Awesome GitHub RepositoriesWorkflow Frameworks

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

Ray is a distributed computing framework designed to scale Python and Java applications across clusters by abstracting task scheduling and resource management. It functions as a resource-aware execution engine that manages task dependencies, placement, and fault tolerance across networked compute nodes. At its core, the system provides a stateful actor model, allowing developers to define classes that run in dedicated processes to maintain and mutate internal state across remote method calls. The framework distinguishes itself through a robust cross-language interoperability layer, enabling f

Kestra is a declarative workflow orchestrator designed to manage complex task dependencies and automated processes through versioned configuration files. It functions as a distributed platform that decouples task scheduling from execution by offloading computational workloads to a fleet of worker nodes. The system uses a reactive, event-driven engine to initiate workflows automatically in response to external signals, webhooks, schedules, or file system changes. The platform distinguishes itself through a modular plugin architecture that allows for the integration of custom tasks and external

Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t

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.

Dask es un framework de computación paralela y un programador de tareas distribuido diseñado para escalar flujos de trabajo de ciencia de datos en Python desde máquinas individuales hasta grandes clústeres. Funciona como un gestor de recursos de clúster que orquesta la lógica computacional representando las tareas y sus dependencias como grafos acíclicos dirigidos. Esta arquitectura permite al sistema automatizar la distribución de cargas de trabajo a través del hardware disponible mientras gestiona requisitos de ejecución complejos. El proyecto se distingue por un motor de evaluación perezosa que difiere las operaciones de datos hasta que se solicitan explícitamente, permitiendo la optimización global del grafo y una asignación eficiente de recursos. Incorpora el volcado de datos consciente de la memoria para evitar fallos del sistema al procesar conjuntos de datos que exceden la memoria disponible, y utiliza la fusión de grafos de tareas para combinar secuencias de operaciones en pasos de ejecución únicos, minimizando la sobrecarga de programación y la comunicación entre nodos. La plataforma proporciona una superficie de capacidades integral para el análisis de datos a gran escala, incluyendo soporte para aprendizaje automático distribuido, integración de computación de alto rendimiento y procesamiento de datos en paralelo. Ofrece herramientas extensas para la gestión del ciclo de vida del clúster, perfilado de rendimiento y monitoreo en tiempo real de la ejecución de tareas. Los usuarios pueden desplegar estos entornos en diversas infraestructuras, incluyendo hardware local, proveedores de nube, sistemas en contenedores y clústeres de computación de alto rendimiento.

Kedro is a data science pipeline framework and production toolbox designed to build reproducible, modular workflows using software engineering best practices. It functions as a data engineering orchestrator and catalog manager, bridging the gap between interactive analysis and maintainable production pipelines. The framework distinguishes itself by using a data catalog to decouple data access from processing logic and providing tools to transition analysis from interactive notebooks into structured workflows. It includes a workflow visualization tool that generates visual maps of data pipelin

Cocoindex is an incremental data processing engine that builds and maintains live indexes for AI agents, with a core focus on codebase indexing and knowledge graph extraction. The engine uses a function-graph execution model where user-defined Python functions are composed into a directed acyclic graph, and it processes data incrementally so only changed source records or code paths are re-computed, avoiding full recomputation at any scale. It supports automatic schema inference from transformation pipeline type annotations and provides full data lineage tracing, tagging every output record wi

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, trace, persist, and execute on your own infrastructure.

A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow

Data workflow tool, like a "Make for data"

An R-focused pipeline toolkit for reproducibility and high-performance computing

Function-oriented Make-like declarative workflows for R

Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments

Pinball is a scalable workflow manager

A language and runtime for distributed, incremental data processing in the cloud

Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.

Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.

NIPYPE: Neuroimaging in Python: Pipelines and Interfaces