13 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐦𝐨𝐧𝐥𝐲 𝐮𝐬𝐞𝐝 𝐜𝐨𝐦𝐦𝐚𝐧𝐝 𝐟𝐨𝐫 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 🎯

Here are 13 of the most commonly used kubectl commands for managing a real production Kubernetes environment, along with explanations and common use cases:

✅ 𝐂𝐨𝐫𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭

#1 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐠𝐞𝐭
→ kubectl get pods (list pods)
→ kubectl get deployments (list deployments)
→ kubectl get services (list services)
→ kubectl get all (list most resources in a namespace)

#2 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞
→ kubectl describe pod my-pod
→ kubectl describe node my-node

#3 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐜𝐫𝐞𝐚𝐭𝐞
→ kubectl create -f my-deployment.yaml

#4 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐚𝐩𝐩𝐥𝐲
→ kubectl apply -f my-deployment.yaml (apply a deployment definition)

#5 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐝𝐞𝐥𝐞𝐭𝐞
→ kubectl delete pod my-pod
→ kubectl delete service my-service

✅ Debugging and Troubleshooting

#6 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐥𝐨𝐠𝐬
→ kubectl logs my-pod
→ kubectl logs my-pod -c my-container (specify a container)

#7 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐞𝐱𝐞𝐜
→ kubectl exec -it my-pod -- bash (interactive shell)

#8 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐩𝐨𝐫𝐭-𝐟𝐨𝐫𝐰𝐚𝐫𝐝
→ kubectl port-forward my-pod 8080:80

#9 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐭𝐨𝐩
→ kubectl top pod (pod resource usage)
→ kubectl top node (node resource usage)

#10 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐞𝐱𝐩𝐥𝐚𝐢𝐧
→ kubectl explain pod
→ kubectl explain pod.spec (more specific)

✅ Managing Workloads

#11 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐫𝐨𝐥𝐥𝐨𝐮𝐭
→ kubectl rollout status deployment/my-deployment
→ kubectl rollout undo deployment/my-deployment

#12 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐬𝐜𝐚𝐥𝐞
→ kubectl scale deployment/my-deployment --replicas=5

#13 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐞𝐝𝐢𝐭
→ kubectl edit deployment my-deployment


⚡️ 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

📣 Streamlining CI/CD Workflow with GitHub, Jenkins, SonarQube, Docker, Argo-CD and GitOps. ⚙️

𝟏 . 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧.
----------------------------------------------------------------------------------
📌 Git Repo consists of source code for the Spring Boot application. Any commit/changes that happen here will be triggered to Jenkins through Webhook.

📌 As we are using Java application, we use Maven to build the application. If it is a success, it will move to the next stage i.e. code analysis. If it fails, then Jenkins will send an alert to the user through email or Slack notification.

📌 Code Analysis is done through SonarQube. It will check for code vulnerabilities and if it does have one, will send an alert to the user through email or Slack notification. If it does not, then it will move to the next step: Docker.

📌 Here, Docker is used for building the docker image. This image will be saved in Docker hub. If it is a success, it will move to the next step: Continuous Deployment. If it fails, then Jenkins will send an alert to the user and the pipeline ends there.

𝟐. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭
----------------------------------------------------------------------------------
📌 The CD will get to know that the image is updated in the Docker Hub through Shell script/ArgoCD Image Updater. As a new image is updated, the new version is updated in the manifests folder's deployment.yaml file.

📌 GitOps tools are basically Kubernetes controllers, which are sitting inside the Kubernetes cluster. Argo CD will try to maintain a state between the Git repository and the Kubernetes cluster. Whenever there is a change, ArgoCD will pick those changes and deploy the application in the Kubernetes cluster.

🔗 BLOG URL HERE

🔗 GITHUB URL HERE


✈️ 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

➡️ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐃𝐨𝐜𝐤𝐞𝐫𝐟𝐢𝐥𝐞?

A Dockerfile is essentially a set of instructions that Docker follows to build a Docker image. These instructions specify what operating system to use, what software packages to install, what files to copy into the container, what environment variables to set, and what commands to run when the container starts.

