Airflow on Kubernetes: Dynamic Workflows Simplified - Daniel Imberman, Bloomberg & Barni Seetharaman
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Airflow on Kubernetes: Dynamic Workflows Simplified - Daniel Imberman, Bloomberg & Barni Seetharaman, Google
Apache Airflow is an open source workflow orchestration engine that allows users to write Directed Acyclic Graph (DAG)-based workflows using a simple Python library. Airflow offers a wide range of native operators for services ranging from Spark and HBase to Google Cloud Platform (GCP) and Amazon Web Services (AWS). Until recently, the Airflow user experience has been hindered by the need to launch and maintain statically-sized Celery-based Airflow clusters. These clusters were both expensive (over and under-utilization) and complex (multiple points of failure). To address these issues, we developed and published a native Kubernetes Operator and Kubernetes Executor for Apache Airflow. These products allow one-step Airflow deployments, dynamic allocation of Airflow worker pods, full power over run-time environments, and per-task resource management.
To learn more: https://sched.co/GrUO
Видео Airflow on Kubernetes: Dynamic Workflows Simplified - Daniel Imberman, Bloomberg & Barni Seetharaman канала CNCF [Cloud Native Computing Foundation]
Don't miss KubeCon + CloudNativeCon 2020 events in Amsterdam March 30 - April 2, Shanghai July 28-30 and Boston November 17-20! Learn more at kubecon.io. The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects
Airflow on Kubernetes: Dynamic Workflows Simplified - Daniel Imberman, Bloomberg & Barni Seetharaman, Google
Apache Airflow is an open source workflow orchestration engine that allows users to write Directed Acyclic Graph (DAG)-based workflows using a simple Python library. Airflow offers a wide range of native operators for services ranging from Spark and HBase to Google Cloud Platform (GCP) and Amazon Web Services (AWS). Until recently, the Airflow user experience has been hindered by the need to launch and maintain statically-sized Celery-based Airflow clusters. These clusters were both expensive (over and under-utilization) and complex (multiple points of failure). To address these issues, we developed and published a native Kubernetes Operator and Kubernetes Executor for Apache Airflow. These products allow one-step Airflow deployments, dynamic allocation of Airflow worker pods, full power over run-time environments, and per-task resource management.
To learn more: https://sched.co/GrUO
Видео Airflow on Kubernetes: Dynamic Workflows Simplified - Daniel Imberman, Bloomberg & Barni Seetharaman канала CNCF [Cloud Native Computing Foundation]
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16 декабря 2018 г. 8:03:40
00:23:22
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