AWS for Solutions Architects E-Book

Saurabh Shrivastava

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 995

Veröffentlichungsjahr: 2023

Beschreibung

The second edition of AWS for Solutions Architects provides a practical guide to designing cloud solutions that align with industry best practices. This updated edition covers the AWS Well-Architected Framework, core design principles, and cloud-native patterns to help you build secure, high-performance, and cost-effective architectures.
Gain a deep understanding of AWS networking, hybrid cloud connectivity, and edge deployments. Explore big data processing with EMR, Glue, Kinesis, and MSK, enabling you to extract valuable insights from data efficiently. New chapters introduce CloudOps, machine learning, IoT, and blockchain, equipping you with the knowledge to develop modern cloud solutions.
Learn how to optimize AWS storage, implement containerization strategies, and design scalable data lakes. Whether working on simple configurations or complex enterprise architectures, this guide provides the expertise needed to solve real-world cloud challenges and build reliable, high-performing AWS solutions.

Leseprobe

AWS for Solutions Architects

Copyright © 2023 Packt Publishing

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Early Access Publication: AWS for Solutions Architects

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Table of Contents

AWS for Solutions Architects, Second Edition: Design your cloud infrastructure by implementing DevOps, containers, and Amazon Web Services

1 Understanding AWS Principles and Key Characteristics

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What is cloud computing?

Private versus public clouds

What is AWS (Amazon Web Services)?

The market share, influence, and adoption of AWS

Basic cloud and AWS terminology

Why is AWS so popular?

Elasticity

Security

Availability

Faster hardware cycles

System administration staff

Summary

2 Understanding AWS Well-Architected Framework and Getting Certified

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The AWS Well-Architected Framework

The six pillars of the Well-Architected Framework

The first pillar – security

The second pillar – reliability

The third pillar – performance efficiency

The fourth pillar – cost optimization

The fifth pillar – operational excellence

The sixth pillar – sustainability

AWS Well-Architected lenses

Building credibility and getting certified

Building a non-tech AWS cloud career

AWS Certified Cloud Practitioner – Foundational

AWS Solutions Architect path

AWS Certified Solutions Architect – Associate

AWS Certified Solutions Architect – Professional

AWS Cloud DevOps Engineer path

AWS Certified SysOps Administrator – Associate

AWS Certified DevOps Engineer – Professional

AWS Cloud Developer path

AWS Certified Developer – Associate

AWS Specialty Solutions Architect path

AWS Certified Advanced Networking – Specialty

AWS Certified Security – Specialty

AWS Certified Machine Learning – Specialty

AWS Certified Database – Specialty

AWS Certified Data Analytics – Specialty

AWS Certified SAP – Specialty

Learning tips and tricks to obtain AWS certifications

Focus on one cloud provider

The best way to get certified

Getting started in AWS

Focus on the Associate-level certifications

Get experience wherever you can

Some frequently asked questions about the AWS certifications

Certification preparation approach

How long will it take to get certified?

How to request additional exam time

What are some last-minute tips for the day of the exam?

Summary

3 Leveraging the Cloud for Digital Transformation

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A brief introduction to cloud computing

Cloud computing models

Understanding IaaS

Advantages of IaaS

Disadvantages of IaaS

Use cases for IaaS

Examples of AWS IaaS services

Understanding PaaS

Advantages of PaaS

Disadvantages of PaaS

PaaS use cases

Examples of AWS PaaS services

Understanding SaaS

Characteristics of SaaS

Advantages of SaaS

Disadvantages of SaaS

Examples of AWS SaaS solutions

Examples of third-party SaaS solutions

Selection by use case of SaaS, PaaS, or IaaS

Cloud Migration Strategy

The three-phase migration process

Cloud migration patterns - The 7 R’s

Rehost in the cloud

Re-platform in the cloud

Refactor in the cloud

Revise before migrating to the cloud

Repurchase in the cloud

Relocate to the cloud

Retain in on-premise

Retire

Migration assessment tools

Implementing a digital transformation program

What exactly is a digital transformation?

Digital transformation drivers

Digital transformation examples

Digital transformation tips

Digital transformation pitfalls

AWS Cloud Adoption Framework (AWS CAF)

Summary

4 Networking in AWS

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Learning about the AWS Global Infrastructure

Regions, Availability Zones, and Local Zones

AWS Networking Foundation

AWS Network security with third-party solutions

Summary

5 Storage in AWS – Choosing the Right Tool for the Job

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Understanding Amazon Elastic Block Storage

General-purpose Solid-State Devices (SSDs)

Provisioned IOPS SSD

Throughput Optimized HDD

Cold HDD

Amazon EBS Snapshots

Investigating Amazon Elastic File System (EFS)

Building file system-specific workload with Amazon FSx

Learning about Amazon Simple Storage Service (S3)

S3 Standard

Amazon S3 Intelligent-Tiering

Amazon S3 Standard-IA

Amazon S3 One Zone-IA

Amazon S3 Glacier

Amazon S3 Glacier Deep Archive

Managing data with S3 Object Lambda

Amazon S3 Multi-destination Replication

Understanding the difference between block storage and object storage

Versioning in Amazon S3

Exploring Amazon S3 best practices

Enhancing Amazon S3 performance

Protecting your data in Amazon S3

Build hybrid storage with AWS Storage Gateway

Amazon S3 File Gateway

Amazon FSx File Gateway

Tape Gateway

Volume Gateway

AWS Backup

Summary

6 Harnessing the Power of Cloud Computing

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Compute in AWS

Learning about Amazon EC2

AWS Graviton

EC2 instance families

Advantage of EC2

EC2 pricing model

AWS Compute Optimizer

Amazon Machine Images (AMI)

Reviewing Amazon EC2 best practices

Access

Storage

Resource management

Limit management

Backup, snapshots, and recovery

Serverless compute

AWS Lambda

AWS Fargate

High-Performance Computing

Hybrid compute

AWS Outposts

Summary

7 Selecting the Right Database Service

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A brief history of databases