➡️𝐖𝐡𝐲 𝐔𝐬𝐞 𝐚 𝐃𝐨𝐜𝐤𝐞𝐫𝐟𝐢𝐥𝐞?

🔹Reproducibility: With a Dockerfile, you can ensure that your application environment is consistent across different environments, such as development, testing, and production. This reproducibility eliminates the "it works on my machine" problem.

🔹Version Control: Dockerfiles are text files that can be version controlled using tools like Git. This means you can track changes to your Docker environment over time and easily roll back to previous versions if needed.

🔹Automation: Dockerfiles enable automation of the containerization process. Once you have defined your Dockerfile, you can use it to build your Docker image with a single command, streamlining the deployment process.

🔹Scalability: Dockerfiles allow you to define the components of your application stack in a modular way. This makes it easy to scale your application by adding or removing containers as needed.

🔹Collaboration: Dockerfiles make it easy to share your application environment with collaborators. By sharing your Dockerfile, others can quickly spin up the same environment on their own machines.


😎 𝗙𝗼𝗹𝗹𝗼𝘄 @devcloudninjas 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!! // Join for DevOps DOCs: @devcloudninjas

➡️ As a software professional, keeping up with the latest DevOps tools is essential for optimizing development and delivery.

The right tools make teams more productive, applications more resilient, and organizations more innovative.

Here are some of the most impactful DevOps tools I recommend learning in 🌟🌟:

✍️ Kubernetes: This open-source container orchestration system simplifies deploying and managing containerized applications. It allows scaling and balancing container workloads.

✍️ Terraform: Infrastructure as code is a must today. Terraform lets you define cloud and on-prem infrastructure in files and provision it automatically.

✍️ Docker: Containerizing apps with Docker packages software into standardized units along with dependencies to run anywhere consistently. It accelerates development.

✍️ Ansible: This configuration management and automation tool executes tasks across nodes via SSH without needing agents. It's simple yet powerful.

✍️ Prometheus: A top open-source monitoring and alerting solution that scrapes metrics and provides robust query capabilities with PromQL.

✍️ Grafana: The leading analytics and visualization platform for monitoring. It’s easy to create rich dashboards for operational insights.

✍️ ELK Stack: Combines Elasticsearch, Logstash, and Kibana for centralized logging, searching, and visualizing log data for troubleshooting.

I aim to provide actionable insights on leading tools so you can advance your skills efficiently.


📱 𝗙𝗼𝗹𝗹𝗼𝘄 @devcloudninjas 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

Here are the main Azure CLI command groups:

1. Account
- az account
- az account clear
- az account list
- az account show
- az account set
2. AD
- az ad
- az ad app
- az ad group
- az ad sp
- az ad user
3. Advisor
- az advisor
- az advisor recommendation
4. Aks
- az aks
- az aks browse
- az aks create
- az aks delete
- az aks get-credentials
- az aks list
- az aks scale
- az aks show
- az aks update
5. Api
- az api
- az api create
- az api delete
- az api list
- az api show
- az api update
6. Appconfig
- az appconfig
- az appconfig create
- az appconfig delete
- az appconfig list
- az appconfig show
- az appconfig update
7. Appservice
- az appservice
- az appservice create
- az appservice delete
- az appservice list
- az appservice show
- az appservice update
8. Backup
- az backup
- az backup container
- az backup item
- az backup job
- az backup policy
- az backup protected-item
- az backup recovery-point
- az backup vault
9. Batch
- az batch
- az batch account
- az batch application
- az batch certificate
- az batch job
- az batch node
- az batch pool
10. Billing
- az billing
- az billing account
- az billing enrollment-account
- az billing invoice
- az billing period
- az billing profile
- az billing subscription

...and many more! You can use az --help to explore more command groups and commands.

