Introduction to Azure Kubernetes Service - Terraform & ELK

Learn to deploy an Azure Kubernetes Service cluster with Terraform, creating resource groups, Log Analytics, a virtual network and subnet, an agent pool, and observability via dashboards and OMS agent.

Detail how to create an Azure Kubernetes cluster with Terraform, using a service principal via Azure CLI, and run plan and apply while configuring a log analytics workspace.

Access the Azure Kubernetes Service cluster created with Terraform, using the Azure CLI and kubectl, install kubectl, download credentials with az aks get-credentials, and manage nodes, pods, and services.

Explore the Kubernetes dashboard, a web-based visualization to monitor cluster objects, pods, services, and logs, with steps to deploy and access it on Azure Kubernetes Service.

Learn to create and integrate an Azure Container Registry with Azure Kubernetes Service using Terraform and the Azure CLI, enabling private Docker images for Kubernetes to pull.

Learn how Kubernetes namespaces act as virtual clusters to segregate teams and environments, isolate workloads, apply RBAC, and enable controlled cross-namespace communication.

Create and manage namespaces with kubectl and kubens, list, create, delete, and apply yaml manifests for namespaces, set active namespace to production, and deploy nginx.

Explain how pods are the smallest deployable units in Kubernetes, mapping 1-to-1 with containers, and how deployments, stateful sets, and daemon sets manage them.

Explore how deployments in Kubernetes manage pods to ensure high availability, enable rolling updates and seamless rollbacks, and scale stateless apps using replicas and Azure Container Registry images.

Learn how Kubernetes services bridge pods and users, exposing apps via cluster IP, load balancer, node port, external name, and headless services with IPs and DNS.

Create Kubernetes services for Elasticsearch in the production namespace using a cluster IP for internal access, configuring Elasticsearch and Elastic Discovery via a YAML manifest.

Demonstrate exposing an internal Elasticsearch service by changing Kubernetes service type from cluster IP to load balancer, test access on port 9200, then revert to internal cluster IP.

Create a persistent volume claim using Azure managed premium storage to provision a 10 GB disk for Elasticsearch, then attach it to pods in the production namespace.

Create a Kubernetes config map to decouple Elasticsearch configuration from the application, using a YAML file in the production namespace, then apply and verify its creation.

Create a stateful set for Elasticsearch from scratch using a config map. Run a single replica with config map mounted and a Busy Box container, plus an Azure disk storage.

Validate and deploy an Elasticsearch statefulset on Azure Kubernetes Service by applying a manifest with init containers, config map, persistent volume, and resource requests; then monitor pods and logs.

Identify and fix a crashloopback in a Kubernetes Elasticsearch stateful set by correcting vm.max_map_count to at least 262144, then reapply manifests and use port-forward to access the service.

Expose an internal elasticsearch service by switching to a load balancer for a public IP, then revert to cluster IP, preparing Cabana manifests for Elasticsearch integration.

Create and deploy a kibana manifest to Azure Kubernetes Service, wiring Kibana to Elasticsearch, exposing it with a load balancer and persistent volumes.

Learn how to scale cabana with replicas to achieve high availability in production, and perform rolling updates to upgrade pods with minimal downtime using kubectl and replica sets.

Achieve a persistent Azure load balancer IP in Kubernetes by assigning a static IP via annotations in the service manifest, enabling DNS mapping with a DNS name.

Explore how logstash collects data from multiple sources, transforms it, and ships it to Elasticsearch, with hands-on steps to create and deploy a logstash pipeline in cabana.

Explore how to scale AKS workloads with auto scale, configuring node pools for replicas, while monitoring cluster health, CPU usage, and pod status through insights, alerts, and live data.

Apply node selectors to force Elasticsearch pods onto the elastic node pool by labeling nodes and updating the pod spec with a key-value selector.

Master Kubernetes scheduling by applying taints on nodes and tolerations on pods to ensure pods run only on matching nodes, and compare with node selectors and upcoming node affinity.

Learn how taints and tolerations enable controlled pod scheduling in Kubernetes, and how node affinity and node selectors use match expressions to apply hard and soft placement rules.

Discover Lens, an open source tool to manage multiple Kubernetes clusters via a visual user interface, import kube configs, connect/disconnect clusters, view pods, logs, and helm charts.

Learn to deploy and manage Kubernetes resources with K8s Lens, apply manifests, scale deployments and replicas, inspect logs, and create daemon sets and secrets for an Elasticsearch cluster.

Master cluster management with K9s by navigating Kubernetes resources via an interactive CLI. View pods across namespaces, inspect logs, edit deployment YAML, and switch between deployments, services, and cron jobs.