Preface

Cloud computing came into our world in 2006, about 60 years after the first computer emerged. Cloud computing provides a brand-new concept of computing power services such as elastic, self-provisioning, and on-demand. In a traditional computing data center model, computing infrastructure is conceived as physical hardware with space, compute and network equipment, admin staff, physical security, and capital expenditure – entailing a long procurement cycle, big maintenance costs, and a lumbering structure. The new cloud computing model builds the computing infrastructure as software that matches your business needs: provisioning and terminating computing resources on-demand, scaling the computing resources up and down elastically and automatically, deploying the cloud resources as immutable code with version control, and paying for what you use.

Amazon Web Services (AWS) was the first cloud service, followed by Microsoft Azure and Google Cloud Platform (GCP). These are the three main clouds that are dominating the world, and this book helps you to learn about and master all of them and build a successful career in cloud computing.

Who this book is for

The book is for individuals in the information technology domain, whether you are a beginner looking to start your cloud computing journey or an experienced professional seeking to expand your skills. Our interactive study book is designed to empower you with the knowledge and practical experience necessary to excel in the world of cloud computing. With the detailed roadmap in the book, you will be able to complete a comprehensive cloud learning journey and develop a successful cloud computing career thereafter.

What this book covers

Chapter 1, Amazon EC2 and Compute Services, introduces AWS cloud compute services including EC2, among others.

Chapter 2, Amazon Cloud Storage Services, delves into AWS cloud storage services including EBS, EFS, S3, and so on.

Chapter 3, Amazon Cloud Networking Services, discusses AWS cloud networking services, including VPC, Amazon Direct Connect, Amazon Domain Name Service (DNS), and Content Delivery Network (CDN).

Chapter 4, Amazon Cloud Database Services, covers relational databases, NoSQL databases, and data warehouses in the AWS cloud.

Chapter 5, Amazon Cloud Big Data Services, explores AWS big data services for data ingestion, storing, processing, and visualization in the Amazon cloud.

Chapter 6, Amazon Cloud Machine Learning Services, examines AWS cloud machine learning (ML) services, including SageMaker and AWS ML API services.

Chapter 7, Amazon Cloud Security Services, addresses AWS cloud security services for hardening the Amazon cloud environment.

Chapter 8, Google Cloud Foundation Services, covers Google Compute Engine (GCE), Persistent Disks (PDs), network storage (Filestore), Google Cloud Storage (GCS), Google VPC, and VPC peering.

Chapter 9, Google Cloud Data Services, covers GCP data services such as Cloud SQL, Firestore, Datastore, and Bigtable; and GCP big data services, including BigQuery, Pub/Sub, Dataproc, Dataflow, and so on.

Chapter 10, Google Cloud AI Services, examines GCP ML services, focusing on GCP Vertex AI and AI APIs.

Chapter 11, Google Cloud Security Services, discusses GCP security services including endpoint security, network security, data security, and Security Command Center (SCC), which is the focal point of this chapter.

Chapter 12, Azure Cloud Foundation Services, explores the concepts of Azure cloud virtual machines and disk storage, file storage, Binary Large Object (BLOB) storage, queue storage, table storage, Azure vNets, and peering.

Chapter 13, Azure Cloud Data Services, covers Azure cloud-managed database services such as relational databases (Azure SQL Database), NoSQL databases (Azure Cosmos DB), and cache databases (Azure Cache for Redis), and discusses Azure big data services including Azure Data Factory, Azure Databricks, and Azure HDInsight.

Chapter 14, Azure Cloud AI Services, discusses Azure Machine Learning workspaces and Azure Cognitive Services, including the Azure OpenAI service.

Chapter 15, Azure Cloud Security Services, covers Microsoft Azure cloud security, including Azure security best practices, the Azure cloud security reference architecture, and an Azure security case study of a real-life project.

Chapter 16, Achieving Cloud Certification, reviews cloud certification roadmaps for AWS, Azure, and GCP, develops cloud exam strategies, and analyzes practice questions for seven cloud certification exams.

Chapter 17, Building a Successful Cloud Computing Career, discusses the cloud job market and the soft skills in a cloud career, and I share my own cloud story.

To get the most out of this book

To get the most out of this book, study the chapters to master the basic concepts, learn by doing all the lab examples in the chapters, study the certification exam contents, and go on to achieve cloud certifications.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Self-Taught-Cloud-computing-Engineer.

If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Using putty to connect to a Linux instance, install mysql client pkg and connect to the RDS endpoint.”

A block of code is set as follows:

Create database school;
Create table school.students (
    StudentID int primary key,
    LastName varchar(100),
    FirstName varchar(100),
    City varchar(100)  );

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

Create database school;
Create table school.students (
    StudentID int primary key,
    LastName varchar(100),
    FirstName varchar(100),
    City varchar(100)  );

Any command-line input or output is written as follows:

gcloud functions deploy image_checking  --trigger-resource  z04092023-upload --trigger-event google.storage.object.finalize --runtime python37

Bold: Indicates a new term, an important word, or words that you see on screen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Click Explore table items.”

Tips or important notes

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Get in touch

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