It supports a fully integrated data scheduling ecosystem, including dozens of job components. It also supports features such as file triggering, sub-workflows, and task branching components. With control over workflow instances and task instances, users can make adjustments specific to each run without affecting the design flow of the original template.

With the self-developed WhaleTunnel Zeta data integration engine, data synchronization can be achieved at a speed far exceeding other products while conserving resources and ensuring data quality. It covers all data synchronization scenarios and provides a visual drag-and-drop interface for data scientists, data analysts, product managers, and other business users, enabling end-to-end visualization of task definition, invocation, monitoring, and management.

WhaleScheduler offers a variety of out-of-the-box machine learning scheduling components, seamlessly connecting feature engineering and scheduling stages. It enables scalable deployment and creates end-to-end MLOps workflows, allowing developers to focus on exploring business value.