Data Engineering with Medallion Architecture: Building scalable multi-cloud pipelines with auditable governance and automated DevOps (English Edition)

Data engineering fuels the AI revolution by transforming raw information into high-quality insights. This guide navigates the evolution from traditional warehousing to modern lakehouse systems, teaching you to build and safely operate the medallion architecture (bronze, silver, and gold layers) in production.

This book explores the evolution from data warehousing to the rise of data mesh and lakehouse patterns. You will master medallion architecture and data vault for auditable and ROI-driven integration with AWS Step Functions and multi-cloud design across AWS, Azure, and GCP using Kafka, dbt, and Terraform, while implementing the Four-Gate Governance Model for secure operations. You will also implement critical MLOps workflows using AWS SageMaker and DevOps practices with GitHub Actions. The book concludes with expert migration protocols, Z-ordering optimization, and observability techniques to ensure your data platform remains high-performing and cost-effective.

By the end of the book, you will confidently design and operate medallion architecture across cloud environments and implement governance frameworks that satisfy auditors. You will be a competent AI collaboration architect ready to orchestrate complex data lifecycles in the BFSI, healthcare, or retail sectors. You will possess the practical skills to deploy serverless streaming pipelines and maintain rigorous compliance.

What you will learn

● Design medallion architecture with bronze, silver, and gold layers.

● Create audit trails that answer auditors in one click.

● Build scalable pipelines with Kafka, dbt, and Terraform.

● Deploy AI/ML models through the same governance gates.

● Migrate to the cloud without disrupting live operations.

● Implement data mesh and lakehouse patterns at scale.

● Reduce firefighting and increase deployment confidence.

Who this book is for

The book is designed for data engineers, architects, and AI specialists. This book requires proficiency in SQL, Python, and cloud platforms like AWS. It targets professionals experienced in building systems who seek advanced mastery in production-grade medallion architectures and resilient, automated data pipelines.

Table of Contents

Reading Guide

1. Evolution of Data Architecture

2. Understanding Data Mesh, Lakehouse, and Medallion

3. Data Integration Strategy, Business Impact, and ROI

4. Medallion Architecture in Multi-cloud

5. Building Scalable Data Pipelines

6. Data Governance and Compliance

7. MLOps for AI Model Deployment and Monitoring

8. DevOps and CI/CD for Data Engineering

9. Cloud Migration and Coexistence Strategies

10. Scaling Data Platforms with Optimization