Some other commonly used Azure CLI commands include:

- az group: Manage resource groups
- az resource: Manage resources
- az storage: Manage storage accounts
- az vm: Manage virtual machines
- az network: Manage network resources

Remember to use az --help to get more information about each command and its usage

📱 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

📢 Kubernetes All End-to-End Content 2024

▶️ This Includes:
- All Kubernetes Content
- Kubernetes Realtime scenarios
- All Kubernetes Exercises with solutions
- No More AWS PDFs needed
- Easy to Learn from anywhere
- Detailed Explanation guide
- All Kubernetes Tricks & Techniques for DevOps guy
- Added Certified Kubernetes Administrator (CKA) Notes
- All Kubernetes Realtime examples included

📱 Link: https://github.com/devcloudninjas/into-the-devops/tree/master/topics/kubernetes


📱 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

🌟 A Day in the Life of a DevOps Engineer 🌟


👨‍💻 Ever wondered what a DevOps Engineer does every day?Here’s a glimpse into their daily lifecycle:

1. Morning Standup Meeting:
- Participate in a daily scrum meeting to discuss progress, blockers, and plans for the day.

2. Code Review and Integration:
- Review code changes submitted by developers.
- Ensure seamless integration by merging code into the main branch.

3. CI/CD Pipeline Management:
- Monitor and manage Continuous Integration/Continuous Deployment pipelines.
- Fix any issues that arise in automated build and deployment processes.

4. Infrastructure as Code (IaC):
- Write and update scripts using tools like Terraform or CloudFormation.
- Provision and configure cloud resources programmatically.

5. Container Management:
- Build, test, and deploy Docker containers.
- Manage Kubernetes clusters for container orchestration.

6. Monitoring and Incident Response:
- Use tools like Prometheus and Grafana for system monitoring.
- Respond to alerts and troubleshoot issues to maintain system uptime.

7. Configuration Management:
- Automate configuration tasks with Ansible, Chef, or Puppet.
- Ensure consistency across development, testing, and production environments.

8. Collaboration and Communication:
- Work closely with developers, QA, and operations teams.
- Communicate effectively to resolve issues and implement new features.

9. Continuous Improvement:
- Analyze system performance and identify areas for improvement.
- Implement best practices for security, scalability, and efficiency.

10. Learning and Development:
- Stay updated with the latest tools, technologies, and industry trends.
- Participate in training sessions and attend webinars/conferences.

🔧 Being a DevOps Engineer is really FUN. It is dynamic and challenging, requiring a mix of technical skills, problem-solving abilities, and collaboration.


🎄 𝗙𝗼𝗹𝗹𝗼𝘄 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

🔣 Docker is a powerful tool that allows you to package your application and its dependencies into a standardized unit called a container.

👉 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝗵𝗼𝘄 𝗗𝗼𝗰𝗸𝗲𝗿 𝘄𝗼𝗿𝗸𝘀:

➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 are self-contained units that package your application code, runtime, system tools, settings, and libraries. They're like tiny virtual machines, but they share the underlying operating system kernel with other containers, making them much more lightweight and efficient.

➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗶𝗺𝗮𝗴𝗲𝘀 are blueprints that contain instructions for creating containers. They're kind of like recipes that tell Docker how to build a container with all the necessary ingredients. You can find and share images on public registries like Docker Hub, or create your custom images.

➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗱𝗮𝗲𝗺𝗼𝗻 (𝗱𝗼𝗰𝗸𝗲𝗿𝗱) is the engine that builds, runs, and manages Docker containers. It's the behind-the-scenes workhorse that makes everything tick.

➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗰𝗹𝗶𝗲𝗻𝘁 (𝗱𝗼𝗰𝗸𝗲𝗿 𝗖𝗟𝗜) is the tool you use to interact with the Docker daemon. It allows you to build, run, stop, and manage your containers using commands or a graphical user interface (GUI).

➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝗶𝗲𝘀 are repositories that store and share Docker images. Think of them as libraries for Docker images. Docker Hub is the most popular public registry, but there are also private registries that organizations can use to store their custom images.

➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 manage how data is stored and persisted within containers. The default storage driver is overlay2, but there are other options available, such as aufs, zfs, and btrfs.

➡️𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿𝘀 like Kubernetes and Swarm are tools that help you manage and scale large deployments of Docker containers. They take care of provisioning, scheduling, and healing your containers, making it easier to run complex applications in production.


😎 𝗙𝗼𝗹𝗹𝗼𝘄 @devcloudninjas 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

Docker 🐬 & Containers All End-to-End Content 2024 ❤️


⚡️This Includes:
- All Docker-Containers Content
- Docker Realtime scenarios
- All Docker Exercises with solutions
- No More Docker PDFs needed
- Easy to Learn from anywhere
- Detailed Explanation guide
- All Docker file examples for DevOps Engineer

📱 Link : https://github.com/devcloudninjas/into-the-devops/tree/master/topics/containers


📱 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

☁️ Git/GitHub All End-to-End Content 2024

➡️This Includes:

- All Git/GitHub Content with use cases
- Git Realtime scenarios
- All Git/GitHub Exercises with solutions
- No More Git PDFs needed
- Easy to Learn from anywhere
- Detailed Explanation guide
- All Git/GitHub Branching Strategies for DevOps guy

🔗 Link : https://github.com/devcloudninjas/into-the-devops/tree/master/topics/git

💥 Follow me on 🌐GitHub : https://www.github.com/devcloudninjas


✈️ 𝗙𝗼𝗹𝗹𝗼𝘄 @devcloudninjas 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

⚡️ Before spending hours on YouTube Videos/Courses, just know what you are signing up for.

➡️ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Spend 25% of the time dealing with Infrastructure from provisioning to preventing configuration drift and being cloud agnostic.

➡️ 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Shift left the security. From removing unwanted binaries to enforcing runtime security.

➡️ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Write 100s of shell scripts or Ansible Playbooks or build a pipeline to automate the workflow.

➡️ 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Make sure Logging + Profiling + Tracing + Monitoring are in sync.

➡️ 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Write tons of docs for releases, post-mortems and internal operations.

➡️ 𝗧𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁𝗶𝗻𝗴: Do the RCA and spend days cluelessly staring at the screen.


📱 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

➡️What DevOps and Cloud Engineers think their jobs will be:
- 40% Scripting automation
- 30% Cloud deployments
- 20% Monitoring and optimizing
- 10% Team collaboration

➡️What their jobs often actually look like:
- 20% Scripting automation
- 25% Cloud deployments
- 15% Monitoring and optimizing
- 40% Team collaboration
- 65.73% Debating on the infra/tool choices

➡️That’s because, beyond technical aspects, DevOps and Cloud Engineering involves:
- On-demand support
- Many alignment meetings
- Managing system incidents
- Balancing cost-efficiency
- Technical review sessions
- Cross-department collaboration
- Defending infrastructure choices
- Implementing stakeholder feedback

✅Technical skills get you in the door.
✅Communication and collaboration skills push your career forward.
✅To excel, keep up with both the latest technology trends and best practices in teamwork and communication.


📱 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!!

Writing a Kubernetes manifest isn't just about copying and modifying it. Security should be at the core.

Some DevOps engineers don't know how to write a Kubernetes manifest that address to best security practices.

This is just one example; there's much more to consider. You can't get this directly from the documentation. You have to dig deep and pull together the necessary information to produce a secure Kubernetes manifest.


📱 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!!

📣 𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐩𝐥𝐨𝐲 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐨𝐧 𝐀𝐳𝐮𝐫𝐞 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫?

This architecture describes how to deploy a microservices application on Azure Kubernetes Service (AKS). AKS is a managed Kubernetes service that makes it easy to deploy, manage, and scale containerized applications.

The architecture consists of the following components:
✅AKS cluster: The AKS cluster is the foundation of the architecture. It provides the infrastructure for running the microservices application.

✅Virtual network: The virtual network isolates the AKS cluster from the rest of the Azure network. It also provides a private network for the microservices application to communicate with each other.

