head to head · open source
airflow vs metorikku
airflow has 47,350 GitHub stars, 18,299 forks, 1,831 open issues and last shipped yesterday. metorikku has 589 stars, 155 forks, 65 open issues and last shipped 2 months ago. airflow leads on adoption by 7,939% (47,350 vs 589 stars). airflow is written in Python under Apache-2.0; metorikku is written in Scala under MIT. airflow has attracted 39% as many forks as stars, metorikku 26%. airflow was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (etl), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| airflow | metorikku | |
|---|---|---|
| GitHub stars | ★ 47K | ★ 589 |
| License | Apache-2.0 | MIT |
| Written in | Python | Scala |
| Last push | 2026-11-02 | 2026-09-01 |
| Forks | ⑂ 18K | ⑂ 155 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick airflow if
- You weight community size — 47K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick metorikku if
- You want the metorikku feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Scala
- You evaluated both and metorikku fits your workflow better
About airflow
Apache Airflow Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on Dags a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when…
read the full airflow overview →
About metorikku
Metorikku is a library that simplifies writing and executing ETLs on top of Apache Spark. It is based on simple YAML configuration files and runs on any Spark cluster. The platform also includes a simple way to write unit and E2E tests. Getting started To run Metorikku you must first define 2 files. Metric file A metric file defines the steps and queries of the ETL as well as where and what to output. For example a simple configuration YAML (JSON is also supported) should be as follows: yaml metric: "/path/to/metric" mocks: name: table 1 path: mocks/table 1.jsonl tests: df2: id: 200 name: test id: 300 name…
read the full metorikku overview →
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Frequently asked questions
Is airflow or metorikku more popular?
airflow has 47,350 GitHub stars and metorikku has 589. airflow has the larger community by that measure.
Are airflow and metorikku free?
Both are open source. airflow is licensed under Apache-2.0 and metorikku under MIT. Neither carries a licence fee.
What is the difference between airflow and metorikku?
airflow is written in Python and metorikku in Scala. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, airflow or metorikku?
Choose airflow if you want the larger community (47,350 stars) or its Apache-2.0 licence terms. Choose metorikku if its feature set, stack or MIT licence fits better. Both are self-hostable.