It supports a fully integrated data scheduling ecosystem with dozens of job components. It also offers flexible version control, allowing users to make adjustments specific to each individual run without affecting the design flow of the original template.

It provides full-mode DAG development capabilities, allowing users to orchestrate workflow tasks using an IDE, low-code interface, Python code, or importing from Excel. This enables rapid integration of development and usage processes for both internal users and developers.

It supports automated CI/CD processes and allows for local file uploads and cross-environment packaging and deployment. This helps enterprises quickly adapt to DataOps processes and improves overall efficiency in development, testing, and deployment workflows.

It supports public cloud, private cloud, and hybrid cloud environments, embracing cloud-native architectures. It is compatible with traditional data warehouses, open-source data lakes, and provides simultaneous management of batch and streaming tasks. This enables enterprise users to achieve integrated scheduling for data lakes, data warehouses, and cloud environments.

With robust fault tolerance mechanisms and workflow execution management, it ensures data quality and greatly reduces the burden on operations teams. It allows operations personnel to handle various complex scenarios quickly and effectively.

It supports the customization of various business calendars and cut-off time management, meeting the collaborative needs of multiple departments within an enterprise.