✅Ingress controller: The ingress controller is responsible for routing traffic to the different microservices in the application.

✅Azure Load Balancer: The Azure Load Balancer distributes traffic evenly across the nodes in the AKS cluster.

✅Azure Container Registry: The Azure Container Registry is a private Docker registry for storing the Docker images for the microservices application.

✅Azure Pipelines: Azure Pipelines is a continuous integration and continuous delivery (CI/CD) service that can be used to build, test, and deploy the microservices application to AKS.

✅Helm: Helm is a package manager for Kubernetes that can be used to manage the Kubernetes manifests for the microservices application.

✅Azure Monitor: Azure Monitor collects and stores metrics, logs, and traces for the microservices application. It can be used to monitor the health of the application and troubleshoot problems.

➡️ Deployment process:

The following steps describe the process for deploying a microservices application to AKS using this architecture:
☑️Create an AKS cluster.
☑️Create a virtual network for the AKS cluster.
☑️Deploy the ingress controller to the AKS cluster.
☑️Create an Azure Load Balancer.
☑️Create an Azure Container Registry.
☑️Push the Docker images for the microservices application to the Azure Container Registry.
☑️Create a Helm chart for the microservices application.
☑️Deploy the Helm chart to the AKS cluster.
☑️Configure Azure Monitor to collect metrics, logs, and traces for the microservices application.


😎 𝗙𝗼𝗹𝗹𝗼𝘄 @devcloudninjas 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗰𝗹𝗼𝘂𝗱 & 𝗗𝗲𝘃𝗢𝗽𝘀!!!

🚨 AWS with Terraform and Jenkins Pipeline ♾

In this article, we will explain how to create and manage the public and private subnets using terraform and create instance in the desired subnet.

🌐 Blog Link: https://blog.devcloudninjas.com/posts/deploying-a-kubernetes-cluster-on-azure-kubernetes-serviceaks-with-terraform

☁️ Source Code Link: https://github.com/devcloudninjas/Jenkins-Terraform-AWS-Infra


💬 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

After a long day of troubleshooting. I realized the error was in the Replicaset and Deployment. Lets talk about that.

📣 ReplicaSet and Deployment :

ReplicaSet and Deployment are both Kubernetes resources used for managing and scaling application instances, but they serve different purposes and have distinct features:

🔖 ReplicaSet:
➡️A ReplicaSet ensures that a specified number of pod replicas are running at any given time.
It is a lower-level controller in Kubernetes.
It does not support declarative updates or rollback strategies.
➡️When using a ReplicaSet, you manage updates manually by creating new ReplicaSets with updated configurations and then deleting the old ReplicaSets.
It is often used directly only in advanced scenarios where finer control over scaling and updates is required.

🔖 Deployment:
➡️A Deployment is a higher-level resource that manages ReplicaSets and provides declarative updates and rollback strategies.
It is a higher-level abstraction built on top of ReplicaSets.
➡️Deployments allow you to describe the desired state of your application, and Kubernetes handles the process of creating and managing the ReplicaSets to achieve that state.


🎄 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!!

➡️ Mastering GitHub Actions for DevOps Engineers: A Complete Guide from Beginner to Advanced ✅


🖥 Read it now: https://dev.to/devcloudninjas/mastering-github-actions-for-devops-engineers-a-complete-guide-from-beginner-to-advanced-2lc1

Are you ready to level up your DevOps skills with GitHub Actions? Whether you're a beginner or an experienced engineer, this comprehensive guide has everything you need to know about automating CI/CD pipelines, integrating with popular DevOps tools, and optimizing workflows.

🔥 Topics Covered:
✅ Core Concepts (Workflows, Jobs, Actions)
✅ CI/CD Pipeline Automation
✅ Advanced Concepts like Matrix Builds & Self-Hosted Runners
✅ Best Practices for Optimization
✅ Real-World Use Cases

👨‍💻 This guide is packed with practical tips and examples to help you master GitHub Actions!

