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Real World Cloud Deployment and CICD Essentials

Understand How Data Engineering Code Moves from Development to Production Using AWS CloudFormation, GitHub Actions, and Real-World CI/CD Workflows.

Course Summary

Real World Cloud Deployment and CI/CD Essentials

Ever wondered how Data Engineering code actually moves from development to production in real-world companies?

As a Data Engineer, building an AWS Glue job, Lambda function, or Step Functions workflow is only part of the job.

But what happens after you've developed and tested your pipeline?

How does your code move from Development to QA and finally to Production? Who creates the infrastructure? How do Git, AWS CloudFormation, and CI/CD pipelines work together? And how do you confidently explain this entire process in a Data Engineering interview?

That's exactly what you'll learn in this course.

Real World Cloud Deployment and CI/CD Essentials is designed specifically for Data Engineers who want to understand the end-to-end deployment lifecycle followed in real-world AWS environments.

Rather than turning you into a DevOps Engineer, this course focuses on the practical deployment knowledge every Data Engineer should have.

What You'll Learn

  • Real-World Deployment Architecture: Understand how Development, System Integration (SI/QA), and Production environments work in enterprise AWS environments.
  • AWS CloudFormation & Infrastructure as Code: Learn how AWS infrastructure can be represented using CloudFormation templates and how to generate these templates using Kiro AI.
  • Git & Branching Strategies: Understand how feature, develop, and master branches support controlled code deployment across environments.
  • CI/CD with GitHub Actions: Explore how DevOps engineers create automated deployment workflows and how GitHub Actions integrates with AWS.
  • Understanding the Three Layers of Code: Learn the differences between Data Engineering application scripts, CloudFormation infrastructure templates, and CI/CD workflow files.
  • Production Deployment Processes: Understand pull requests, approvals, testing, QA sign-offs, ServiceNow change requests, and production release workflows.
  • Data Engineering Interview Preparation: Learn how to explain the complete deployment process confidently when interviewers ask, "How does your code get deployed to production?"

A Practical, Real-World Approach

Throughout this course, you'll connect concepts to actual AWS Data Engineering pipelines involving services such as AWS Glue, Lambda, Step Functions, S3, and SNS.

You'll understand how these resources are developed, represented as Infrastructure as Code, and deployed through automated workflows.

You'll also gain exposure to the responsibilities of Data Engineers, DevOps engineers, and cloud infrastructure teams throughout the deployment lifecycle.

Who Is This Course For?

  • Data Engineers working with AWS who want to understand real-world deployment processes.
  • Aspiring AWS Data Engineers preparing for technical interviews.
  • ETL Developers transitioning into modern cloud Data Engineering roles.
  • Experienced Data Engineers who want to strengthen their understanding of Git, CloudFormation, and CI/CD workflows.

Prerequisite

This course is designed to be completed alongside Real World Git Essentials. Follow the recommended learning sequence explained in the introduction to build a complete understanding of Git and deployment workflows.

By the End of This Course

You'll have a clear understanding of how Data Engineering applications move from development to production, how the different teams collaborate, and how automated CI/CD workflows support enterprise deployments.

Most importantly, you'll be better prepared to explain the real-world deployment lifecycle with clarity and confidence in your next Data Engineering interview.

Because knowing how to build a Data Engineering pipeline is important. Understanding how that pipeline reaches production is what completes the picture.

Course Curriculum

Sachin Chandrashekhar

Lead Data Engineer @ World's #1 Airline

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