What this book covers
This book is divided into 3 sections and 12 chapters. Each section 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 concept using BigQuery.
Chapter 4, Building Orchestration 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 for Making Data-Driven Decisions with Data Studio, covers how to use data from BigQuery to visualize it as charts in Data Studio.
Chapter 8, Building Machine Learning Solutions on Google Cloud Platform, sets out the concept of MLOps using Vertex AI.
Chapter 9, G User and Project Management in GCP, explains the fundamentals of GCP Identity and Access Management and project structures.
Chapter 10, Cost Strategy in GCP, covers how to estimate the overall data solution using GCP.
Chapter 11, CI/CD on Google Cloud Platform for Data Engineers, explains the concept of CI/CD and its relevance to data engineers.
Chapter 12, 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.