🎄 𝗙𝗼𝗹𝗹𝗼𝘄 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

🔔 𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐨𝐧 𝐚 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐒𝐜𝐚𝐥𝐞

Netflix's database infrastructure is a true marvel! They use a combination of several cutting-edge technologies to ensure content is available 24/7, without buffering or interruptions.

Netflix's engineering team leverages a diverse array of databases to deliver top-notch service. Here's a glimpse into their database selection:

🔍 Relational Databases: For billing transactions, subscriptions, taxes, and revenue, Netflix chooses MySQL. They also harness CockroachDB to support multi-region active-active architecture, global transactions, and data pipeline workflows.

📊 Columnar Databases: Netflix turns to Redshift and Druid for structured data storage, Spark and data pipeline processing, and Tableau for data visualization, especially for analytics purposes.

🔑 Key-Value Databases: Netflix's trusted companion for over a decade is EVCache, built on top of Memcached. It's the go-to for caching various data, powering the Netflix Homepage, and delivering personalized recommendations.

📚 Wide-Column Databases: Cassandra takes the stage for almost everything, from Video/Actor information to User Data, Device details, and Viewing History.

🎮 Time-Series Databases: Netflix's innovation shines with Atlas, an open-source in-memory database designed for metrics storage and aggregation.

⏸ Unstructured Data: When it comes to storing Image/Video/Metrics/Log files, Netflix relies on S3 as the default choice. They also harness the power of Apache Iceberg with S3 for big data storage.


🎄 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!!

📣 ReplicaSet and Deployment :

ReplicaSet and Deployment are both Kubernetes resources used for managing and scaling application instances, but they serve different purposes and have distinct features:

🔖 ReplicaSet:
➡️A ReplicaSet ensures that a specified number of pod replicas are running at any given time.
It is a lower-level controller in Kubernetes.
It does not support declarative updates or rollback strategies.
➡️When using a ReplicaSet, you manage updates manually by creating new ReplicaSets with updated configurations and then deleting the old ReplicaSets.
It is often used directly only in advanced scenarios where finer control over scaling and updates is required.

🔖 Deployment:
➡️A Deployment is a higher-level resource that manages ReplicaSets and provides declarative updates and rollback strategies.
It is a higher-level abstraction built on top of ReplicaSets.
➡️Deployments allow you to describe the desired state of your application, and Kubernetes handles the process of creating and managing the ReplicaSets to achieve that state.


🎄 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas

⚠️ As a DevOps engineer, understanding Splunk commands is essential for effective log analysis and monitoring.

Here are some commonly used Splunk commands:

1. search: The primary command for searching data in Splunk. Use it to retrieve events based on specific criteria.

2. index: Specifies the index from which to retrieve data. You can filter data by index using this command.

3. source: Filters events based on the source of the data (e.g., log files, network streams).

4. sourcetype: Filters events based on the type of data source (e.g., Apache logs, Windows Event Logs).

5. eval: Creates calculated fields or modifies existing fields. Useful for creating custom fields or transforming data.

6. stats: Aggregates and summarizes data. You can use it to calculate counts, averages, and other statistics.

7. timechart: Generates time-based charts and visualizations. Useful for trend analysis and identifying patterns over time.

8. rex: Extracts fields using regular expressions. Helpful when dealing with unstructured data.

9. dedup: Removes duplicate events based on specified fields.

10. transaction: Groups related events into transactions. Useful for analyzing multi-step processes.

11. top: Identifies the top values for a specific field (e.g., top IP addresses, top error codes).

12. lookup: Enriches events by joining them with external lookup tables (e.g., mapping IP addresses to geolocation data).

Remember that these commands are just a starting point. Depending on your use case, you might need to explore additional commands and features. Happy Splunking! 🚀🔍

For more detailed information, check out the Splunk Cheat Sheet and the Splunk Quick Reference Guide[1][2].

➡️Reference links: [1] [2] [3]


😎 𝐅𝐨𝐥𝐥𝐨𝐰 @devcloudninjas 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devcloudninjas