Data-Driven Innovation trends

Database consistency model

Database usages model

AWS database services

Relational databases

Advantage of DocumentDB

In-Memory Database

Graph databases

Time-series databases

Ledger databases

Wide-column store databases

Amazon Keyspaces (for Apache Cassandra)

Benefits of AWS database services

Moving to fully managed database services

Building modern applications with purpose-built databases

Moving off from legacy databases

Choosing the right tool for the job

Migrating database to AWS

Summary

8 Best Practices for Application Security, Identity, and Compliance

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Understanding the importance of security, identity, and compliance on AWS

Understanding the shared responsibility model

AWS security, identity, and compliance solutions

Getting familiar with Identity and Access Management

AWS Identity and Access Management (IAM)

Managing resources, permissions, and identities using IAM

IAM Users

IAM User Groups

IAM Roles

Policies and Permissions

Policy evaluation

AWS Directory Service

AWS IAM Identity Center (successor to AWS SSO)

AWS Organizations

AWS Control Tower

AWS Resource Access Manager

Amazon Cognito

Applying Detective controls

Amazon GuardDuty

Amazon Inspector

AWS Security Hub

Building Infrastructure Protection

AWS Web Application Firewall (WAF)

AWS Firewall Manager

AWS Shield

Creating Data protection

Amazon Macie

AWS Key Management Service

AWS CloudHSM

AWS Certificate Manager

AWS Secrets Manager

Responding to incident

Amazon Detective

Adhering compliances

AWS Artifact Reports

Best Practices for AWS Security

Summary

9 Dive efficiency with Cloud Operation Automation and DevOps in AWS

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What is the cloud operation (CloudOps) model and the role of automation?

CloudOps pillars

First Pillar - Building the foundation for cloud governance.

Second Pillar - Managing Configuration, Compliance, and Audit

Third Pillar - Provisioning & orchestration

Fourth Pillar - Monitor & observe your applications

Fifth Pillar - Centralized Operations Management

Sixth Pillar - Manage your cloud financial

DevOps in AWS

DevOps Best Practices

AWS CodeCommit

Integrate third-party Git source repositories in AWS CodePipeline

DevSecOps in AWS

Cloud Automation best practice

Chef and Puppet Configuration Management with AWS OpsWorks

Configuration Management with AWS Systems Manager

Control Infrastructure with AWS Service Catalog

Policy as Code with AWS Config

Best practice for cloud monitoring and Alert

Summary

10 Bigdata and streaming data processing in AWS

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Why cloud for Big Data Analytics?

Amazon Elastic Map Reduce (EMR)

Understanding EMR Cluster and Nodes

Understanding EMR file system

Amazon EMR Studio

Securing data in Amazon EMR

Why is AWS Glue a cornerstone service?

Introduction to AWS Glue

Operating the AWS Glue console

Cataloging with AWS Glue Data Catalog

Crawling with AWS Glue crawlers

Categorizing with AWS Glue classifiers

Generating code with AWS Glue code generators

AWS Glue serverless streaming ETL

Putting it all together

AWS Glue best practices

Choosing the right worker type

Optimizing file splitting

Exceeding Yarn's memory overhead allocation

Leveraging the Apache Spark UI

Processing many small files

Data partitioning and predicate pushdown

Partitioning data while writing to Amazon S3

Choosing between AWS Glue and Amazon EMR

Handling streaming data in AWS

Streaming data processing with Amazon Kinesis

Amazon Managed Streaming for Apache Kafka (MSK)

Amazon MSK Cluster Architecture

Data Cataloging for streaming data using AWS Glue Schema Registry (GSR)

Choosing between Amazon Kinesis and Amazon MSK

Summary

11 Datawarehouse, Data Query and Visualization in AWS

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Datawarehouse in AWS with Amazon Redshift

Amazon Redshift architecture

Redshift instance types

Querying your data lake in AWS with Amazon Athena

Deep diving into Amazon Athena

CSV files

JSON files

ORC files

Apache Avro files

Apache Parquet files

Understanding how Amazon Athena works

Using Amazon Athena Federated Query

Data source connectors

Learning about Amazon Athena workgroups

Optimizing Amazon Athena

Optimization of data partitions

Data bucketing

File compression

File size optimization

Columnar data store generation optimization

Column selection

Predicate pushdown

ORDER BY clause optimization

Join optimization

group by clause optimization

Approximate function use

Visualizing data with Amazon QuickSight

Putting AWS analytics services together

Summary

12 Machine Learning, IoT, and Blockchain in AWS

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What is AI/ML?

AI/ML in AWS

AWS ML infrastructure and framework

Amazon Sagemaker

ML Data Preparation

ML Model Building

ML Model Training and Tuning

ML model deployment and monitoring

AWS AI services

Vision

Speech

Language

Chatbots

Forecasting

Recommendations

Building ML best practices with MLOps

What is IoT?

Building IoT application in AWS

AWS IoT Core

AWS IoT device management:

AWS IoT Analytics

AWS IoT Greengrass

AWS IoT Device Defender

AWS IoT Things Graph:

AWS IoT Sitewide

AWS IoT TwinMaker

AWS Industrial IoT (IIoT)

Best Practices to build AWS IoT application

Blockchain in AWS

Quantum Computing with AWS Bracket

Summary

13 Containers in AWS

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Understanding containerization

Advantages of containers

Disadvantages of containers

Ecosystem inconsistencies

Graphical applications

Virtual machines and virtualization

Containers versus VMs

Learning about Docker

Docker components

Amazon Elastic Container Service (ECS)

Amazon ECS Architecture

ECS components

Learning about Kubernetes

Components of Kubernetes

Kubernetes advantages

Amazon Elastic Kubernetes Service (Amazon EKS)

Learning about AWS Fargate

Red Hat OpenShift Service on AWS (ROSA)

