Hiring! AWS DevOps Engineer at LogiQuad Solutions ⭐️
➡️ Exp:- 3 to 5 years
➡️ Location : Remote
🔧 Expertise & Experience:-
AWS Cloud: Extensive hands-on experience designing and managing scalable, secure cloud environments.
Terraform: Proficient in Infrastructure as Code (IaC) to automate and optimize infrastructure deployment.
Kubernetes: Skilled in deploying and managing containerized applications for seamless, efficient scaling.
Serverless Applications: Developed and maintained serverless architectures, ensuring cost-efficiency and agility.
Infrastructure Development: Proven track record of building infrastructure from scratch, tailoring solutions to meet business needs.
✉️ Interested candidates can send their resumes to ppandya@logiquad.com
✈️ 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs
AWS Cloud: Extensive hands-on experience designing and managing scalable, secure cloud environments.
Terraform: Proficient in Infrastructure as Code (IaC) to automate and optimize infrastructure deployment.
Kubernetes: Skilled in deploying and managing containerized applications for seamless, efficient scaling.
Serverless Applications: Developed and maintained serverless architectures, ensuring cost-efficiency and agility.
Infrastructure Development: Proven track record of building infrastructure from scratch, tailoring solutions to meet business needs.
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Basic 📱 Git Flow in DevOps ♾ CI-CD!
1️⃣ . Developer Creates Feature Branch: The developer creates a new feature branch and is used to work on a new feature or a specific task.
2️⃣ . Developer Writes Code: The developer writes the necessary code for the feature in their local development environment.
3️⃣ . Developer Commits Changes: Once the developer is satisfied with the changes, they commit the changes to the feature branch in the local Git repository.
4️⃣ . Developer Creates Pull Request: The developer pushes the committed changes by creating a pull request to merge the feature branch into the main branch.
5️⃣ . Code Review by Team: The pull request initiates a code review process where team members review the changes.
6️⃣ . Approval of Pull Request: After addressing any feedback and making necessary adjustments, the pull request is approved by the reviewers.
7️⃣ . Merge to Main Branch: The approved pull request is merged into the main branch of the Git repository.
8️⃣ . Triggers CI/CD Pipeline: This automation ensures that the changes are continuously integrated and deployed.
9️⃣ . Then we follow the procedure for building and testing the code, deploying to staging env. Once the tests in the staging environment pass, a manual approval is required to deploy the changes to the production environment. Once the code is deployed to production env, the prod env is monitored using Prometheus to track the performance and health of the application. The collected metrics are visualized using Grafana. Finally alerts are configured.
❤️ 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs
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Here's a streamlined workflow for managing Terraform remote state with AWS:
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In this article, we’ll explore a practical example of a Fully Serverless Architecture implemented using Terraform — a popular IaC tool and CI/CD implemented using GitHub Actions. The code repository we’ll be examining is hosted on GitHub
I have a NodeJS Cloud Native API which I have used to deploy in this architecture. This API is specifically designed to make use of AWS serverless services.
Following are the serverless services used in this project:
❤️🔥 Share with friends and colleagues❤️🔥
📣 Note: Fork this Repository☁️ for upcoming future projects, Every week releases new Project.
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1. What is Jenkins and why is it used in DevOps?
2. Explain the key features of Jenkins.
3. What are Jenkins plugins, and how do they extend Jenkins functionality?
4. How do you install Jenkins?
5. What are the different ways to set up Jenkins?
6. What is a Jenkins Pipeline?
7. What are the differences between Declarative and Scripted Pipelines in Jenkins?
8. How do you configure a Jenkins job?
9. Explain how you would create and use Jenkinsfiles.
10. What is the difference between a Freestyle project and a Pipeline in Jenkins?
11. How do you schedule a Jenkins job?
12. How do you secure Jenkins?
13. How do you manage users and roles in Jenkins?
14. Explain how to backup and restore Jenkins configurations.
15. What strategies would you use to scale Jenkins?
16. How do you integrate Jenkins with version control systems like Git?
17. What are some common CI/CD tools that integrate with Jenkins?
18. How do you automate tests with Jenkins?
19. Describe how to set up a continuous deployment pipeline with Jenkins.
20. How do you use Jenkins to deploy applications to different environments (e.g., dev, test, prod)?
21. How do you monitor Jenkins and its jobs?
22. What are some common issues you might encounter with Jenkins and how do you resolve them?
23. How can you optimize Jenkins performance?
24. What strategies would you use to handle long-running jobs in Jenkins?
25. How do you handle failing Jenkins builds?
26. Explain the use of Jenkins agents and how to configure them.
27. What is the role of Blue Ocean in Jenkins?
28. How do you use Jenkins for building Docker images?
29. Describe how you can trigger Jenkins jobs remotely.
30. How do you use Jenkins with Kubernetes for CI/CD?
31. Describe a CI/CD pipeline you have implemented using Jenkins.
32. How do you handle secrets and credentials in Jenkins?
33. How would you migrate Jenkins jobs from one server to another?
34. Explain a situation where you improved the CI/CD process using Jenkins.
35. How do you manage dependencies in a Jenkins pipeline?
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Gradle is a popular build automation tool used for Java, Groovy, and Kotlin projects, offering similar functionality to Maven but with a more flexible and powerful build scripting language.
➡️ These commands are fundamental for building, testing, packaging, and managing dependencies in Gradle projects, making them essential tools for DevOps practitioners working with Java, Groovy, or Kotlin applications.
