Streamline Your Python Development with AWS DevOps
👋 Hello! I'm passionate about DevOps and have over 1+ years of experience in the field. I'm proficient in a variety of cutting-edge technologies and always motivated to expand my knowledge and skills. Let's connect and grow together!
SKILLS:
🔹 Languages & Runtimes: Python, Shell Scripting, HCL, YAML 🔹 Cloud Technologies: AWS, Microsoft Azure, GCP 🔹 Infrastructure Tools: Docker, Terraform, AWS CloudFormation 🔹 Other Tools: Linux, Git and GitHub Actions, Jenkins, Jira, GitLab (beginner), Docker, AWS DevOps 🔹 Web Development: HTML, CSS, Bootstrap, Python, SQL
Job & Responsibilities:
🚀 Improved development efficiency by implementing CI/CD pipelines, resulting in a 30% reduction in deployment time on the test server. 🔒 Strengthened deployment and testing reliability by utilizing Docker containers and optimizing Dockerfile, reducing development issues on the test server by 20%. ⚙️ Automated S3 bucket log creation with Shell scripting, eliminating 100% of manual search and saving 2 hours per week. 📅 Scheduled EC2 instance start/stop using Lambda functions and Event Bridge, leading to a 25% decrease in infrastructure costs. 🔧 Utilized AWS, Linux, Python, Docker, Shell scripting, Terraform, Jenkins Pipelines, and automation to streamline workflows and improve overall system performance.
I'm very detail-oriented and possess strong written and verbal communication skills. As a high performer with a possibility mindset, I strive to solve problems using efficient approaches.
Let's Connect & Grow:
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Step-by-step guide
For implementing Continuous Integration for a Python application using AWS DevOps tools:
Create an AWS account: If you don't already have an AWS account, create one by visiting the AWS website and signing up for a new account.
Set up CodeCommit: Create a CodeCommit repository to host the code for the Python application. Clone the repository to your local development environment.
Write the code: Write the Python code for the application and commit the changes to the CodeCommit repository.
Set up CodeBuild: Create a CodeBuild project to build and test the code. In the CodeBuild project, specify the CodeCommit repository as the source code location, configure the build environment, and specify the build commands to compile the code and run the tests.
Set up CodeGuru: Create a CodeGuru repository assessment to analyze the code for best practices and coding standards. In the CodeGuru repository assessment, specify the CodeCommit repository as the source code location.
Set up CodeReview: Create a CodeReview review to provide feedback on code changes. In the CodeReview review, specify the CodeCommit repository as the source code location.
Set up CodeDeploy: Create a CodeDeploy application and deployment group to deploy the code to a production-ready environment. In the CodeDeploy application and deployment group, specify the CodeBuild output as the source code location and configure the deployment settings.
Set up CloudWatch: Create a CloudWatch alarm to monitor the health of the application. In the CloudWatch alarm, specify the CloudWatch metrics to track and the alarm actions to take when the thresholds are breached.
Test the pipeline: Push a change to the CodeCommit repository to trigger the CI/CD pipeline. Verify that the code is built, tested, analyzed, reviewed, and deployed as expected.
Conclusion
This guide provides a high-level overview of the steps involved in implementing Continuous Integration for a Python application using AWS DevOps tools. The specific details of each step will vary depending on the requirements of your project, but this guide should give you a good starting point.
