2 dépôts
Capabilities for passing small data packets and metadata between tasks in a single workflow execution.
Distinct from Cross-Package Metadata Sharing: Shortlist candidates cover ML metadata or package build metadata, not runtime data exchange between orchestration tasks.
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Airflow is a workflow orchestration platform for authoring, scheduling, and monitoring complex data pipelines as code using Python. It employs a DAG-based task scheduler to manage execution timing and dependencies via directed acyclic graphs, utilizing a distributed task execution engine to run workloads across a cluster of worker nodes. The platform provides a data pipeline monitor for tracking the health and execution history of programmatic workflows. This includes a web interface for workflow progress visualization and health monitoring to identify and troubleshoot pipeline failures. The
Facilitates the sharing of small data packets and metadata between tasks within a single workflow execution.
This project is a Python workflow orchestration platform and programmatic data pipeline engine used to author, schedule, and monitor complex data pipelines. It functions as a directed acyclic graph manager and scheduler, allowing users to define data movement and transformation tasks as code to ensure precise execution order and maintainability. The platform distinguishes itself by treating workflows as code, enabling pipelines to be versioned and tested through a standard programming language. It utilizes a system of extensible operators to encapsulate integration logic and employs a templat
Enables the exchange of small data packets and metadata between individual tasks within a workflow.