Choosing between AWS container services

Learning about AWS Batch

AWS Batch components

Features of AWS Batch

AWS Batch best practices

Summary

14 Microservice and Event-Driven Architectures

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Understanding microservices

Microservice architecture patterns

Layered architecture

Event-driven architecture

Event-driven architecture models

Benefits of event-driven architecture

No more polling

Disadvantages of Event-Driven Architecture

Event-driven architectures are not a silver bullet

When things go wrong

Learning about microkernel architecture

Microservices best practices

Best practice #1: Decide whether microservices are the right tool

Best practice #2: Clearly define the requirements and design of the microservice

Best practice #3: Leverage Domain-Driven Design (DDD) to create microservices

Best practice #4: Ensure buy-in from all stakeholders

Best practice #5: Leverage logging and tracing tools

Best practice #6: Think microservices first

Best practice #7: Minimize the number of languages and technologies

Best practice #8: Leverage RESTful APIs

Best practice #9: Implement microservice communication asynchronously

Best practice #10: Implement clear separation between microservice frontends and backends

Best practice #11: Organize your team around microservices

Best practice #12: Provision individual data stores for each individual microservice

Best practice #13: Self-documentation and full documentation

Best practice #14: Use a DevOps toolset

Best practice #15: Invest in monitoring

Best practice #16: Two pizzas should be enough to feed your team

Best practice #17: Twelve-factor design

Summary

15 Domain-Driven Design

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Domain-driven design

History of domain-driven design

Definition of a domain

Reasons to use domain-driven design.

Challenges with domain-driven design

Microservices

Microservices and serverless technologies

Monolithic architecture versus microservices architecture

Advantages of microservices

DDD and microservices

Communication between microservices

Microservices in AWS

Fundamental services for creating microservices in AWS

Microservice examples

Using microservices to transform media files

Text-to-speech microservice

Summary

16 Data Lake Patterns – Integrating Your Data across the Enterprise

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Definition of a data lake

What is a data lake?

Purpose of a data lake

Components of a data lake

Data Lake Zones

Landing or transient data zone

Raw data zone

Organized or trusted data zone

Curated or refined data zone

Sandboxes

Characteristics of a data lake

Data lake in AWS with Lake Formation

Data Mesh in AWS

Data lake best practices

Machine learning

Natural language processing and natural language understanding

Entity extraction

Security

The silo mentality

Data governance

Relevant metadata

Key metrics in a data lake

Metrics to gauge the success of your data lake

Google – the gold standard

Data and metadata curation

Web content homogeneity

Enhancing document relevance based on document relationships

Veracity and validity of data

Locating documents across the enterprise using faceted search

Security

Maturity

Summary

17 Availability, Reliability, and Scalability Patterns

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Availability in cloud computing

Reliability in cloud computing

Scalability in cloud computing

Architectures to provide high availability, reliability, and scalability

Active architecture

Active/passive architecture

Active/Active architecture

Sharding architecture

Recovery Point Objective (RPO) and Recovery Time Objective (RTO)

RTO

RPO

Recovery Point Actual and Recovery Time Actual

Chaos engineering

Scaling in and scaling out versus scaling up and scaling down

Scaling up or vertical scaling

Scaling out or horizontal scaling

Advantages of cloud scalability

Availability in AWS

Availability for Amazon EC2 instances

AWS high availability for Amazon RDS

AWS's high availability for storage services

Amazon Elastic Load Balancing

ELB rules

Host-based routing

Path-based routing

Elastic Load Balancer types

Classic Load Balancers

Application Load Balancers

Network Load Balancers

CLB versus ALB versus NLB comparison

CLB and ALB commonalities

The best choice of ELB by use case

Summary

18 AWS Hands-On Lab and Use Case

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An introduction to the use cases

Reviewing the list of available AWS programming languages

Interpretive versus compiled languages

Java

Python

C++

.NET

JavaScript

Node.js

Ruby

Go

PHP

Swagger

Deciding which is the best language

Understanding the serverless AWS microservice architecture

Setting up services

AWS Command Line Interface

AWS CloudFormation

AWS Cloud Development Kit

AWS CodeDeploy

AWS Config

AWS Elastic Beanstalk

AWS Service Catalog

Learning about front-end user interfaces (UX)

Authenticating, authorizing, and managing users

AWS Identity and Access Management

Grasping CDN concepts

Understanding the asynchronous communication service

Amazon EventBridge

Learning about file uploads

Uploading to an AWS service via the AWS console

Uploading with signed URLs

Uploading files using the File Transfer Protocol

Uploading to an AWS service via the CLI

Uploading files to AWS via the AWS SDK

Uploading files to AWS using REST

Uploading files using AWS Snowball

Covering the AWS API gateway

Reviewing business APIs

Business rules engine

Understanding state-machine services

Exploring backend persistence services

Summary

Cover

Table of contents

AWS for Solutions Architects, Second Edition: Design your cloud infrastructure by implementing DevOps, containers, and Amazon Web Services

Welcome to Packt Early Access. We’re giving you an exclusive preview of this book before it goes on sale. It can take many months to write a book, but our authors have cutting-edge information to share with you today. Early Access gives you an insight into the latest developments by making chapter drafts available. The chapters may be a little rough around the edges right now, but our authors will update them over time.

You can dip in and out of this book or follow along from start to finish; Early Access is designed to be flexible. We hope you enjoy getting to know more about the process of writing a Packt book.

Chapter 1: Understanding AWS Cloud Principles and Key Characteristics

Chapter 2: Understanding AWS Well Architected Framework and Getting Certified

Chapter 3: Leveraging the Cloud for Digital Transformation

Chapter 4: AWS Networking and Content Delivery

Chapter 5: Storage in AWS – Choosing the Right Tool for the Job

Chapter 6: Harnessing the Power of Cloud Computing

Chapter 7: Selecting the Right Database Service

Chapter 8: Best Practices for Application Security, Identity, and Compliance

Chapter 9: Drive Efficiency with Cloud Automation, Monitoring, and Alert

Chapter 10: AWS EMR and AWS Glue – Extracting, Transforming, and Loading Data

Chapter 11: Amazon Athena and Amazon QuickSight– Combining the Simplicity of Files with the Power of SQL and Data Visualization

Chapter 12: Machine Learning, IoT, and Blockchain in AWS

Chapter 13: Serverless and Container Patterns

Chapter 14: Microservice and Event-Driven Architectures

Chapter 15: Domain-Driven Design

Chapter 16: Data Lake Patterns – Integrating Your Data across the Enterprise

Chapter 17: Availability, Reliability, and Scalability Patterns

Chapter 18: Hands-On Lab and Use Case

1 Understanding AWS Principles and Key Characteristics

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https://packt.link/AWS4SAs

The last decade has revolutionized the IT infrastructure industry; cloud computing was introduced and now it is everywhere, from small start-ups to large enterprises. Nowadays, the cloud is the new normal. It all started with Amazon launching a cloud service called Amazon Web Services (AWS) in 2006 with a couple of services.

