Hydra ETL
Declarative ETL pipelines in YAML, validated before they run.
Declarative ETL pipelines in YAML, validated before they run.
This page is designed to help you find out whether Hydra ETL is good and if it is the right choice for you.
Hydra ETL turns data pipelines into readable YAML. A job is a handful of small files — sources, destinations, transformations — that can be reviewed in a pull request, and hdrctl validate checks them against each other before any data is touched.
Listed in
Declarative pipelines
Jobs written as plain YAML files, reviewable in a pull request
Validation before run
hdrctl validate cross-checks sources, destinations and transformations without touching data
Workflows
DAG of jobs and actions with dependencies, retries and conditions
Connectors
CSV, JSON, Parquet, PostgreSQL, MySQL/MariaDB, SQL Server, MongoDB, REST APIs
Transformations
select, rename, cast, filter, calculate, sort, aggregate, join, deduplicate, pivot, Python script
Three interfaces
hdrctl CLI, REST API and Hydra Studio visual editor on the same files
Engines
pandas and DuckDB, optional Rust CSV reader and more ...
Environments
Per-environment parameters and secrets read from environment variables
Editor support
VS Code extension with autocompletion and validation and Hydra Studio a graphical environment IDE like
License
Open source, AGPL-3.0, free
Data engineers, analysts and consultants who move data between files and databases without wanting to run infrastructure, and teams whose pipelines must stay readable by people who don’t write Python
Pipelines are plain YAML files, and hdrctl validate checks them against each other before any data is read or written. Typos and broken references are caught before the run, not halfway through it.
No scheduler to deploy, no cluster, no Python code to maintain: pip install hydra-etl and a few YAML files. The same pipeline runs from the CLI, a REST API or a visual editor. For large orchestration platforms, Airflow or Dagster remain the better fit.
Hydra ETL is built by one developer, Bechir Bejaoui, in Tunis, in his spare time. It started from a simple need: a data pipeline should be something you can read, review and check before you run it, rather than a script you discover broken in production.
Python, pandas, DuckDB, Pydantic, Click, FastAPI, an optional Rust CSV reader, and React with TypeScript for the visual Studio.
Could be data consultants and people who is the primary field is handling data but they don't have enough experience in programming and IT
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The latest comments about Hydra ETL on Reddit. This can help you find out how popualr the product is and what people think about it.
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Is Hydra ETL good? This is an informative page that will help you find out. Moreover, you can review and discuss Hydra ETL here. The primary details have been verified within the last quarter. So they could be considered up to date. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
