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𝟭𝟮 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 ▷ Docker - Manages containers through Docker Daemon. - Uses a registry to store container images. - Clients can interact with the service or host to deploy containers. ▷ Container Orchestration - Manages multiple containers and nodes. - Example: Kubernetes is the orchestrator for deploying and managing containerized applications. ▷ Caching - Improves application performance by using distributed caches. - Reduces database load by temporarily storing frequently accessed data. ▷ Single DB - Multiple services (Service A, Service B, Service C) connect to a single, shared database. - Simplifies data consistency but can create a bottleneck. ▷ Distributed Tracing - Tracks requests across multiple services in a microservices architecture. - Useful for troubleshooting and monitoring service interactions. ▷ Monitoring and Tracing - Provides visibility across frontend and backend components. - Ensures performance monitoring and issue detection across services. ▷ Logging - Centralizes logs from different microservices (Microservice 1, Microservice 2). - Allows for easier tracking and troubleshooting of issues. ▷ Event Bus - Facilitates communication between microservices through an event-driven architecture. - Ensures asynchronous processing of events across services. ▷ Service Discovery - Helps services find each other automatically. - Uses a service registry and load balancer to connect service providers and consumers. ▷ Load Balancing - Distributes incoming requests evenly across multiple servers. - Improves application scalability and fault tolerance. ▷ API Gateway - Acts as a single entry point for clients to access multiple services. - Handles routing, authentication, and rate limiting. ▷Cloud Provider - Hosts infrastructure in the cloud for scalability. - Allows for flexible resource provisioning and management. #LinuxShellScripting #ShellScripting #Linux #DevOps #SystemAdmin #Coding #Programming #PacktPublishing #AndrewMallett #TechBooks #ScriptAutomation

💡Cloud-Native Application Deployment and Optimization Sharing my recent experience on deploying a two-tier application built with Flask and MySQL using best DevOps practices! What I Did: • Dockerized the Application: I containerized the app to streamline development and ensure consistency across environments, pushing images to DockerHub for version control. • Kubernetes Setup: I started with Kubeadm to establish a robust Kubernetes cluster and later transitioned to AWS EKS using eksctl for enhanced fault tolerance. • HELM for Deployment: I packaged the Kubernetes manifest files with HELM, simplifying the deployment process on AWS EKS. • High Availability: I ensured a multi-node cluster setup, allowing for high availability and scalability. 👉By leveraging AWS EKS, I improved the application’s scalability and reduced downtime by 70%! I’m always eager to learn and grow, so if you have any tips or experiences to share about Kubernetes or DevOps, I’d love to connect! #DevOps #Kubernetes #Docker #HELM #EKS #AWS #Flask #MySQL #AWSEKS #CloudComputing #ContinuousDeployment #Scalability

𝗗𝗮𝘆 𝟮𝟳: 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: 𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐫𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 🚀 To successfully build modern microservice architectures, here’s a roadmap of key technologies and tools: 𝟭. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗦𝗤𝗟: MySQL, PostgreSQL for structured data. 𝗡𝗼𝗦𝗤𝗟: MongoDB, Cassandra, DynamoDB, HBase for unstructured data. 𝟮. 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗕𝗿𝗼𝗸𝗲𝗿𝘀 Kafka, RabbitMQ, Amazon SQS for efficient communication between services. 𝟯. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗧𝗼𝗼𝗹𝘀 Grafana, Kibana, Prometheus for tracking performance and health of services. 𝟰. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 Java, .NET, Go, NodeJS, Python to cater to diverse development needs. 𝟱. 𝗖𝗜/𝗖𝗗 𝗧𝗼𝗼𝗹𝘀 GitHub Actions, Jenkins, TeamCity, GitLab, CircleCI for automating the software development pipeline. 𝟲. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 JWT, OAuth 2.0, API Authorization, TLS for secure communication and access control. 𝟳. 𝗖𝗹𝗼𝘂𝗱 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀 AWS, Azure, GCP, Linode, DigitalOcean for hosting and scaling services. 𝟴. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ECS, OpenShift, HashiCorp, Kubernetes for managing and deploying containerized applications. 𝟵. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 Docker, Podman for creating consistent and portable application environments. This roadmap outlines the critical components required to develop scalable, resilient, and efficient microservice architectures. 📚 Keep learning, keep sharing! 🔄 Follow Nadeem Ahmad for daily insights on Java frameworks, cloud services, and building high-performance systems! 💻☁️ #Microservices #Java #AWS #Kubernetes #Docker #CloudComputing #DevOps #SoftwareArchitecture #Scalability #Resilience #CICD #APISecurity #Programming