Netflix migrated to AWS in 2008 and became a market disrupter. After that, there was no looking back and there were many industry revolutions led by cloud-born start-ups like Airbnb in hospitality, Robinhood in finance, Lyft in transportation, and many more. The cloud rapidly gained the market share, and now big names like Capital One, JP Morgan Chase, Nasdaq, the NFL, and General Electric are all accelerating their digital journey with cloud adoption.

Even though the term cloud is pervasive today, not everyone understands what the cloud is as the cloud can be different things for different people, and it is continuously evolving. In this chapter, you will learn what the cloud is, and then what AWS is more specifically. You will learn about the vast and ever-growing influence and adoption of the cloud in general and of AWS in particular. After that, you will start getting introduced to some elementary cloud and AWS terms to get your feet wet with the lingo while gaining an understanding of why cloud computing is so popular. In this chapter, we will cover the following topics:

What is cloud computing?

What is Amazon Web Services (AWS)?

The market share, influence, and adoption of AWS

Basic cloud and AWS terminology

Why is AWS so popular?

Let's get started, shall we?

What is cloud computing?

At a high level, cloud computing is the on-demand availability of IT resources such as servers, storage, databases, and so on over the web, without the hassle of managing physical infrastructure. The best way to understand the cloud is to take the electricity supply analogy. To get light in your house, you just flip a switch on, and electric bulbs light up your home. In this case, you only pay for your electricity use when you need it; when you switch off electric appliances, you do not pay anything. Now, imagine if you needed to power a couple of appliances, and for that, you had to set up an entire powerhouse. It would be costly, right? It would involve the costs of maintaining the turbine and generator and building the whole infrastructure. Utility companies make your job easier by supplying electricity in the quantity you need. They maintain the entire infrastructure to generate electricity and they can keep costs down by distributing electricity to millions of houses, which helps them benefit from mass utilization. Here, the utility companies represent cloud providers such as AWS, and the electricity represents the IT infrastructure available in the cloud.

While consuming cloud resources, you pay for IT infrastructure such as computing, storage, databases, networking, software, machine learning, and analytics in a pay-as-you-go model. Here, public clouds like AWS do the heavy lifting to maintain IT infrastructure and provide you with on-demand access over the internet under a pay-as-you-go model. As you generally only pay for the time and services you use, most cloud providers can provide massive scalability, making it easy to scale services up and down. Where, traditionally, you would have to maintain your servers all by yourself on-premise to run your organization, now you can offload that to the public cloud and focus on your core business. For example, Capital One's core business is banking and it does not run a large data center.

As much as we tried to nail it down, this is still a pretty broad definition. For example, we specified that the cloud can offer software, that's a pretty general term. Does the term software in our definition include the following?

Video conferencing

Virtual desktops

Email services

Contact center

Document management

These are just a few examples of what may or may not be included as available services in a cloud environment. When AWS started, it only offered a few core services, such as compute (Amazon EC2) and basic storage (Amazon S3). AWS has continually expanded its services to support virtually any cloud workload. As of 2022, it has more than 200 fully featured services for computing, storage, databases, networking, analytics, machine learning, artificial intelligence, Internet of Things, mobile, security, hybrid, virtual and augmented reality, media, application development, and deployment. As a fun fact, as of 2021, Amazon Elastic Cloud Compute (EC2) alone offers over 475 types of compute instances.

For the individual examples given here, AWS offers the following:

Video conferencing  – Amazon Chime

Virtual desktops – AWS WorkSpaces

Email services – Amazon WorkMail

Contact Center – Amazon Connect

Document Management – Amazon WorkDocs

Not all cloud services are highly intertwined with their cloud ecosystems. Take these scenarios, for example:

Your firm may be using AWS services for many purposes, but they may be using WebEx, Microsoft Teams, Zoom, or Slack for their video conference needs instead of Amazon Chime. These services have little dependency on other underlying core infrastructure cloud services.

You may be using Amazon SageMaker for artificial intelligence and machine learning projects, but you may be using the TensorFlow package in Sagemaker as your development kernel, even though Google maintains TensorFlow.

If you are using Amazon RDS and choose MySQL as your database engine, you should not have too much trouble porting your data and schemas over to another cloud provider that supports MySQL if you decide to switch over.

However, it will be a lot more difficult to switch to some other services. Here are some examples:

Amazon DynamoDB is a NoSQL proprietary database only offered by AWS. If you want to switch to another NoSQL database, porting it may not be a simple exercise.

Suppose you are using CloudFormation to define and create your infrastructure. In that case, it will be difficult, if not impossible, to use your CloudFormation templates to create infrastructure in other cloud provider environments. Suppose the portability of your infrastructure scripts is important to you, and you are planning on switching cloud providers. In that case, using Ansible, Chef, or Puppet may be a better alternative.

Suppose you have a streaming data requirement and use Amazon Kinesis Data Streams. You may have difficulty porting out of Amazon Kinesis since the configuration and storing mechanism are quite dissimilar if you decide to use another streaming data service like Kafka.

As far as we have come in the last 15 years with cloud technologies, I think vendors realize that these are the beginning innings, and locking customers in right now while they are still deciding who their vendor should be will be a lot easier than trying to do so after they pick a competitor.

However, looking at a cloud-agnostic strategy has its pros and cons. You want to distribute your workload between cloud providers to have competitive pricing and keep your options open like in the old days. But each cloud has different networking needs, and connecting distributed workloads between clouds to communicate with each other is a complex task. Also, each major cloud provider, like AWS, Azure, and GCP, has a breadth of services, and building a workforce with all three skill sets is another challenge.

