In 2022, Apache Airflow experienced a significant year of growth and innovation, marked by over 5,000 commits and contributions from more than 2,300 contributors. This progress was underpinned by a consistent release process, including 12 core releases, biweekly updates, and monthly provider releases. Notable advancements included the introduction of data-dependent scheduling with Airflow 2.4, allowing users to define datasets as part of their DAGs and schedule them based on data changes, which facilitated collaboration across data teams. Dynamic task mapping debuted with Airflow 2.3, enabling tasks to be generated dynamically at runtime, and enhanced with Airflow 2.4 and 2.5 for broader support. The Airflow UI saw improvements with the Grid view and Datasets view, enhancing monitoring and organization of DAGs. Astronomer contributed to the ecosystem with the Astro Python SDK, simplifying DAG authoring by abstracting Airflow-specific syntax, and the release of Astronomer Providers, which introduced deferrable operators for increased scalability. The Airflow community's efforts in 2022 laid the foundation for further innovations in 2023, focusing on data-dependent functionality and simplifying the DAG-authoring experience.