Automating as much as possible will make the life of most Devops people happy. Using tools on AWS like Eventbridge can drive almost any kind of automation based on events or schedules. The article below describes a data ingestion architecture involving AWS Batch and S3. The Eventbridge scheduler can be used to create one-time or recurring schedules to initial almost any kind of action in AWS. In the setup described below by Geoff, the Eventbridge scheduler triggers jobs in AWS Batch on a schedule to retrieve files from an FTP server and store them in S3. This involves AWS Batch using a container-based implementation for retrieving the data from the FTP server. In many cases using something like AWS Lambda for this would be ideal but depending on how long these interactions with external servers take you could hit the 15 minute Lambda execution limit. This example shows that there isn't always a single way to do everything and you need to take advantage of all the tools available and use the best ones for each problem. https://lnkd.in/e5PeE9zd

☸ 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐢𝐧 𝐃𝐨𝐜𝐤𝐞𝐫 𝐓𝐞𝐫𝐦𝐬 -> K8s architecture, focusing on its two planes: the control plane and the data plane. -> The control plane includes the API server, etcd, and scheduler, while the data plane comprises kubelet, proxy, and container runtime. 𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗱𝗼𝘄𝗻 𝘁𝗵𝗲𝘀𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝘂𝘀𝗶𝗻𝗴 𝗗𝗼𝗰𝗸𝗲𝗿'𝘀 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲. -> Just like how a Java application needs Java runtime, Docker runtime relies on a 𝗗𝗼𝗰𝗸𝗲𝗿 𝘀𝗵𝗶𝗺. Both are essential under the hood. -> When a request reaches the master (control plane), kubelet verifies if the pod is running. -> Unlike Docker, Kubernetes supports various runtimes like 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗱 , eliminating the need for a Docker shim. -> In Docker, bridging is the default networking method, similar to Kubernetes 𝗸𝘂𝗯𝗲-𝗽𝗿𝗼𝘅𝘆 using IP tables for networking. -> Kube-proxy generates IP addresses, while 𝗸𝘂𝗯𝗲𝗹𝗲𝘁 handles pod creation, and a container runtime is necessary for running containers. -> The API server acts as 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗵𝗲𝗮𝗿𝘁, exposing its functionalities, while the scheduler receives information from it. -> Etcd stores 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 𝘀𝘁𝗮𝘁𝗲, and the controller manager automates tasks like auto-scaling. -> For cloud control management, Kubernetes offers Cloud Controller Manager (CCM), ideal for cloud deployments. -> We use 𝗠𝗶𝗻𝗶𝗞𝘂𝗯𝗲 for local development, and Kubernetes Enterprise for larger setups. Options like Kubernetes, OpenShift, Rancher, Tanzu, EKS, AKS, etc are available for production environments. -> MiniKube is designed for single-node setups, while EKS provides additional Amazon support, including extra CLI control. #kubernetes #dockerarchitecture #devops #cloudnative #containerization #k8s #docker #learninginpublic #cloud

Automating as much as possible will make the life of most Devops people happy. Using tools on AWS like Eventbridge can drive almost any kind of automation based on events or schedules. The article below describes a data ingestion architecture involving AWS Batch and S3. The Eventbridge scheduler can be used to create one-time or recurring schedules to initial almost any kind of action in AWS. In the setup described below by Geoff, the Eventbridge scheduler triggers jobs in AWS Batch on a schedule to retrieve files from an FTP server and store them in S3. This involves AWS Batch using a container-based implementation for retrieving the data from the FTP server. In many cases using something like AWS Lambda for this would be ideal but depending on how long these interactions with external servers take you could hit the 15 minute Lambda execution limit. This example shows that there isn't always a single way to do everything and you need to take advantage of all the tools available and use the best ones for each problem. https://lnkd.in/e5PeE9zd