Finally, clouds like AWS provide economy of scale, which means the more you use, the more the price goes down, which may not benefit you if you choose multi-cloud. Again, it doesn't mean you cannot choose a multi-cloud strategy, but you have to think about logical workload isolation. It would not be wise to run the application layer in one cloud and the database layer in other, but you can think about logical isolation like running the analytics workload and application workload in a separate cloud.

In this section, you learned about cloud computing at a very high level. Let’s learn about the difference between the public and private clouds.

Private versus public clouds

A private cloud is a service dedicated to a single customer—it is like your on-premise data center, which is accessible to one large enterprise. A private cloud is a fancy name for a data center managed by a trusted third party. This concept gained momentum to ensure security as, initially, enterprises were skeptical about public cloud security, which is multi-tenant. However, having your own infrastructure in this manner diminishes the value of the cloud as you have to pay for resources even if you are not running them.

Let's use an analogy to understand the difference between private and public clouds further. The gig economy has great momentum. Everywhere you look, people are finding employment as contract workers. One of the reasons contract work is getting more popular is because it enables consumers to contract services that they may otherwise not be able to afford. Could you imagine how expensive it would be to have a private chauffeur? But with Uber or Lyft, you almost have a private chauffeur who can be at your beck and call within a few minutes of you summoning them.

A similar economy of scale happens with a public cloud. You can have access to infrastructure and services that would cost millions of dollars if you bought them on your own. Instead, you can access the same resources for a small fraction of the cost.

In general, private clouds are expensive to run and maintain in comparison to public clouds. For that reason, many of the resources and services offered by the major cloud providers are hosted in a shared tenancy model. In addition to that, you can run your workloads and applications on a public cloud securely: you can use security best practices and sleep well at night knowing that you use AWS’s state-of-the-art technologies to secure your sensitive data.

Additionally, most major cloud providers' clients use public cloud configurations. That said, there are a few exceptions even in this case. For example, the United States government intelligence agencies are a big AWS customer. As you can imagine, they have deep pockets and are not afraid to spend. In many cases with these government agencies, AWS will set up the AWS infrastructure and dedicate it to the government workload. For example, AWS launched Top Secret Region—AWS Top Secret-West accredited to operate workloads at the Top-Secret U.S. security classification level. The other AWS GovCloud regions are:

GovCloud (US-West) Region - Launched in 2011 Availability Zones: 3

GovCloud (US-East) Region - Launched in 2018 Availability Zones: 3

AWS GovCloud (US) consists of isolated AWS Regions designed to allow U.S. government agencies and customers to move sensitive workloads to AWS. It addresses specific regulatory and compliance requirements, including Federal Risk and Authorization Management Program (FedRAMP) High, Department of Defense Security Requirements Guide (DoD SRG) Impact Levels 4 and 5, and Criminal Justice Information Services (CJIS) to name a few.

Public cloud providers such as AWS provide you choices to adhere to compliance needs as required by government or industry regulations. For example, AWS offers Amazon EC2 dedicated instances, which are EC2 instances that ensure that you will be the only user for a given physical server. Further, AWS offers AWS Outpost, where you can order server racks and host workloads on-premise using the AWS control plane.

Dedicated instance and outpost costs are significantly higher than on-demand EC2 instances. On-demand instances are multi-tenant, which means the physical server is not dedicated to you and may be shared with other AWS users. However, just because the physical servers are multi-tenant doesn’t mean that anyone else can access your server as those will be dedicated virtual EC2 instances accessible to you only. As we will discuss later in this chapter, you will never know the difference when using EC2 instances if they are hosted on a dedicated physical server compared to a multi-tenant server because of virtualization and hypervisor technology. One common use case for choosing dedicated instances is government regulations and compliance policies that require certain sensitive data to not be in the same physical server with other cloud users.

Now that we have gained a better understanding of cloud computing in general, let's get more granular and learn about how AWS does cloud computing.

What is AWS (Amazon Web Services)?

Amazon Web Services (AWS) is the world’s most broadly adopted cloud platform, offering over 200 fully-featured services from data centers globally. Even though there are a few worthy competitors, it doesn't seem like anyone will push them off the podium for a while.

For example, it’s difficult to catch up with AWS’ pace of innovation. AWS services and features have grown exponentially every year: as shown in the following figure, in 2011, AWS released over 80 new significant services and features, followed by nearly 160 in 2012; 280 in 2013; 516 in 2014; 722 in 2015; 1,017 in 2016; 1,430 in 2017; and 1,957 in 2018; 2,345 in 2019, 2,757 in 2020, and 3,084 in 2021:

Figure 1.1 – AWS – number of features released per year

There is no doubt that the number of offerings will continue to grow at a similar rate for the foreseeable future. Gartner named AWS as a leader for the 11th consecutive year in the 2021 Gartner Magic Quadrant for Cloud Infrastructure & Platform Services. AWS is innovating fast, especially in new areas such as machine learning and artificial intelligence, the Internet of Things (IoT), serverless computing, blockchain, and even quantum computing.

The following are some of the key differentiators for AWS in a nutshell:

Oldest and most experienced cloud provider

AWS was the first major public cloud provider (started in 2006) and since then it has gained millions of customers across the globe.

Fast pace of innovation

AWS has 200+ fully featured services to support any cloud workload. They released 3000+ features in 2021 to meet customer demand.

Continuous price reduction

AWS has reduced its prices across various services 111 times since its inception in 2006 to improve the

Total Cost of Ownership

(

TCO

).

Community of partners to help accelerate the cloud journey

AWS has a large Partner Network of 100,000+ Partners across 150 + countries. These partners include large consulting partners and software vendors.

Security and compliance

AWS provides security standards and compliance certifications to fulfill your local government and industry compliance needs.

Global infrastructure

AWS has 84 Availability Zones within 26 geographic Regions, 17 Local Zones, 24 Wavelength Zones, 310+ Points of Presence (300+ Edge locations and 13 regional mid-tier caches) in 90+ cities across 47 countries.

