The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Principalele funcționalități ale ploomber/ploomber sunt: ML Ops, Workflow Orchestration, Data Pipelines, Data Orchestration, Interactive Notebooks, Publishing and Conversion.
Alternativele open-source pentru ploomber/ploomber includ: prefecthq/prefect — Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as… dagster-io/dagster — Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative… orchest/orchest — Orchest is a data pipeline orchestrator and containerized workflow manager. It provides a platform for designing,… couler-proj/couler — Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows,… apache/airflow — Airflow is a platform for programmatically authoring, scheduling, and monitoring complex data pipelines. It functions… argoproj/argo-workflows — Argo Workflows is a container-native workflow engine that functions as a Kubernetes custom resource controller. It…
Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep
Orchest is a data pipeline orchestrator and containerized workflow manager. It provides a platform for designing, scheduling, and executing complex data processing sequences through a combination of a graphical interface and scripting. The platform distinguishes itself by using containers to manage software dependencies, ensuring consistent execution across different environments. It features a polyglot task scheduler capable of triggering jobs written in multiple programming languages and includes a version control system that tracks historical snapshots of project configurations and code.
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.
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