Data Engineering with Google Cloud Platform: A Guide to Leveling Up as a Data Engineer by Building a Scalable Data Platform with Google Cloud, Second Edition

Building a data platform on Google Cloud means connecting many moving parts: storage, processing, orchestration, governance, and the systems that turn data into useful outputs. Adi Wijaya’s second edition approaches that work step by step, moving from data-engineering fundamentals into hands-on use of Google Cloud services.

The book pairs explanations with practical scenarios and exercises, making it useful for readers who want to understand not only what services such as BigQuery, Dataproc, and Dataflow do, but how they contribute to a broader data workflow.

From data-engineering basics to Google Cloud systems

The opening chapters introduce the data lifecycle, data warehouses, the responsibilities of a data engineer, and the differences between ETL and ELT. From there, readers get an orientation to Google Cloud’s data-related capabilities before working through concrete platform components.

Build the platform one piece at a time

• BigQuery: Explore data-warehouse concepts and develop practical examples.
• Cloud Composer: Work with Airflow-based orchestration for batch data loading and pipeline dependencies.
• Dataproc: Examine data lakes, Hadoop, Spark, and data-processing workflows.
• Pub/Sub and Dataflow: Learn about messaging, ingestion, and streaming-data processing.

Later chapters extend the picture to visualization with Looker Studio, machine learning with Vertex AI, user and project management, cost strategy, and CI/CD. The second edition also covers data governance on Google Cloud, including services such as Dataplex and Dataform.

Practical learning, not just service names

Exercises and worked examples give readers opportunities to follow the concepts through implementation. The result is a structured introduction to how Google Cloud tools can work together across data storage, transformation, orchestration, and delivery.

Who may find it useful?

This book is relevant to aspiring data engineers, data professionals developing Google Cloud skills, and practitioners who want a guided overview of building data platforms on GCP. Its progression from foundational concepts to platform-specific topics gives readers a route into the subject while keeping the focus on applied data-engineering work.