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Building Scalable Microservices on AWS: Deploy a Cryptocurrency App

Unlock the power of AWS and elevate your software architecture with our course on building scalable microservices—learn to design, develop, deploy, and manage with hands-on labs and real-world applications
Raghunandana Sanur
Staff Data Engineer & MLOps Engineer at Talabat
DevOps Pre-Requisite Course
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What you’ll learn

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Welcome to the comprehensive course on building scalable microservices using Amazon Web Services (AWS). In this course, you will learn everything you need to know to design, develop, deploy, and manage microservices architecture on AWS. You will create microservices with our end-to-end hands-on labs.

Here are some of the key topics covered in this course:

  1. Introduction to Microservices:
    • Understand the principles and benefits of microservices architecture.
    • Learn how microservices differ from monolithic architectures.
    • Explore real-world use cases and examples of successful microservices implementations.
  2. Setting up Cloud9 and Docker:
    • Set up an AWS Cloud9 development environment for efficient coding and collaboration.
    • Learn Docker fundamentals and how to containerize your microservices for easy deployment and scalability.
  3. CodeBuild and ECR:
    • Utilize AWS CodeBuild to automate build processes and ensure code quality.
    • Learn how to securely store and manage Docker images with Amazon Elastic Container Registry (ECR).
  4. Deploying Services onto ECS:
    • Deploy your microservices onto Amazon Elastic Container Service (ECS) for high availability and scalability.
    • Configure ECS clusters, services, and tasks to efficiently run your containers.
  5. Task Definition, Image Tags in Production, and Load Balancer:
    • Define tasks and specify container configurations using ECS Task Definitions.
    • Implement best practices for managing image tags in production environments.
    • Set up and configure AWS Elastic Load Balancer (ELB) to distribute traffic to your microservices.
  6. Rolling Deployment and Rollback of Deployments:
    • Implement rolling deployments to update your microservices with zero downtime.
    • Learn rollback strategies to revert to previous versions in case of issues or failures.
  7. CodePipeline Automated Deployment:
    • Automate your deployment process using AWS CodePipeline.
    • Create CI/CD pipelines to build, test, and deploy your microservices with ease.
  8. Monolith to Microservice Design:
    • Understand the challenges and benefits of transitioning from a monolithic architecture to microservices.
    • Learn strategies and best practices for refactoring and decomposing monolithic applications into microservices.

Throughout this course, you will work on hands-on labs and real-world projects to reinforce your understanding of each topic. By the end of this course, you will have the skills and knowledge to architect, build, and deploy scalable microservices applications on AWS effectively.

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About the instructor

Raghunandana Krishnamurthy, currently serving as a Staff Data Engineer and MLOps Engineer at Talabat, is renowned for his expertise in the fields of data engineering and machine learning operations. He has a strong background in both GCP and AWS platforms, utilizing tools like SageMaker and VertexAI to accelerate model development and deployment.

His experience at HelloFresh as a Senior Data Engineer involved migrating legacy ETL to AWS EMR and managing hybrid data infrastructure, showcasing his proficiency in big data cloud stacks. He was also responsible for creating visibility on ETL's through monitoring and alerting with Prometheus, Grafana, and other tools.

At Careem, Raghunandana was a Data Engineering Technical Lead, focusing on big data platform development and ensuring the health and alignment of the growing team. His responsibilities included maintaining big data platforms and ensuring data quality for analytics, machine learning, and AI applications.

His tenure at Cerner Corporation as a Big Data Engineer further highlights his deep understanding of Hadoop systems and DevOps culture. He was involved in the development, maintenance, and upgrading of Hadoop clusters, as well as in building scalable distributed systems.

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This course comes with hands-on cloud labs
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