head to head · open source
airflow vs Elementary Data
airflow has 47,350 GitHub stars, 18,299 forks, 1,831 open issues and last shipped yesterday. Elementary Data has 2,429 stars, 239 forks, 12 open issues and last shipped 12 days ago. airflow leads on adoption by 1,849% (47,350 vs 2,429 stars). airflow is written in Python under Apache-2.0; Elementary Data is written in HTML under Apache-2.0. airflow has attracted 39% as many forks as stars, Elementary Data 10%. airflow was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (data-pipelines), 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.
← all 20902 open source comparisons
Side by side
| airflow | Elementary Data | |
|---|---|---|
| GitHub stars | ★ 47K | ★ 2.4K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | HTML |
| Last push | 2026-11-02 | 2026-10-22 |
| Forks | ⑂ 18K | ⑂ 239 |
| 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 Elementary Data if
- You want the Elementary Data feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches HTML
- You evaluated both and Elementary Data 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 Elementary Data
Elementary OSS: dbt native data observability Docs » Join the Elementary Community » ⭐️ Star the repo Built by the Elementary team, helping you deliver trusted data in the AI era. Elementary OSS is the open source CLI for dbt native data observability. It works with the Elementary dbt package to generate the basic Elementary observability report and send alerts to Slack and Microsoft Teams. For teams that need data reliability at scale, we offer Elementary Cloud , a full Data & AI Control Plane with automated ML monitoring, column level lineage from source to BI, a built in catalog, and AI agents that scale…
read the full Elementary Data overview →
More in Data & Analytics
Related comparisons
More Data Engineering & Integration projects
Compare either of these against the rest of the Data Engineering & Integration field.
Frequently asked questions
Is airflow or Elementary Data more popular?
airflow has 47,350 GitHub stars and Elementary Data has 2,429. airflow has the larger community by that measure.
Are airflow and Elementary Data free?
Both are open source. airflow is licensed under Apache-2.0 and Elementary Data under Apache-2.0. Neither carries a licence fee.
What is the difference between airflow and Elementary Data?
airflow is written in Python and Elementary Data in HTML. 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 Elementary Data?
Choose airflow if you want the larger community (47,350 stars) or its Apache-2.0 licence terms. Choose Elementary Data if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.