Apache Airflow Kubernetes Executor in Practice (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Apache Airflow Kubernetes Executor in Practice" "Apache Airflow Kubernetes Executor in Practice" offers a comprehensive and modern guide to orchestrating data workflows using Apache Airflow in containerized, cloud-native environments. Beginning with foundational concepts, the book explores Airflow's architecture, Directed Acyclic Graph (DAG) orchestration, and the spectrum of executor options. It provides essential Kubernetes knowledge for data engineers, including core abstractions, containerization best practices, and a strategic rationale for choosing the Kubernetes Executor to achieve sca...
"Apache Airflow Kubernetes Executor in Practice"
"Apache Airflow Kubernetes Executor in Practice" offers a comprehensive and modern guide to orchestrating data workflows using Apache Airflow in containerized, cloud-native environments. Beginning with foundational concepts, the book explores Airflow's architecture, Directed Acyclic Graph (DAG) orchestration, and the spectrum of executor options. It provides essential Kubernetes knowledge for data engineers, including core abstractions, containerization best practices, and a strategic rationale for choosing the Kubernetes Executor to achieve scalable, elastic data pipelines.
Going beyond introductory material, the book delivers a meticulous examination of the Kubernetes Executor's internal design, covering pod generation, resource isolation, environment injection, persistent storage patterns, and robust failure recovery mechanisms. Readers gain actionable insights into deploying Airflow on Kubernetes, leveraging Helm and Kustomize for reproducible infrastructure, and designing clusters for high availability and cost efficiency. Advanced chapters delve into modular DAG development, CI/CD automation for pipeline delivery, custom operator design, and advanced resource optimization techniques that balance cost and throughput at scale.
Security, observability, and operations are given first-class treatment, with chapters on Kubernetes RBAC, secrets management, network policies for workflow isolation, compliance logging, advanced monitoring with Prometheus and Grafana, and incident response. To round out the practitioner's playbook, detailed troubleshooting guidance, production lessons learned, and forward-looking best practices are anchored by real-world case studies. Whether building foundational knowledge or engineering robust, enterprise-scale workflows, this book is an essential resource for anyone seeking mastery of Airflow in Kubernetes environments.
"Apache Airflow Kubernetes Executor in Practice" offers a comprehensive and modern guide to orchestrating data workflows using Apache Airflow in containerized, cloud-native environments. Beginning with foundational concepts, the book explores Airflow's architecture, Directed Acyclic Graph (DAG) orchestration, and the spectrum of executor options. It provides essential Kubernetes knowledge for data engineers, including core abstractions, containerization best practices, and a strategic rationale for choosing the Kubernetes Executor to achieve scalable, elastic data pipelines.
Going beyond introductory material, the book delivers a meticulous examination of the Kubernetes Executor's internal design, covering pod generation, resource isolation, environment injection, persistent storage patterns, and robust failure recovery mechanisms. Readers gain actionable insights into deploying Airflow on Kubernetes, leveraging Helm and Kustomize for reproducible infrastructure, and designing clusters for high availability and cost efficiency. Advanced chapters delve into modular DAG development, CI/CD automation for pipeline delivery, custom operator design, and advanced resource optimization techniques that balance cost and throughput at scale.
Security, observability, and operations are given first-class treatment, with chapters on Kubernetes RBAC, secrets management, network policies for workflow isolation, compliance logging, advanced monitoring with Prometheus and Grafana, and incident response. To round out the practitioner's playbook, detailed troubleshooting guidance, production lessons learned, and forward-looking best practices are anchored by real-world case studies. Whether building foundational knowledge or engineering robust, enterprise-scale workflows, this book is an essential resource for anyone seeking mastery of Airflow in Kubernetes environments.
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