It’s not always possible to move all workloads into the cloud, and for that purpose, AWS provides a broad set of hybrid capabilities in the areas of networking, data, access, management, and application services. For example, VMware Cloud on AWS allows customers to seamlessly run existing VMware workloads on AWS with the skills and toolsets they already have without additional hardware investment. If you want to run your workload on-premise, then AWS Outposts brings native AWS services, infrastructure, and operating models to virtually any data center, co-location space, or on-premises facility. You will learn more details about hybrid cloud services later in this book.

This is just a small sample of the many AWS services that you will see throughout this book. Let's delve a little deeper into how influential AWS currently is and how influential it has the potential to become.

The market share, influence, and adoption of AWS

For the first nine years of AWS's existence, Amazon did not break down its AWS sales, and since 2015 Amazon started reporting AWS sales separately. As of April 2022, Microsoft does not fully break down its Azure revenue and profit in its quarterly reports. They disclosed their Azure revenue growth rate without reporting the actual revenue number, instead burying Azure revenues in a bucket called Commercial Cloud, which also includes items such as Office 365 revenue. Google has been cagey about breaking down its Google Cloud Platform (GCP) revenue for a long time. Google finally broke down its GCP revenue in February 2019, but GCP also combines its cloud and workplace (G-suite) tools in the same bucket.

AWS has a large market share with a $74B run rate in 2021 and 37% year-over-year growth, which is phenomenal for a business of its size. As of 2021, AWS is leading cloud IaaS with 39% of the market share as per TechRadar’s global cloud market report. AWS has done a great job of protecting its market share by adding more and more services, adding features to existing services, building higher-level functionality on top of the core services it already offers, and educating the masses on how to best use these services.

We are in an exciting period when it comes to cloud adoption. Until a few years ago, many C-suite executives were leery of adopting cloud technologies to run their mission-critical and core services. A common concern was that they felt having on-premises implementations was more secure than running their workloads on the cloud.

It has become apparent to most of them that running workloads on the cloud can be just as secure as running them on-premises. There is no perfectly secure environment, and it seems that almost every other day, we hear about sensitive information being left exposed on the internet by yet another company. But having an army of security experts on your side, as is the case with the major cloud providers, will often beat any security team that most companies can procure on their own.

The current state of the cloud market for most enterprises is a state of Fear Of Missing Out (FOMO). Chief executives are watching their competitors jumping on the cloud, and they are concerned that they will be left behind if they don't leap.

Additionally, we see an unprecedented level of disruption in many industries propelled by the power of the cloud. Let's take the example of Lyft and Uber. Both companies rely heavily on cloud services to power their infrastructure, and old-guard companies in the space, such as Hertz and Avis, that depend on older on-premises technology are getting left behind. Part of the problem is the convenience that Uber and Lyft offer by being able to summon a car on demand. But the inability to upgrade their systems to leverage cloud technologies undoubtedly played a role in their diminishing share of the car rental market.

Let's continue learning some of the basic cloud terminologies and AWS terminology.

Basic cloud and AWS terminology

There is a constant effort by technology companies to offer common standards for certain technologies while providing exclusive and proprietary technology that no one else offers. An example of this can be seen in the database market. The Standard Query Language (SQL) and the ANSI-SQL standard have been around for a long time. The American National Standards Institute (ANSI) adopted SQL as the SQL-86 standard in 1986. Since then, database vendors have continuously supported this standard while offering various extensions to make their products stand out and lock in customers to their technology.

Cloud providers provide the same core functionality for a wide variety of customer needs, but they all feel compelled to name these services differently, no doubt in part to try to separate themselves from the rest of the pack. As an example, every major cloud provider offers compute services. In other words, it is simple to spin up a server with any provider, but they all refer to this compute service differently:

AWS uses 

Elastic Cloud Computing

 (

EC2

) instances.

Azure uses 

Azure Virtual Machines

.

GCP uses 

Google Compute Engine

.

The following tables give a non-comprehensive list of the different core services offered by AWS, Azure, and GCP and the names used by each of them. However, if you are confused by all the terms in the tables, don't fret. We will learn about many of these services throughout the book and when to use them.

Figure 1.2 – Cloud provider terminology and comparison (part 1)

These are some of the other services, including serverless technology services and database services:

Figure 1.3 – Cloud provider terminology and comparison (part 2)

These are additional services:

Figure 1.4 – Cloud provider terminology and comparison (part 3)

The next section will explain why cloud services are becoming popular and why AWS adoption is prevalent.

Why is AWS so popular?

Depending on who you ask, some estimates peg the global cloud computing market at around USD 445 billion in 2021, growing to about USD 950 billion by 2026. This implies a Compound Annual Growth Rate (CAGR) of around 17% for the period.

There are multiple reasons why the cloud market is growing so fast. Some of them are listed here:

Elasticity

Security

Availability

Faster hardware cycles

System administration staff

In addition to the above, AWS provides access to emerging technologies and faster time to market. Let's look at the most important reason behind the popularity of cloud computing (and, in particular, AWS) first.

Elasticity

Elasticity may be one of the most important reasons for the cloud's popularity. Let's first understand what it is.

Do you remember the feeling of going to a toy store as a kid? There is no feeling like it in the world. Puzzles, action figures, games, and toy cars were all at your fingertips, ready for you to play with them. There was only one problem: you could not take the toys out of the store. Your mom or dad always told you that you could only buy one toy. You always had to decide which one you wanted, and invariably, after one or two weeks of playing with that toy, you got bored with it, and the toy ended up in a corner collecting dust, and you were left longing for the toy you didn't choose.

What if I told you about a special, almost magical, toy store where you could rent toys for as long as you wanted, and the second you got tired with the toy, you could return it, change it for another toy, and stop any rental charges for the first toy? Would you be interested?

The difference between the first, traditional store and the second, magical store is what differentiates on-premises environments and cloud environments.

The first toy store is like setting up infrastructure on your own premises. Once you purchase a piece of hardware, you are committed to it and will have to use it until you decommission it or sell it at a fraction of what you paid for it.

