As a developer tools analyst, I've compared Apache DolphinScheduler (Project A) and Dagster (Project B) based on momentum, community size, and apparent use cases for senior engineers. In terms of momentum, Dagster (Project B) is currently outpacing Apache DolphinScheduler (Project A), with 133 new stars in the last 30 days compared to Project A's 46. This indicates a more rapid recent adoption rate for Dagster. Overall, Dagster has a slightly larger community, with 15,152 stars versus Project A's 14,206. Both projects cater to data workflow orchestration, but their use cases diverge slightly. Apache DolphinScheduler is positioned as a low-code, high-performance workflow creator, suggesting suitability for teams seeking agile, user-friendly workflow management, potentially appealing to data engineers and analysts alike. Dagster, emphasizing the development, production, and observation of data assets, appears to target more comprehensive data pipeline management, likely appealing to senior engineers focusing on end-to-end data asset lifecycle management in complex, possibly enterprise, environments. The choice between the two may hinge on the specific needs of low-code agility versus comprehensive data asset management capabilities. Dagster's current growth rate may also influence decisions for teams prioritizing community vibrancy and potentially more active support and feature development.