Book Image

Professional Cloud Architect Google Cloud Certification Guide - Second Edition

By : Konrad Cłapa, Brian Gerrard
5 (1)
Book Image

Professional Cloud Architect Google Cloud Certification Guide - Second Edition

5 (1)
By: Konrad Cłapa, Brian Gerrard

Overview of this book

Google Cloud Platform (GCP) is one of the industry leaders thanks to its array of services that can be leveraged by organizations to bring the best out of their infrastructure. This book is a comprehensive guide for learning methods to effectively utilize GCP services and help you become acquainted with the topics required to pass Google's Professional Cloud Architect certification exam. Following the Professional Cloud Architect's official exam syllabus, you'll first be introduced to the GCP. The book then covers the core services that GCP offers, such as computing and storage, and takes you through effective methods of scaling and automating your cloud infrastructure. As you progress through the chapters, you'll get to grips with containers and services and discover best practices related to the design and process. This revised second edition features new topics such as Cloud Run, Anthos, Data Fusion, Composer, and Data Catalog. By the end of this book, you'll have gained the knowledge required to take and pass the Google Cloud Certification – Professional Cloud Architect exam and become an expert in GCP services.
Table of Contents (25 chapters)
1
Section 1: Introduction to GCP
5
Section 2: Manage, Design, and Plan a Cloud Solution Architecture
14
Chapter 12: Exploring Storage and Database Options in GCP – Part 2
17
Section 3: Secure, Manage and Monitor a Google Cloud Solution
21
Section 4: Exam Focus

Summary

In this chapter, we covered the main aspects of big data relating to the exam. We covered each service and showed that these can be used at different stages of our end-to-end solution. We took the time to see how we can configure Pub/Sub, Dataflow, and BigQuery from the GCP console and discussed Dataproc and Cloud IoT Core.

Exam Tip

The key takeaway from this chapter is to understand which services map to the ingest, process, and analysis stages of data.

Then, we looked at the processing stage of our solution. Cloud Dataflow will deploy Google Compute Engine instances to deploy and execute our Apache Beam pipeline, which will process data from Pub/Sub and pass it onto further stages for analysis or storage. We have shown how we can easily create a pipeline in the GCP console, which pulls information from Pub/Sub for analysis in BigQuery.

After, we covered BigQuery and understood that it is a data warehouse. It is designed to make data analysts more productive, crunching...