Configure logging with ElasticSearch, Fluentd and Kibana for your Kubernetes cluster
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Complete course on Udemy: Logging in Kubernetes with EFK Stack | The Complete Guide | NOTE: This video is part of above Udemy course
I designed this course to guide you step-by-step through a somewhat complex process of
1) setting up a k8s cluster from scratch,
2) deploying your own dockerized applications inside and
3) configuring a fully functioning logging inside the cluster using EFK stack.
So we’ll go through and learn the whole process end-2-end.
The individual steps we’ll go through and all the things you will learn in this compact course.
► We will create a k8s cluster from scratch on Linode cloud platform (with LKE)
► You will learn how to host your own docker images on a private repository and deploy them into k8s cluster
► You will learn concepts of Elasticsearch, Kibana, Logstash and Fluentd
► as well as concepts of StatefulSet and DaemonSet components and Helm technology.
► After understanding all these concepts, we will deploy ElasticSearch (with data persistence ) , Kibana and Fluentd applications in k8s in an easy way using Helm charts
► And finally we will configure collecting, processing and visualising the application logs
I explain all the concepts theoretically with simple animations and visuals, so that you understand how they work. And then we go through demo parts to set up these technologies, so that you also learn how to use them in practice. For that it’s of course important that you practice along with me. I have prepared a git repository with all the needed resources to follow along easier.
#efk #elk #elasticsearch #kubernetes #kubernetestutorial
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Смотрите видео Logging in Kubernetes with Elasticsearch, Fluentd and Kibana | Complete Course Overview онлайн без регистрации, длительностью часов минут секунд в хорошем качестве. Это видео добавил пользователь TechWorld with Nana 13 Сентябрь 2020, не забудьте поделиться им ссылкой с друзьями и знакомыми, на нашем сайте его посмотрели 66,716 раз и оно понравилось 857 людям.