Book Image

Data Engineering with Google Cloud Platform

By : Adi Wijaya
3 (1)
Book Image

Data Engineering with Google Cloud Platform

3 (1)
By: Adi Wijaya

Overview of this book

With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.
Table of Contents (17 chapters)
Section 1: Getting Started with Data Engineering with GCP
Section 2: Building Solutions with GCP Components
Section 3: Key Strategies for Architecting Top-Notch Data Pipelines

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.