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Workflow Manager
Workflow automation and scheduling system are used to author, schedule, and manage data pipelines. A Workflow manager includes scheduling and triggering jobs, resolving dependencies between steps and workflows, versioning, and monitoring and alerting on failures. As modern-day data ecosystems have grown more complex, a workflow manager automates the ETL process and the scheduling of dependent API calls, which can be monitored and triggered from a centralized control panel.
- A workflow manager provides a common orchestration framework that data scientists, machine learning (ML) and data engineers, and others can use to automate the acquisition and processing of data at every phase of a data science or ML project’s lifecycle.
- A workflow manager allows you to scale your data operations effectively. It provides the ability to automate repetitive tasks, handle larger volumes of data, and accommodate evolving requirements without sacrificing efficiency.
- A workflow manager provides visibility into the entire data pipeline, enabling you to monitor and track the progress of data processes in real-time.
Last modified 28d ago