Dagster and Metaflow are both prominent open-source projects catering to data professionals, though with distinct focuses. Dagster, boasting 15,152 stars and a recent 30-day growth of 133 stars, positions itself as a comprehensive orchestration platform for the entire data asset lifecycle, from development to production and observation. Its broad scope suggests suitability for a wide range of data engineering and analytics workflows, emphasizing robust data lineage, testing, and operational visibility. Metaflow, with 10,042 stars and 131 stars in the last 30 days, is specifically designed for building, managing, and deploying AI/ML systems. Its star count and recent momentum are comparable to Dagster's, indicating a healthy and active community. Metaflow's design appears geared towards empowering data scientists to move from experimentation to production more seamlessly, with features likely focused on experiment tracking, versioning, and deployment of machine learning models. While both projects attract significant attention, Dagster's broader "data asset" framing suggests a wider applicability beyond just ML, whereas Metaflow's explicit AI/ML focus targets a more specialized but rapidly growing domain.