StatefulSet in Kubernetes: A StatefulSet in Kubernetes is a controller that manages the deployment and scaling of a set of Pods, and provides guarantees about the ordering and uniqueness of these Pods. StatefulSets are used for stateful applications, and they differ from Deployments in the following ways: Key Features of StatefulSets 🔔 Stable, Unique Network Identifiers: Each Pod in a StatefulSet has a stable, unique identifier that is maintained across rescheduling. 🔔 Ordered, Graceful Deployment and Scaling: Pods in a StatefulSet are deployed and scaled in a predictable, ordered fashion. 🔔 Stable, Persistent Storage: StatefulSets can provide stable storage by associating each Pod with a PersistentVolumeClaim (PVC). Components of a StatefulSet 🔔 Service: A Headless Service to control the network domain. 🔔 StatefulSet: The StatefulSet object itself, which describes the desired state and manages the Pods. 🔔 PersistentVolumeClaims (PVCs): Claims to persistent storage that Pods use. Example Use Cases Databases (e.g., MySQL, PostgreSQL) Applications requiring unique, persistent storage per instance Example StatefulSet Manifest Below is an example of a StatefulSet manifest for a simple web application: #devops #cicd #kubernetes

**Do you know how to master Kubernetes (k8s)? Understanding the cluster components is crucial.** Give a thumbs up 👍 and comment if you find this helpful! Here is a brief overview of Kubernetes cluster architecture and its components: 1. **Master Node**: Controls the cluster and manages the scheduling and lifecycle of containers.   - **API Server**: Serves as the frontend for the Kubernetes control plane.   - **Controller Manager**: Runs controller processes to regulate the state of the cluster.   - **Scheduler**: Assigns workloads to nodes based on resource availability.   - **etcd**: Stores all cluster data. 2. **Worker Nodes**: Run the applications and workloads.   - **Kubelet**: Ensures containers are running as expected.   - **Kube-proxy**: Manages network rules on nodes.   - **Container Runtime**: Runs containers (e.g., Docker, containerd). 3. **Add-ons**: Extend the functionality of Kubernetes.   - **DNS**: Provides service discovery.   - **Dashboard**: Web-based UI for Kubernetes.   - **Monitoring and Logging**: Tools for tracking cluster health and performance. #DevOps #Kubernetes #k8s #CloudComputing #Containerization #Microservices #ITInfrastructure #Tech #TechCommunity #SysAdmin #CloudNative #OpenSource --- Feel free to adjust any part of the content or tags to better suit your audience!

Running a Single-Node Kafka Cluster via Docker! As discussed in my previous posts about Kafka, you can set up and run Kafka in two ways: either by using Kafka binaries or Docker. Docker is my personal favorite route because it's programmatic, clean, and doesn't require any additional resources apart from a running Docker runtime. In the image below, I describe how you can quickly set up a single-node Kafka cluster along with Zookeeper on your local machine using Docker Compose. You just need to save this content to a YAML file, name it `docker-compose.yml`, and then run the following command: > docker-compose up -d Just wait a couple of seconds, and you will see in the terminal that a Kafka node is now up and running! To connect to this Kafka node and work with it from outside the Docker environment, use the URL `localhost:29092`. For connecting from within the Docker setup, use `kafka:29092`, as Docker can manage the networking for you within its context. -------------------- Hello there 👋, I'm Sriram Kumar Mannava I'm a full-stack developer, and I can help you jumpstart into software engineering by sharing various useful concepts in a simple way, based on my experience 😁🔥 If you're interested, please do follow me and stay notified! #ApacheKafka #Docker #DockerCompose #DataStreaming #BigData #DevOps #SoftwareDevelopment #Containerization #KafkaTutorial #TechStack

🚀 Excited to share a brief overview of Kubernetes (K8s) architecture! I’ve worked extensively with K8s and wanted to illustrate its core components. 🔹 Master Node: - API Server: The front-end of the control plane, communicating with all other components. - Scheduler: Assigns workloads to nodes. - Controller Manager: Ensures the cluster's desired state. - ETCD: Key-value store for cluster data. 🔹 Worker Node: - Kubelet: Ensures containers are running in a Pod. - Kube-Proxy: Manages network rules and communication. Kubernetes is pivotal for container orchestration, ensuring scalability, high availability, and efficient resource utilization. . . . . #kubernetes #devops #docker #aws #cloud #linux #python #cloudcomputing #azure #developer #technology #jenkins #coding #software #java #programming #k #javascript #git #bigdata #devopsengineer #s #ansible #machinelearning #microservices #it #datascience #gcp #cybersecurity #googlecloud