You're reading from Data Engineering with Google Cloud Platform A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781835080115

Length 476 pages

Edition 2nd Edition

Languages

SQL

Tools

Google Cloud Platform

Concepts

Data Engineering

Author (1):

Adi Wijaya

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1: Getting Started with Data Engineering with GCP FREE CHAPTER

2. Chapter 1: Fundamentals of Data Engineering

3. Chapter 2: Big Data Capabilities on GCP

4. Part 2: Build Solutions with GCP Components

5. Chapter 3: Building a Data Warehouse in BigQuery

6. Chapter 4: Building Workflows for Batch Data Loading Using Cloud Composer

7. Chapter 5: Building a Data Lake Using Dataproc

8. Chapter 6: Processing Streaming Data with Pub/Sub and Dataflow

9. Chapter 7: Visualizing Data to Make Data-Driven Decisions with Looker Studio

10. Chapter 8: Building Machine Learning Solutions on GCP

11. Part 3: Key Strategies for Architecting Top-Notch Solutions

12. Chapter 9: User and Project Management in GCP

13. Chapter 10: Data Governance in GCP

14. Chapter 11: Cost Strategy in GCP

15. Chapter 12: CI/CD on GCP for Data Engineers

16. Chapter 13: Boosting Your Confidence as a Data Engineer

17. Index

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Technical requirements

Before we begin this chapter, you must have a few prerequisites ready.

In this chapter’s exercises, we will use Dataflow, Pub/Sub, Google Cloud Storage (GCS), and BigQuery. If you’ve never opened any of these services in your GCP console, you can open them now and enable their application programming interfaces (APIs).

Also, make sure you have your GCP console, Cloud Shell, and Cloud Shell Editor ready.

You can download the example code and the dataset from here: https://github.com/PacktPublishing/Data-Engineering-with-Google-Cloud-Platform-Second-Edition/tree/main/chapter-6.

Be aware of the cost that might arise from Dataflow streaming. Make sure you delete all the environments after executing the exercises in this chapter to prevent unexpected charges.

This chapter will use the same data from Chapter 5, Building a Data Lake Using Dataproc. You can choose to use the same data or prepare new data from the preceding GitHub repository...