Book Image

Professional Cloud Architect – Google Cloud Certification Guide

By : Konrad Cłapa, Brian Gerrard
Book Image

Professional Cloud Architect – Google Cloud Certification Guide

By: Konrad Cłapa, Brian Gerrard

Overview of this book

Google Cloud Platform (GCP) is one of the leading cloud service suites and offers solutions for storage, analytics, big data, machine learning, and application development. It features an array of services that can help organizations to get the best out of their infrastructure. This comprehensive guide covers a variety of topics specific to Google's Professional Cloud Architect official exam syllabus and guides you in using the right methods for effective use of GCP services. You'll start by exploring GCP, understanding the benefits of becoming a certified architect, and learning how to register for the exam. You'll then delve into the core services that GCP offers such as computing, storage, and security. As you advance, this GCP book will help you get up to speed with methods to scale and automate your cloud infrastructure and delve into containers and services. In the concluding chapters, you'll discover security best practices and even gain insights into designing applications with GCP services and monitoring your infrastructure as a GCP architect. By the end of this book, you will be well versed in all the topics required to pass Google's Professional Cloud Architect exam and use GCP services effectively.
Table of Contents (26 chapters)
Free Chapter
1
Section 1: Introduction to GCP
5
Section 2: Managing, Designing, and Planning a Cloud Solution Architecture
15
Section 3: Designing for Security and Compliance
17
Section 4: Managing Implementation
19
Section 5: Ensuring Solution and Operations Reliability
21
Section 6: Exam Focus

Dataproc

Dataproc is GCP's big data-managed service for running Hadoop and Spark clusters. Hadoop and Spark are open source frameworks that handle data processing for big data applications in a distributed manner. Essentially, they provide massive storage for data, whilst also providing enormous processing power to handle concurrent processing tasks.

If we refer back to the End-to-end big data solution section of this chapter, Dataproc is also part of the processing stage. It can be compared to Dataflow; however, Dataproc requires us to provision servers, whereas Dataflow is serverless.

Exam tip: Dataproc should be chosen over Dataflow if we have an existing Hadoop or Spark Cluster. Also, skill sets of existing resources are needed. If we need to create new pipeline jobs or to process streaming data, then we should select Dataflow.

As an alternative to hosting these services...