Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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Updated
Oct 31, 2024 - Python
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Workflow Engine for Kubernetes
Apache DolphinScheduler is the modern data orchestration platform. Agile to create high performance workflow with low-code
PipelineAI
Build data pipelines, the easy way 🛠️
Docker Apache Airflow
Curated list of resources about Apache Airflow
DataSphereStudio is a one stop data application development& management portal, covering scenarios including data exchange, desensitization/cleansing, analysis/mining, quality measurement, visualization, and task scheduling.
Elyra extends JupyterLab with an AI centric approach.
A series of DAGs/Workflows to help maintain the operation of Airflow
Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
An end-to-end GoodReads Data Pipeline for Building Data Lake, Data Warehouse and Analytics Platform.
Dynamically generate Apache Airflow DAGs from YAML configuration files
Example end to end data engineering project.
More than 2000+ Data engineer interview questions.
A Data Engineering & Machine Learning Knowledge Hub
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Personal Data Engineering Projects
Optimus is an easy-to-use, reliable, and performant workflow orchestrator for data transformation, data modeling, pipelines, and data quality management.
The User-Community Airflow Helm Chart is the standard way to deploy Apache Airflow on Kubernetes with Helm. Originally created in 2017, it has since helped thousands of companies create production-ready deployments of Airflow on Kubernetes.
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