What this book covers
This book is divided into 3 parts and 13 chapters. Each part is a collection of independent chapters that have one objective.
Chapter 1, Fundamentals of Data Engineering, explains the role of data engineers and how data engineering relates to GCP.
Chapter 2, Big Data Capabilities on GCP, introduces the relevant GCP services related to data engineering.
Chapter 3, Building a Data Warehouse in BigQuery, covers the data warehouse concepts using BigQuery.
Chapter 4, Building Workflows for Batch Data Loading Using Cloud Composer, explains data orchestration using Cloud Composer.
Chapter 5, Building a Data Lake Using Dataproc, details the data lake concept with Hadoop using Dataproc.
Chapter 6, Processing Streaming Data with Pub/Sub and Dataflow, explains the concept of streaming data using Pub/Sub and Dataflow.
Chapter 7, Visualizing Data to Make Data-Driven Decisions with Looker Studio, covers how to utilize data from BigQuery to visualize it as charts in Looker Studio.
Chapter 8, Building Machine Learning Solutions on GCP, sets out the concepts of MLOps using Vertex AI.
Chapter 9, User and Project Management in GCP, explains the fundamentals of GCP identity and access management as well as GCP project structures.
Chapter 10, Data Governance in GCP, explains the concept of data governance and how to utilize Dataplex and Dataform to implement some of the foundations.
Chapter 11, Cost Strategy in GCP, covers how to estimate an overall data solution using GCP.
Chapter 12, CI/CD on GCP for Data Engineers, explains the concept of CI/CD and its relevance to data engineers.
Chapter 13, Boosting Your Confidence as a Data Engineer, prepares you for the GCP certification and offers some final thoughts in terms of summarizing what’s been learned in this book.
