Empowering businesses with cloud solutions and automation 🚀 | AWS DevOps Professional | 13+ years of IT expertise

Hi, I’m Swapnil Yavalkar, an AWS Cloud Professional with over 5 years of experience in AWS & DevOps and 13+ years of IT expertise. I specialize in building scalable, resilient infrastructure and automating processes for maximum efficiency, helping businesses reduce costs and improve workflows. With a background in Tableau & SAP BO administration, I have managed complex environments and led numerous successful cloud projects.

💻 Cloud & DevOps Engineer | 🔧 Automation Expert | 📊 BI Specialist

I’m passionate about cloud computing, automation, and DevOps, and I've worked with multiple organizations to optimize their cloud infrastructure, reduce operational costs, and streamline deployments through CI/CD pipelines. My experience in Business Intelligence also includes implementing large-scale solutions using Tableau and SAP BO.

🎓 AWS Certified Solutions Architect - Professional 🎓 SCJP (Sun Certified Java Programmer)
🎓 CCNA (Cisco Certified Network Associate)

Languages	Cloud Platforms	DevOps Tools	Automation & Monitoring

Here are some key projects I’ve worked on, showcasing my experience in AWS and DevOps:

1. Cloud Infrastructure Optimization for E-commerce Platform
   Optimized AWS infrastructure for a global e-commerce company, reducing costs by 30% through EC2 Spot Instances and Reserved Instances (RIs), while automating the environment with CloudFormation templates.
   Technologies: AWS (EC2, RDS, S3, CloudFormation), Jenkins, Docker.

2. CI/CD Pipeline Implementation for an Online Payment Firm
   Designed and implemented a CI/CD pipeline using Jenkins for a payment processing company, reducing deployment times by 50% and improving software delivery efficiency.
   Technologies: Jenkins, Docker, Kubernetes, AWS ECS.

3. Highly Available Tableau Deployment for a Fortune 500 Company
   Deployed a highly available multi-node Tableau environment on AWS with RDS for database storage, S3 for backups, and CloudWatch for monitoring. Ensured continuous uptime with Auto Scaling and Elastic Load Balancing.
   Technologies: AWS (EC2, RDS, S3, CloudWatch), Tableau, Auto Scaling.

4. Serverless API for Retail Analytics
   Developed and deployed a serverless API for a retail company using AWS Lambda, API Gateway, and DynamoDB to handle real-time analytics data processing.
   Technologies: AWS Lambda, API Gateway, DynamoDB, S3.

5. Data Lake Implementation for a Media Streaming Service
   Implemented a data lake architecture using S3 and AWS Glue for a media streaming service to centralize and process streaming analytics data.
   Technologies: AWS S3, AWS Glue, Athena.

Here’s what my clients have to say about my work:

• Surajit Bhowmick - AWS Cloud Specialist:
  "Swapnil's expertise in AWS, DevOps, BO, and Tableau is commendable. He is dedicated and always goes the extra mile, helping our team deliver efficient solutions."

• Subhash A Kotian - Product Support Head at LearningMate:
  "Swapnil manages pressure with ease and communicates challenges effectively. His work ethic and dedication make him a great team player."

• Sanket Shah - Project Manager:
  "Swapnil is an excellent asset with commendable knowledge in BI and DevOps. His constant learning attitude and dedication make him stand out."

• Sandesh Kortikar - Senior Delivery Manager: "Since I know Swapnil, I always found him to be very dedicated, self motivated and go getter fella. He is very sincere in his work and keeps on Innovating some or the other thing to ease out everyone's life in team. Technically very good and he us quite fast learner. I wish him all the best in his future endeavors."

• 🗣️ You can find testimonials on my LinkedIn profile.

Here are some of my community projects and open-source repositories:

Each project folder is organized into AWS Infrastructure Projects and DevOps Projects categories, with subcategories based on difficulty levels: Easy, Medium, Hard, and Complex. Each project folder contains a detailed README.md file with step-by-step instructions to keep everything simple and easy to follow.

