Apache Airflow vs Redpanda Connect
| Tagline | Programmatically author, schedule, and monitor workflows as Python DAGs | Declarative stream processor and data pipeline tool with 200+ connectors |
| Category | Automation & iPaaS | Automation & iPaaS |
| Replaces | Workato | Zapier, Tray.io |
| GitHub stars | 46k | 8.2k |
| Language | Python | Go |
| License | Apache-2.0 | Apache-2.0 |
| Self-host difficulty | 4/5 Involved | 2/5 Easy |
| Deploy options | Docker Compose Kubernetes Manual | Docker Kubernetes Manual |
| Managed hosting | ||
| Last updated | 5 days ago | 1 month ago |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Apache Airflow
- Fully code-first (Python DAGs); there is no no-code builder for non-developers.
- Heavyweight to operate: scheduler, webserver, metadata DB, and executor/workers must be configured and maintained.
- Not built around consumer SaaS app triggers; it targets data orchestration rather than iPaaS connectors.
- Real-time/event triggering is weaker than purpose-built automation tools, which favor scheduling.
Redpanda Connect
- No graphical UI; all pipeline configuration is done in YAML, requiring developer involvement
- No support for human-in-the-loop or approval workflow steps
- Monitoring requires pairing with external tools like Prometheus and Grafana
Bottom line
Choose Redpanda Connect if you want the lower-effort setup; choose Apache Airflow for the larger community and ecosystem. Apache Airflow has seen more recent development. Open each guide below for deploy steps and the full feature gap.
Apache Airflow
Programmatically author, schedule, and monitor workflows as Python DAGs
Redpanda Connect
Declarative stream processor and data pipeline tool with 200+ connectors