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

Cloud Dataflow

Cloud Dataflow is a service based on Apache Beam, which is an open source software for creating data processing pipelines. A pipeline is essentially a piece of code that determines how we wish to process our data. Once these pipelines have been constructed and input into the service, they become a Dataflow job. This is where we can process the data that's been ingested by Pub/Sub. It will perform steps to change our data from one format to another and can transform both real-time streams or historical batch data. Dataflow is completely serverless and fully managed. It will spin up the necessary resources to execute our Dataflow job and then delete these resources when the job is complete. As an example, a pipeline job might be made up of several steps. If a specific step needs to be executed on 15 machines in parallel, then Dataflow will automatically scale to these 15 machines and remove them when the job is complete. These resources are based on Compute Engine...