📦 aws-devops-realworld-projects
 ┣ 📂 aws-infrastructure-projects
 ┃ ┣ 📂 easy
 ┃ ┃ ┣ 📂 01-cloudfront-s3-static-websites
 ┃ ┃ ┣ 📂 02-s3-crossregion-replication
 ┃ ┃ ┣ 📂 03-security-best-practices-iam
 ┃ ┃ ┣ 📂 04-ec2-autoscaling-loadbalancer
 ┃ ┃ ┣ 📂 05-monitoring-ecs-prometheus-grafana
 ┃ ┃ ┗ 📂 06-infrastructure-as-code-cloudformation
 ┃ ┣ 📂 medium
 ┃ ┃ ┣ 📂 01-multiregion-failover-route53
 ┃ ┃ ┣ 📂 02-loadbalancing-alb-nlb
 ┃ ┃ ┣ 📂 03-automated-backups-aws-backup
 ┃ ┃ ┣ 📂 04-secure-configurations-secretsmanager
 ┃ ┃ ┣ 📂 05-lambdaatedge-content-delivery
 ┃ ┃ ┣ 📂 06-data-lake-s3-athena-glue
 ┃ ┃ ┣ 📂 07-s3-event-driven-architecture-with-sns-lambda-sqs
 ┃ ┃ ┗ 📂 08-serverless-lambda-api-gateway-dynamodb
 ┃ ┣ 📂 hard
 ┃ ┃ ┣ 📂 01-api-security-waf
 ┃ ┃ ┣ 📂 02-vpc-peering-transitive-routing
 ┃ ┃ ┣ 📂 03-data-migration-dms
 ┃ ┃ ┣ 📂 04-stepfunctions-orchestration
 ┃ ┃ ┣ 📂 05-amazon-aurora-highavailability
 ┃ ┃ ┗ 📂 06-graphql-api-appsync
 ┃ ┣ 📂 complex
 ┃ ┃ ┣ 📂 01-multiregion-activeactive-rds
 ┃ ┃ ┣ 📂 02-data-lakehouse-redshift-s3
 ┃ ┃ ┣ 📂 03-data-governance-lakeformation
 ┃ ┃ ┣ 📂 04-hybrid-cloud-aws-outposts-s3
 ┃ ┃ ┣ 📂 05-edge-computing-greengrass
 ┃ ┃ ┣ 📂 06-security-compliance-macie-guardduty
 ┃ ┃ ┣ 📂 07-codeartifact-codepipeline
 ┃ ┃ ┣ 📂 08-serverless-container-fargate
 ┃ ┃ ┣ 📂 09-rds-proxy-aurora-optimization
 ┃ ┃ ┗ 📂 10-serverless-container-lambda
 ┗ 📂 devops-projects
 ┃ ┣ 📂 easy
 ┃ ┃ ┣ 📂 01-ci-cd-jenkins-docker
 ┃ ┃ ┣ 📂 02-ci-cd-jenkins-ec2
 ┃ ┃ ┣ 📂 03-kubernetes-cluster-kops
 ┃ ┃ ┣ 📂 04-kubernetes-cluster-terraform
 ┃ ┃ ┣ 📂 05-automated-deployment-codebuild
 ┃ ┃ ┗ 📂 06-ci-cd-jenkins-github-elastic-beanstalk
 ┃ ┣ 📂 medium
 ┃ ┃ ┣ 📂 01-automated-deployment-codedeploy
 ┃ ┃ ┣ 📂 02-containerized-microservices-ecs
 ┃ ┃ ┣ 📂 03-stepfunctions-orchestration
 ┃ ┃ ┣ 📂 04-realtime-dataanalytics-kinesis-lambda
 ┃ ┃ ┣ 📂 05-containerized-microservices-eks
 ┃ ┃ ┗ 📂 06-automated-microservices-deployment-ecs-cicd
 ┃ ┣ 📂 hard
 ┃ ┃ ┣ 📂 01-deploying-microservices-ecs
 ┃ ┃ ┣ 📂 02-music-streaming-service-aws
 ┃ ┃ ┣ 📂 03-video-streaming-service-aws
 ┃ ┃ ┣ 📂 04-serverless-webapp-aws-amplify
 ┃ ┃ ┣ 📂 05-scalable-node.js-app-deployment-elastic-beanstalk
 ┃ ┃ ┗ 📂 06-enterprise-application-deployment-kubernetes-terraform-ansible-gitlab
 ┃ ┣ 📂 complex
 ┃ ┃ ┣ 📂 01-ecs-bluegreen-deployment
 ┃ ┃ ┣ 📂 02-music-streaming-portal-amazonmusic
 ┃ ┃ ┣ 📂 03-video-streaming-netflix
 ┃ ┃ ┣ 📂 04-image-processing-sagemaker
 ┃ ┃ ┣ 📂 05-advanced-cicd-pipeline-codepipeline
 ┃ ┃ ┣ 📂 06-sagemaker-ml-pipeline
 ┃ ┃ ┣ 📂 07-realtime-video-processing-kinesis-s3
 ┃ ┃ ┗ 📂 08-full-stack-cicd-pipeline-jenkins-aws-codedeploy-nginx

Check out my Docker images on Docker Hub:
Swapnil Yavalkar's Docker Hub

💬 I’m always open to connecting with fellow professionals and collaborating on exciting AWS, DevOps, or automation projects! Reach out to me via LinkedIn or check out my open-source projects on GitHub. Let’s discuss how I can help you automate cloud processes or streamline your DevOps pipelines.

Here's a simple Python script I created to automate Tableau Subscriptions Removal:

import pandas as pd
import tableauserverclient as TSC
import logging
import time
import os
import datetime

# Define the log directory path where the files are located
directory_path = "logs/"

# Define the threshold time to delete files that are older than 10 days
threshold_time = datetime.datetime.now() - datetime.timedelta(days=10)

""" SPECIFY TABLEAU SERVER LOGIN DETAILS BELOW """
server_url = ''
sites = ''
username = ''
password = ''

LOG_FILE_GEN_TIME = time.strftime("%Y%m%d-%H%M%S")

logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s, %(levelname)-8s [%(filename)s:%(module)s:%(funcName)s:%(name)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d:%H:%M:%S',
    filename='logs/SubscriptionsRemoval{0}.log'.format(LOG_FILE_GEN_TIME),
    filemode='a'
)

logger = logging.getLogger(__name__)


def delete_logs():
    global directory_path
    # Loop through all files in the logs directory
    try:
        for file_name in os.listdir(directory_path):
            # Get the creation time of the file
            file_path = os.path.join(directory_path, file_name)
            creation_time = datetime.datetime.fromtimestamp(os.path.getctime(file_path))
            # Check if the file is older than the threshold time
            if creation_time < threshold_time:
                # Delete the file if it's older than the threshold time
                os.remove(file_path)
                print(f"Log Deleted file: {file_name}")
                logger.info(f"Log file older than 10 days deleted file: {file_name}")
    except Exception as e:
        logging.error(f"Error while deleting log files: {str(e)}")