Dynamically generate Apache Airflow DAGs from YAML configuration files
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Updated
May 31, 2024 - Python
Dynamically generate Apache Airflow DAGs from YAML configuration files
Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, persist, and execute on your own infrastructure.
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.
A CLI tool to streamline getting started with Apache Airflow™ and managing multiple Airflow projects
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Udacity Data Engineering Nanodegree Capstone Project
Bayesian inference from binary causal models
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Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data
The dag-checks consist of checks that can help you in maintaining your Apache Airflow instance.
This a repo that was created to learn more about Airflow and develop awesome data engineering projects. 🚀🚀
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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.
Opinionated framework based on Airflow 2.0 for building pipelines to ingest data into a BigQuery data warehouse
Code to draw DAG and SWIG figures for Mendelian randomization studies
Apache Airflow exercises 🚀 https://www.udemy.com/course/the-complete-hands-on-course-to-master-apache-airflow/
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