Mirror of Apache Oozie
apache/oozie की मुख्य विशेषताएं हैं: Data Pipelines, General Purpose Orchestration।
apache/oozie के ओपन-सोर्स विकल्पों में शामिल हैं: dagster-io/dagster — Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative… prefecthq/prefect — Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as… argoproj/argo-workflows — Argo Workflows is a container-native workflow engine that functions as a Kubernetes custom resource controller. It… couler-proj/couler — Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows,… netflix/metaflow — Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of… snakemake/snakemake — This is the development home of the workflow management system Snakemake. For general information, see.
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative modeling and version-controlled code. It functions as a workflow engine that treats data assets as first-class primitives, allowing teams to define, schedule, and monitor complex pipelines while maintaining clear visibility into lineage, dependencies, and data quality. The platform distinguishes itself by using a code-as-configuration framework that enables standard software engineering practices, such as unit testing and local mocking, to be applied directly to data workflows.
Argo Workflows is a container-native workflow engine that functions as a Kubernetes custom resource controller. It orchestrates complex sequences of containerized tasks by executing them as directed acyclic graphs, allowing for dependency management and parallel processing within a cluster. The system extends the native Kubernetes control plane to manage the full lifecycle of automated processes, from initial triggering to final resource cleanup. The platform distinguishes itself through its controller-pattern reconciliation, which continuously monitors workflow states to align them with desi
Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It