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

Going through big data services

Big data services enable the user to process large amounts of data to provide answers to complex problems. GCP offers many services that tightly integrate to create an End-to-End (E2E) data analysis pipeline. These services are as follows:

  • BigQuery: BigQuery is a highly scalable and fully managed cloud data warehouse. It allows users to perform analytics operations with built-in ML. BigQuery is completely serverless and can host petabytes of data. The underlying infrastructure scales seamlessly and allows parallel data processing. The data can be stored in BigQuery Storage, Cloud Storage, Bigtable, Sheets, or Google Drive. The user defines datasets containing tables. BigQuery uses familiar ANSI-compliant SQL for queries and provides ODBC and JDBC drivers. Users can choose from two types of payment models—one is flexible and involves paying for storage and queries, and the other involves a flat rate with stable monthly costs. It is ideal...