📱 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs
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AWS Certified Solutions Architect - Associate
This Article will showcase:
• Knowledge and skills in compute, networking, storage, and database AWS services as well as AWS deployment and management services
• Knowledge and skills in deploying, managing, and operating workloads on AWS as well as implementing…
• Knowledge and skills in compute, networking, storage, and database AWS services as well as AWS deployment and management services
• Knowledge and skills in deploying, managing, and operating workloads on AWS as well as implementing…
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Looking for a skilled DevOps Engineer (1-4 years experience) in Bangalore, Whitefield.
Manage GCP infrastructure
Automate with Terraform
Containerize with Docker
Implement CI/CD pipelines using Jenkins
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Basic 📱 Git Flow in DevOps ♾ CI-CD!
1️⃣ . Developer Creates Feature Branch: The developer creates a new feature branch and is used to work on a new feature or a specific task.
2️⃣ . Developer Writes Code: The developer writes the necessary code for the feature in their local development environment.
3️⃣ . Developer Commits Changes: Once the developer is satisfied with the changes, they commit the changes to the feature branch in the local Git repository.
4️⃣ . Developer Creates Pull Request: The developer pushes the committed changes by creating a pull request to merge the feature branch into the main branch.
5️⃣ . Code Review by Team: The pull request initiates a code review process where team members review the changes.
6️⃣ . Approval of Pull Request: After addressing any feedback and making necessary adjustments, the pull request is approved by the reviewers.
7️⃣ . Merge to Main Branch: The approved pull request is merged into the main branch of the Git repository.
8️⃣ . Triggers CI/CD Pipeline: This automation ensures that the changes are continuously integrated and deployed.
9️⃣ . Then we follow the procedure for building and testing the code, deploying to staging env. Once the tests in the staging environment pass, a manual approval is required to deploy the changes to the production environment. Once the code is deployed to production env, the prod env is monitored using Prometheus to track the performance and health of the application. The collected metrics are visualized using Grafana. Finally alerts are configured.
❤️ 𝐅𝐨𝐥𝐥𝐨𝐰 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs
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- AWS CloudFormation
- AWS CDK
- AWS CloudWatch
- AWS CloudTrail
- AWS CodePipeline
- AWS CodeBuild
- AWS CodeDeploy
- AWS Systems Manager
- AWS OpsWorks
- AWS IAM
- AWS KMS
- AWS VPC
- AWS Direct Connect
- AWS ECS
- AWS ECR
- AWS EKS
- AWS Lambdas
- AWS API Gateway
- AWS RDS
- AWS DynamoDB
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How Docker 🐬 Works Explained
Docker is a platform that simplifies application development and deployment through containerization.
➡️ Here's a brief overview of how it works:
1. Developer: Writes code and prepares a Dockerfile with instructions to build an image.
2. Client: Uses Docker commands (docker build, docker pull, docker run, docker push) to interact with Docker.
3. Dockerfile: Script containing instructions to create an image, specifying base images and configurations.
4. Registry: Stores Docker images, which can be pulled or pushed by developers.
5. Docker Host: Runs the Docker daemon, managing images and containers.
6. Docker Daemon: Background service that manages the lifecycle of containers.
7. Images: Templates for creating containers, containing applications and dependencies.
8. Containers: Isolated environments where applications run, sharing the host system's kernel.
➡️ Workflow:
- Build: Developer creates an image from a Dockerfile.
- Push: Image is uploaded to a registry.
- Pull: Image is downloaded from the registry.
- Run: Container is created and started from the image.
❤️ 𝗙𝗼𝗹𝗹𝗼𝘄 @prodevopsguy 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐜𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬!!! // 𝐉𝐨𝐢𝐧 𝐟𝐨𝐫 𝐃𝐞𝐯𝐎𝐩𝐬 𝐃𝐎𝐂𝐬: @devopsdocs
Docker is a platform that simplifies application development and deployment through containerization.
1. Developer: Writes code and prepares a Dockerfile with instructions to build an image.
2. Client: Uses Docker commands (docker build, docker pull, docker run, docker push) to interact with Docker.
3. Dockerfile: Script containing instructions to create an image, specifying base images and configurations.
4. Registry: Stores Docker images, which can be pulled or pushed by developers.
5. Docker Host: Runs the Docker daemon, managing images and containers.
6. Docker Daemon: Background service that manages the lifecycle of containers.
7. Images: Templates for creating containers, containing applications and dependencies.
8. Containers: Isolated environments where applications run, sharing the host system's kernel.
- Build: Developer creates an image from a Dockerfile.
- Push: Image is uploaded to a registry.
- Pull: Image is downloaded from the registry.
- Run: Container is created and started from the image.
Docker ensures applications are portable and consistent across different environments, simplifying deployment and scaling.
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We face this issue when the image is not present in registry or the given image tag is wrong.
Make sure you provide correct registry url, image name and image tag.
We might face authentication failures, when image is being stored in a private registry, make sure to create secret with private registry credentials and add created secret in Kubernetes Deployment File to pull docker image.
We face this issue when the process deployed inside container not running then the POD will be moved to CrashLoopBackOff.
POD might be running out of CPU or memory, POD should get enough resources allocated that’s cpu and memory for an application to be up and running, to fix that check in Resources Requests and Resources Limits.
We face this issue when PODs tries to utilise more memory than the limits we have set.
We can resolve it by setting appropriate resource request and resource limit.
When nodes might not be ready and required resources like CPU and Memory may not be available in nodes for the PODs to be up and running.
POD will be scheduled to a node but POD won’t be running in scheduled node.
We can fix this by providing correct image name, image tag and authentication to registry.
We can fix this by creating appropriate service.
If service is already created and application is still not accessible, make sure application and service are deployed in same namespace.
We can resolve this by setting appropriate resource requests and resource limits for the PODs and having enough resources in worker nodes.
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