The second toy store is analogous to a cloud environment. If you make a mistake and provision a resource that's too small or too big for your needs, you can transfer your data to a new instance, shut down the old instance, and, importantly, stop paying for that instance.

More formally defined, elasticity is the ability of a computing environment to adapt to changes in workload by automatically provisioning or shutting down computing resources to match the capacity needed by the current workload.

In AWS and the other main cloud providers, resources can be shut down without having to terminate them completely, and the billing for resources will stop if the resources are shut down.

One important characteristic of public cloud providers such as AWS is the ability to quickly and frictionlessly provision resources. These resources could be a single instance of a database or a thousand copies of the application and web servers used to handle your web traffic. These servers can be provisioned within minutes.

Contrast that with how performing the same operation may play out in a traditional on-premises environment. Let's use an example. Say you need to set up a cluster of servers to host your latest service. Your next actions would probably look something like this:

You visit the data center and realize that the current capacity is insufficient to host this new service.

You map out a new infrastructure architecture.

You size the machines based on the expected load, adding a few more terabytes and a few gigabytes to ensure that you don't overwhelm the service.

You submit the architecture for approval to the procurement department and hard vendors.

You wait. Most likely for months.

It may not be uncommon once you get the approvals to realize that the market opportunity for this service is now gone or that it has grown more. Imagine what will happen if, after getting everything set up in the data center and after months of approvals, you told the business sponsor that you made a mistake. You ordered a 64 GB RAM server instead of a 128 GB, so you won't have enough capacity to handle the expected load. Getting the right server will take a few more months. Also, the market is moving fast, and your user workload increases five times by the time you get the server. Now it's bad news for business, as you cannot scale your server so fast, the user experience will ultimately be compromised, and users will switch to other options.

This sort of problem is much less likely to happen in a cloud environment because instead of needing months to provision your servers, they can be provisioned in minutes. Correcting the size of the server may be as simple as shutting down the server for a few minutes, changing a drop-down box value, and restarting the server again. You can even go serverless and let the cloud handle the scaling for you while you focus on your business problems. You will learn more about serverless computing in Chapter 5, Harnessing the Power of Cloud Computing.

Hopefully, the example above drives our point home about the power of the cloud. The cloud exponentially improves the time to market by accelerating the time it takes for resources to be provisioned. Being able to deliver quickly may not just mean getting there first. It may be the difference between getting there first and not getting there in time.

Another powerful characteristic of a cloud computing environment is the ability to quickly shut down resources and, significantly, not be charged for that resource while it is down. In our continuing on-premises example, if we shut down one of our servers. Do you think we can call the company that sold us the server and politely asks them to stop charging us because we shut the server down? That would be a very odd conversation. It would probably not be a delightful user experience, depending on how persistent we were. They would probably say, "You bought the server; you can do whatever you want with it, including using it as a paperweight." Once the server is purchased, it is a sunk cost for the duration of the server's useful life.

In contrast, whenever we shut down a server in a cloud environment. The cloud provider can quickly detect that and put that server back into the pool of available servers for other cloud customers to use that newly unused capacity.

This distinction cannot be emphasized enough. The only time absolute on-premises costs may be lower than cloud costs is when workloads are extremely predictable and consistent. Computing costs in a cloud environment on a per-unit basis may be higher than on-premises prices, but the ability to shut resources down and stop getting charged for them makes cloud architectures cheaper in the long run, often in a quite significant way. Let's look at exactly what this means by reviewing a few examples.

Web storefront - A famous use case for cloud services is to use them to run an online storefront. Website traffic in this scenario will be highly variable depending on the day of the week, whether it's a holiday, the time of day, and other factors—almost every retail store in the USA experiences more than a 10x user workload during Thanksgiving week. The same goes for boxing day in the UK, Diwali in India, Singles’ day in china, and almost every country has a shopping festival. This kind of scenario is ideally suited for a cloud deployment. In this case, we can set up resource auto-scaling that automatically scales up and down compute resources as needed. Additionally, we can set up policies that allow database storage to grow as needed.

Big data workloads – As data volumes are increasing exponentially, the popularity of Apache Spark and Hadoop continues to increase to analyze GBs and TBs of data. Many Spark clusters don't necessarily need to run consistently. They perform heavy batch computing for a period and then can be idle until the next batch of input data comes in. A specific example would be a cluster that runs every night for 3 or 4 hours and only during the working week. In this instance, you need decoupled compute and data storage where you can shut down resources that may be best managed on a schedule rather than by using demand thresholds. Or, we could set up triggers that automatically shut down resources once the batch jobs are completed. AWS provides that flexibility where you can store your data in Amazon Simple Storage Service (S3) and spin up an Amazon Elastic Map-reduce cluster (EMR) to run spark jobs and shut them down after storing results back in decoupled Amazon S3. You will learn more about these services in Chapter 9, AWS EMR and AWS Glue – Extracting, Transforming, and Loading Data.

Employee workspace - In an on-premise setting, you provide a high configuration desktop/laptop to your development team and pay for it for 24 hours a day, including weekends. However, they are using one-fourth of the capacity considering an eight-hour workday. AWS provides workspaces accessible by low configuration laptops, and you can schedule them to stop during off-hours and weekends, saving almost 70% of the cost.

Another common use case in technology is file and object storage. Some storage services may grow organically and consistently. The traffic patterns can also be consistent. This may be one example where using an on-premises architecture may make sense economically. In this case, the usage pattern is consistent and predictable.

Elasticity is by no means the only reason that the cloud is growing in leaps and bounds. The ability to easily enable world-class security for even the simplest applications is another reason why the cloud is becoming pervasive.

Security

The perception of on-premises environments being more secure than cloud environments was a common reason companies big and small would not migrate to the cloud. More and more enterprises now realize that it is tough and expensive to replicate the security features provided by cloud providers such as AWS. Let's look at a few of the measures that AWS takes to ensure the security of its systems.

Physical security

AWS data centers are highly secured and continuously upgraded with the latest surveillance technology. Amazon has had decades to perfect its data centers' design, construction, and operation.

