head to head · open source
airflow vs aws-sdk-pandas
airflow has 47,350 GitHub stars, 18,299 forks, 1,831 open issues and last shipped yesterday. aws-sdk-pandas has 4,122 stars, 754 forks, 53 open issues and last shipped 2 days ago. airflow leads on adoption by 1,049% (47,350 vs 4,122 stars). airflow is written in Python under Apache-2.0; aws-sdk-pandas is written in Python under Apache-2.0. airflow has attracted 39% as many forks as stars, aws-sdk-pandas 18%. airflow was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (data-engineering, data-science, 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.
← all 20902 open source comparisons
Side by side
| airflow | aws-sdk-pandas | |
|---|---|---|
| GitHub stars | ★ 47K | ★ 4.1K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-11-01 |
| Forks | ⑂ 18K | ⑂ 754 |
| 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 aws-sdk-pandas if
- You want the aws-sdk-pandas feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and aws-sdk-pandas 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 aws-sdk-pandas
AWS SDK for pandas (awswrangler) Pandas on AWS Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL). An AWS Professional Service open source initiative aws proserve opensource@amazon.com ⚠️ Starting version 3.0, optional modules must be installed explicitly: ➡️ pip install 'awswrangler[redshift]' Table of contents Quick Start At Scale Read The Docs Getting Help Logging Quick Start Installation command: pip install awswrangler ⚠️ Starting version 3.0…
read the full aws-sdk-pandas 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 aws-sdk-pandas more popular?
airflow has 47,350 GitHub stars and aws-sdk-pandas has 4,122. airflow has the larger community by that measure.
Are airflow and aws-sdk-pandas free?
Both are open source. airflow is licensed under Apache-2.0 and aws-sdk-pandas under Apache-2.0. Neither carries a licence fee.
What is the difference between airflow and aws-sdk-pandas?
airflow is written in Python and aws-sdk-pandas in Python. 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 aws-sdk-pandas?
Choose airflow if you want the larger community (47,350 stars) or its Apache-2.0 licence terms. Choose aws-sdk-pandas if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.