVM                      |

Hydra is not a replacement for all of these. It targets file-and-database pipelines that should stay
readable and run without extra infrastructure.

---

## What you get

| Component     | Description                                                              |
| ------------- | ------------------------------------------------------------------------ |
| **Engine**    | Declarative jobs: one source, N transformations, one destination         |
| **CLI**       | `hdrctl`: scaffold, validate, run, inspect. English and Spanish          |
| **API**       | FastAPI, with interactive docs at `/docs`                                |
| **Studio**    | Visual editor for jobs and workflows, served by the same process         |
| **Workflows** | Multi-job DAG with dependencies, actions, retries and runtime parameters |
| **MCP**       | Natural-language pipeline authoring, validated by Hydra                  |

**Connectors** (sources and destinations):

| <img src="assets/connectors/csv.png" alt="" width="40"><br>CSV | <img src="assets/connectors/json.png" alt="" width="40"><br>JSON | <img src="assets/connectors/parquet.png" alt="" width="40"><br>Parquet | <img src="assets/connectors/mysql.png" alt="" width="40"><br>MySQL / MariaDB | <img src="assets/connectors/postgresql.png" alt="" width="40"><br>PostgreSQL | <img src="assets/connectors/mongo.png" alt="" width="40"><br>MongoDB | <img src="assets/connectors/api.png" alt="" width="40"><br>Web API |
|:--------------------------------------------------------------:|:----------------------------------------------------------------:|:----------------------------------------------------------------------:|:----------------------------------------------------------------------------:|:----------------------------------------------------------------------------:|:--------------------------------------------------------------------:|:------------------------------------------------------------------:|

**Transformation engines:** pandas and DuckDB.

---

## Who it is for

- **Data engineers and analysts** who need repeatable file-and-database pipelines without standing up
  infrastructure for them.
- **Teams where pipelines must stay readable** by people who do not write Python: a YAML manifest
  reviewed in a pull request, not a script.
- **Developers embedding ETL in a product**, who want a CLI and a REST API over the same engine.

---

## A job in four files

A job is a folder. Four manifests describe it, and each one answers a single question.

**`sources.yaml`**: where the data comes from

```yaml
version: "1.0"
sources:
  src_input:
    type: csv
    extract:
      table: ./input.csv
```

**`transformations.yaml`**: how it is reshaped

```yaml
version: "1.0"
steps:
  - cast:
      mapping:
        amount: float
  - filter:
      expr: "amount > 0"
  - aggregate:
      by: [name]
      agg:
        total: { func: sum, col: amount }
```

A CSV carries no types, so `cast` comes before any numeric comparison.

**`destinations.yaml`**: where it goes

```yaml
version: "1.0"
destinations:
  dest_output:
    type: csv
    load:
      table: ./output.csv
      mode: replace          # append | replace | upsert
```

**`pipeline.yaml`**: which source feeds which destination

```yaml
version: "1.0"
pipeline:
  from: src_input
  to: dest_output
```

```bash
hdrctl validate my_job && hdrctl run my_job
```

---

## Workflows: order several jobs

```yaml
version: "1.0"
workflow:
  name: daily_etl
  trigger:
    type: schedule
    cron: "0 8 * * *"
  steps:
    - name: extract
      type: job
      job: ./jobs/extract
      depends_on: []

    - name: transform
      type: job
      job: ./jobs/transform
      depends_on: ["extract"]     # always a list, supports fan-in

    - name: notify
      type: action
      action: webhook
      params: { url: "{{ env:WEBHOOK_URL }}" }
      depends_on: ["transform"]
      on_failure: skip
```

```bash
hdrctl workflow validate ./workflow.yaml
hdrctl workflow run      ./workflow.yaml
```

An edge is a **dependency**, not a pipe: it decides *when* a job runs, never what data reaches it.
Steps that share no dependency run in parallel.

<img src="assets/5/dag-workflows-built-in-scheduler.svg" alt="DAG workflows with a built-in scheduler" width="720">

---

## Parameters

Values can be declared once and reused, or created while the workflow runs.

```yaml
- filter:
    expr: "region == '{{ param:region }}'"
```

`{{ param:NAME }}` reads a parameter and `{{ env:NAME }}` an environment variable.
`${SECRET:NAME}` reads a secret: it is looked up in the injected secret store when one is configured, and
otherwise in the environment, under the name upper-cased with dots and dashes turned into underscores — so
`${SECRET:db.password}` reads `DB_PASSWORD`. Secrets therefore come from the process environment (CI
variables, a secret manager, your shell) and never from a file in the repository.
The `set_param` and `assign_param` actions create and change parameters mid-run, so two jobs can share a
placeholder and produce different results.

