Apache Airflow vs. Databricks
A side-by-side comparison based on G2 review data.
| Metric | Apache Airflow | Databricks |
|---|---|---|
| Overall rating | 4.4 | 4.6 |
| Review count | 130 | 1,388 |
| Ease of use | 8.7 | 8.9 |
| Ease of setup | 8.2 | 8.7 |
| Quality of support | 8.5 | 8.9 |
| Meets requirements | 9.2 | 9.1 |
| NPS | 62.5 | 77.851361 |
| Small Business | 22.8% | 15.1% |
| Mid-Market | 45.7% | 37.6% |
| Enterprise | 31.5% | 47.2% |
| Total page views | 451 | 16,340 |
| Total LLM citations | 1,881 | 14,031 |
Apache Airflow
Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic pipeline generation and seamless integration with various technologies. Airflow's modular architecture and message queue system allow it to scale efficiently, managing workflows from single machines to large-scale distributed systems. Its user-friendly web interface provides comprehensive monitoring and management capabilities, offering clear insights into task statuses and execution logs.
- Ease of Use
- User Interface
- Flexibility
- Integrations
- Automation
- Easy Integrations
Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog.
- Features
- Ease of Use
- Integrations
- Collaboration
- Analytics
- Easy Integrations