Cloud Computing for Data Engineering

Cloud Computing for Data Engineering: A Practical Guide to Building Your First Data Pipelines on AWS, GCP, and Azure

Building data pipelines no longer requires heavy servers, long lead times, and complex on-premise setups. Today, cloud platforms make it possible to design scalable, flexible, and cost-efficient pipelines with just a few well-chosen services. But where should you begin if you are new to data engineering in the cloud?

This book is a hands-on, step-by-step guide for anyone looking to move from theory into practice. It is written for aspiring data engineers, analysts transitioning into engineering roles, and developers eager to understand how data flows across the cloud. Whether you are handling batch jobs, streaming analytics, or multi-cloud systems, this book equips you with the knowledge and skills to confidently design and deploy pipelines that deliver value.

What makes this book different is its practical structure and comparative approach. Instead of focusing on a single provider, it walks you through the essentials of all three major cloud platforms-AWS, Google Cloud, and Azure-highlighting their similarities, differences, and strengths. Through clear explanations and real code examples, you will learn:

Foundations of Cloud Data Engineering: Understand pipelines, scalability, and architectures across providers.

Core Services Across the Big Three: Explore S3, GCS, and Azure Data Lake for storage, alongside compute engines and cloud warehouses.

Building Your First Pipelines: Step-by-step projects on AWS (S3, Kinesis, Glue, Redshift), GCP (Pub/Sub, Dataflow, BigQuery), and Azure (Data Factory, Databricks, Synapse).

Orchestration and Real-Time Processing: Learn Airflow, Step Functions, Composer, Kafka, and event-driven pipelines.

Security, Governance, and Cost Optimization: Apply IAM, encryption, compliance, and budgeting best practices.

Advanced Scenarios: Design multi-cloud pipelines, handle interoperability with federated queries, and prepare for interviews with real-world questions.

Every chapter blends explanations with actionable insights, ensuring you not only understand the tools but also how to apply them in production. By the end, you will have built pipelines that span ingestion, transformation, storage, and analytics-on multiple platforms.

If you want to move from experimenting with scripts to confidently designing production-ready pipelines, this book is your guide. Gain the practical skills to start your career or sharpen your expertise in cloud data engineering. Get your copy today and start building pipelines that scale with your ambitions.