---

## Install what you need

The base install is the engine and the CLI. Everything else is opt-in.

```bash
pip install hydra-etl                  # engine + CLI
pip install "hydra-etl[server]"        # + API + Studio
pip install "hydra-etl[postgres]"      # + PostgreSQL driver
pip install "hydra-etl[mssql]"         # + Microsoft SQL Server driver
pip install "hydra-etl[all]"           # everything
```

Available extras: `server`, `native`, `duckdb`, `parquet`, `mysql`, `postgres`, `mssql`, `mongodb`, `http`, `mcp`, `all`.

### Or run it in a container — optional

Hydra needs no container: it stays one `pip install` and one Python process.
If your team standardises on containers, or you would rather keep Python off
the host, the image carries the engine, the CLI, the API and the Studio:

```bash
docker run --rm -p 5678:5678 -v hydra-workspace:/workspace \
  ghcr.io/bejaouibechir/hydra:latest
```

Building it yourself, per system: [Linux](GHCR/README.md#linux) ·
[Windows](GHCR/README.md#windows) · [macOS](GHCR/README.md#macos).

---

## Serving

```bash
hdrctl serve                 # Studio and API on port 5678
hdrctl serve --open          # and open the browser
hdrctl serve --no-studio     # API only, for a headless server
hdrctl serve --port 8080
```

The server writes projects into the directory you launch it from. Interactive API docs are at `/docs`.

---

## Your AI assistant, connected (MCP)

Hydra ships an MCP server. Point Claude Desktop, Cursor, VS Code or any MCP client at it, and ask for a
pipeline in plain language.

```bash
pip install "hydra-etl[mcp]"
hydra-mcp
```

Your assistant writes the manifests; **Hydra validates them before anything is written**, and nothing runs
until you ask. Requires Python 3.10+. See [docs/MCP.md](docs/MCP.md) for client configuration.

Graded by [Glama](https://glama.ai/mcp/servers/bejaouibechir/Hydra), which builds the server in a
sandbox, runs security checks and scores the tool definitions:

[![hydra-etl MCP server – quality and maintenance score on Glama](https://glama.ai/mcp/servers/bejaouibechir/Hydra/badges/card.svg)](https://glama.ai/mcp/servers/bejaouibechir/Hydra)

---

## Native acceleration (optional)

Parts of the engine have a Rust implementation. It is optional and off by default: without it, Hydra
behaves exactly the same.

```bash
pip install "hydra-etl[native]"
HYDRA_BACKEND=rust hdrctl run ./jobs/sales
```

Or per operation, with a `hydra.backends.yaml` file next to the job:

```yaml
default: python
overrides:
  csv.read: rust      # currently the only accelerated operation
```

On one million rows, CSV reading is about four times faster, with byte-for-byte identical results
(parity checked on 26,000 CSV files and one million floats against CPython `repr()`). Hydra falls back to
Python automatically whenever that guarantee cannot be kept. Benchmarks: [`bench/`](bench/).

---

## VS Code extension

Completion and validation for Hydra manifests inside the editor, without installing Hydra.
Download the `.vsix` from the [latest release](https://github.com/bejaouibechir/Hydra/releases/latest),
or build it yourself from [`vscode-extension/`](vscode-extension/) (`python build_vsix.py`, no Node required).

<img src="assets/hydravs.png" alt="Hydra VS Code extension" width="720">

---

## Project status

**Beta.** The documented connectors and steps are implemented and covered by tests. The shape of the YAML
DSL is settled; any breaking change will be announced in the release notes before 1.0.
Use it on real work, and pin your version.

---

## Contributing

Issues, ideas and pull requests are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md).
If Hydra is useful to you, a ⭐ on GitHub helps others find it.

---

# License

Hydra ETL is open source under the **GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later)**.

## You can, for free

- Use Hydra ETL for any purpose, including inside a company and for commercial work.
- Run your own pipelines with it, internally or for clients.
- Modify it and redistribute it.

Using Hydra ETL to move **your** data does not make your data, your YAML pipelines or your other applications subject to the AGPL.

## The one obligation

If you **modify Hydra ETL itself** and make it available to others — including over a network, for example as a hosted service — you must publish your modified source code under the same license.

## Commercial license

If that obligation does not fit your case — typically, you embed a modified Hydra ETL in a proprietary product or offer it as a service without publishing your changes — a commercial license is available.

**Contact:** admin@hydraetl.com — reply within 2 business days.

## Support and services

- Support with response time commitments
- Help migrating existing scripts or tools to Hydra ETL