Hydra ETL

Your data pipelines are described, not programmed.

Website · Docs & playground · MCP server · VS Code extension · Changelog

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.

Quick start

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

Or stay in the terminal:

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.

Related MCP server: Datris MCP Server

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).

• AI-ready. An MCP server lets Claude, Cursor or VS Code write pipelines that Hydra validates.

How it compares

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

Connectors (sources and destinations):

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

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

transformations.yaml: how it is reshaped

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

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

pipeline.yaml: which source feeds which destination

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

hdrctl validate my_job && hdrctl run my_job

Workflows: order several jobs

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

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.

Parameters

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

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

{{ param:NAME }} reads a parameter, {{ env:NAME }} an environment variable, ${SECRET:NAME} a secret. 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.

pip install hydra-etl                  # engine + CLI
pip install "hydra-etl[server]"        # + API + Studio
pip install "hydra-etl[postgres]"      # + PostgreSQL driver
pip install "hydra-etl[all]"           # everything

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

Serving

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.

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 for client configuration.

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.

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:

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/.

VS Code extension

Completion and validation for Hydra manifests inside the editor, without installing Hydra. Download the .vsix from the latest release, or build it yourself from vscode-extension/ (python build_vsix.py, no Node required).

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. If Hydra is useful to you, a ⭐ on GitHub helps others find it.

License

Hydra ETL is released under the GNU Affero General Public License v3 or later. See LICENSE.

You may use, modify and redistribute it freely, including commercially. If you modify Hydra and let others use it, even only over a network, you must make your modified source available under the same terms.

Commercial license: for use without that obligation, contact the author via hydraetl.com or by opening an issue.