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

Dynamically generate Apache Airflow DAGs from YAML configuration files

Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.

A CLI tool to streamline getting started with Apache Airflow™ and managing multiple Airflow projects

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.

Bayesian inference from binary causal models

Orchestrate your Databricks notebooks in Airflow and execute them as Databricks Workflows

Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data

This project demonstrates how to build and automate an ETL pipeline written in Python and schedule it using open source Apache Airflow orchestration tool on AWS EC2 instance.

Airflow plugin to create/edit Dags via drag-and-drop on a convenient UI

This provider contains operators, decorators and triggers to send a ray job from an airflow task

TheEye supervisor

Opinionated framework based on Airflow 2.0 for building pipelines to ingest data into a BigQuery data warehouse

🌱 Concurrently run a set of dependent, asynchronous tasks with type-safe dependencies

dagrad is a Python package that provides an extensible, modular platform for developing and experimenting with differentiable (gradient-based) structure learning methods.

Automate your data pipelines using Apache Airflow with this ready-to-use DAG for data integration, ETL and workflow automation.

Udacity Data Engineering Nanodegree Capstone Project

Experimental implementation of Airflow DAGs in Elixir

Projeto final de conclusão do Bootcamp IGTI 2021/1