#python #analytics #dagster #data_orchestrator #data_pipelines #data_science #etl #scheduler #workflow #workflow_automation

https://github.com/dagster-io/dagster

#java #data #data_engineering #data_orchestration #data_orchestrator #data_pipeline #dataflow #elt #etl #kestra #orchestration #pipeline #scheduler #workflow #workflow_automation #workflow_engine

https://github.com/kestra-io/kestra

#python #analytics #dagster #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #etl #metadata #mlops #orchestration #python #scheduler #workflow #workflow_automation

Dagster is a tool that helps you manage and automate your data workflows. You can define your data assets, like tables or machine learning models, using Python functions. Dagster then runs these functions at the right time and keeps your data up-to-date. It offers features like integrated lineage and observability, making it easier to track and manage your data. This tool is useful for every stage of data development, from local testing to production, and it integrates well with other popular data tools. Using Dagster, you can build reusable components, spot data quality issues early, and scale your data pipelines efficiently. This makes your work more productive and helps maintain control over complex data systems.

https://github.com/dagster-io/dagster

#python #airflow #apache #apache_airflow #automation #dag #data_engineering #data_integration #data_orchestrator #data_pipelines #data_science #elt #etl #machine_learning #mlops #orchestration #python #scheduler #workflow #workflow_engine #workflow_orchestration

Apache Airflow is a tool that helps you manage and automate workflows. You can write your workflows as code, making them easier to maintain, version, test, and collaborate on. Airflow lets you schedule tasks and monitor their progress through a user-friendly interface. It supports dynamic pipeline generation, is highly extensible, and scalable, allowing you to define your own operators and executors.

Using Airflow benefits you by making your workflows more organized, efficient, and reliable. It simplifies the process of managing complex tasks and provides clear visualizations of your workflow's performance, helping you identify and troubleshoot issues quickly. This makes it easier to manage data processing and other automated tasks effectively.

https://github.com/apache/airflow