Data engineers often face challenges in managing numerous Directed Acyclic Graphs (DAGs) across organizations due to issues like code duplication and maintaining consistency, especially when not all team members are proficient in Python. DAG Factory, an open-source tool for declarative DAG authoring in Apache Airflow, has reached a notable milestone with its version 1.0 release, which modernizes DAG creation to align with Airflow 3 standards while remaining user-friendly. This version introduces a revamped YAML specification for more intuitive configuration, a layered default inheritance system to standardize configurations, and a simplified entry point for DAG generation. It also offers full compatibility with Airflow 3 features, enhances the developer experience with improved CLI tools, and provides a straightforward migration path for existing users. By simplifying the creation and maintenance of data pipelines, DAG Factory 1.0 aims to reduce development time, improve consistency, and enable team autonomy, making it easier for new team members to create DAGs without needing to learn Python. Organizations using DAG Factory report significant reductions in development time and maintenance efforts, benefiting from the declarative approach's self-documenting nature, which enhances collaboration across teams.