Dagster is a new paradigm in data orchestration that takes a radically different approach to data orchestration than other tools. It was designed from the ground up with data assets and the full development lifecycle in mind for a more complete and integrated approach to data pipelines. Dagster addresses Airflow's limitations, such as asset orientation, principled architecture, full data engineering lifecycle management, local development and testing, debugging, data lineage and asset management, scalability and isolation, containerization, and CI/CD. It provides better visibility into data lineage and dependencies, rich structured logs for debugging, a local development environment for iteration and error detection, scalable execution environments, isolated environments for tasks, native support for containerized environments, built-in CI/CD practices, automated testing and deployment, and advanced features like data asset management, operational workflows, security and compliance, priority support, context-rich view of assets, platform integration with external assets, searchability and discoverability. Dagster+ adds more features to compete with the best in DataOps, including a built-in data catalog, operational workflows, security and compliance, priority support, context-rich view of assets, platform integration with external assets, and searchability and discoverability. Organizations can use Dagster alongside Airflow or migrate from Airflow to Dagster for better data operations.