AWS has been providing cloud services for over 15 years, and they have an army of technologists, solution architects, and some of the brightest minds in the business. They are leveraging this experience and expertise to create state-of-the-art data centers. These centers are in nondescript facilities. You could drive by one and never know what it is. It will be extremely difficult to get in if you find out where one is. Perimeter access is heavily guarded. Visitor access is strictly limited, and they always must be accompanied by an Amazon employee.

Every corner of the facility is monitored by video surveillance, motion detectors, intrusion detection systems, and other electronic equipment. Amazon employees with access to the building must authenticate themselves four times to step on the data center floor.

Only Amazon employees and contractors that have a legitimate right to be in a data center can enter. Any other employee is restricted. Whenever an employee does not have a business need to enter a data center, their access is immediately revoked, even if they are only moved to another Amazon department and stay with the company. Lastly, audits are routinely performed and are part of the normal business process.

Encryption

AWS makes it extremely simple to encrypt data at rest and data in transit. It also offers a variety of options for encryption. For example, for encryption at rest, data can be encrypted on the server side, or it can be encrypted on the client side. Additionally, the encryption keys can be managed by AWS, or you can use keys that are managed by you using tamper-proof appliances like a Hardware Security Module (HSM). AWS provides you with a dedicated cloud HSM to secure your encryption key if you want one. You will learn more about AWS security in Chapter 7, Best Practices for Application Security, Identity, and Compliance.

AWS supports compliance standards

AWS has robust controls to allow users to maintain security and data protection. We'll discuss how AWS shares security responsibilities with its customers, but the same is true of how AWS supports compliance. AWS provides many attributes and features that enable compliance with many standards established in different countries and organizations. By providing these features, AWS simplifies compliance audits. AWS enables the implementation of security best practices and many security standards, such as these:

STAR

SOC 1/SSAE 16/ISAE 3402 (formerly SAS 70)

SOC 2

SOC 3

FISMA, DIACAP, and FedRAMP

PCI DSS Level 1

DOD CSM Levels 1-5

ISO 9001 / ISO 27001 / ISO 27017 / ISO 27018

MTCS Level 3

FIPS 140-2

I TRUST

In addition, AWS enables the implementation of solutions that can meet many industry-specific standards, such as these:

Criminal Justice Information Services

 (

CJIS

)

Family Educational Rights and Privacy Act

 (

FERPA

)

Cloud Security Alliance

 (

CSA

)

Motion Picture Association of America

 (

MPAA

)

Health Insurance Portability and Accountability Act

 (

HIPAA

)

The above is not a full list of compliance standards; there are many more compliance standards met by AWS according to industries and local authorities across the world.

Another important thing that can explain the meteoric rise of the cloud is how you can stand up high-availability applications without paying for the additional infrastructure needed to provide these applications. Architectures can be crafted to start additional resources when other resources fail. This ensures that we only bring additional resources when necessary, keeping costs down. Let's analyze this important property of the cloud in a deeper fashion.

Availability

When we deploy infrastructure in an on-premises environment, we have two choices. We can purchase just enough hardware to service the current workload or ensure that there is enough excess capacity to account for any failures. This extra capacity and eliminating single points of failure is not as simple as it may seem. There are many places where single points of failure may exist and need to be eliminated:

Compute instances can go down, so we need a few on standby.

Databases can get corrupted.

Network connections can be broken.

Data centers can flood or be hit by earthquakes.

Using the cloud simplifies the "single point of failure" problem. We have already determined that provisioning software in an on-premises data center can be long and arduous. Spinning up new resources can take just a few minutes in a cloud environment. So, we can configure minimal environments knowing that additional resources are a click away.

AWS data centers are built in different regions across the world. All data centers are "always-on" and deliver services to customers. AWS does not have "cold" data centers. Their systems are extremely sophisticated and automatically route traffic to other resources if a failure occurs. Core services are always installed in an N+1 configuration. In the case of a complete data center failure, there should be the capacity to handle traffic using the remaining available data centers without disruption.

AWS enables customers to deploy instances and persist data in more than one geographic region and across various data centers within a region. Data centers are deployed in fully independent zones. Data centers are constructed with enough separation between them such that the likelihood of a natural disaster affecting two of them simultaneously is very low. Additionally, data centers are not built in flood zones.

Data centers have discrete Uninterruptable Power Supplies (UPSes) and onsite backup generators to increase resilience. They are also connected to multiple electric grids from multiple independent utility providers. Data centers are connected redundantly to multiple tier-1 transit providers. Doing all this minimizes single points of failure. You will learn more details about AWS global Infrastructure in Chapter 3, AWS networking and content delivery.

Faster hardware cycles

When hardware is provisioned on-premises, it starts becoming obsolete from the instant that it is purchased. Hardware prices have been on an exponential downtrend since the first computer was invented, so the server you bought a few months ago may now be cheaper, or a new version of the server may be out that's faster and still costs the same. However, waiting until hardware improves or becomes cheaper is not an option. A decision needs to be made at some point to purchase it.

Using a cloud provider instead eliminates all these problems. For example, whenever AWS offers new and more powerful processor types, using them is as simple as stopping an instance, changing the processor type, and starting the instance again. In many cases, AWS may keep the price the same or even cheaper when better and faster processors and technology become available, especially with their own preoperatory technology like the Graviton chip.

The cloud optimizes costs by building virtualization at scale. Virtualization is running multiple virtual instances on top of a physical computer system using an abstract layer sitting on top of actual hardware. More commonly, virtualization refers to the practice of running multiple operating systems on a single computer at the same time. Applications running on virtual machines are unaware that they are not running on a dedicated machine and share resources with other applications on the same physical machine.

A hypervisor is a computing layer that enables multiple operating systems to execute in the same physical compute resource. The operating systems running on top of these hypervisors are Virtual Machines (VMs) – a component that can emulate a complete computing environment using only software but as if it was running on bare metal. Hypervisors, also known as Virtual Machine Monitors (VMMs), manage these VMs while running side by side. A hypervisor creates a logical separation between VMs. It provides each of them with a slice of the available compute, memory, and storage resources. It allows VMs not to clash and interfere with each other. If one VM crashes and goes down, it will not make other VMs go down with it. Also, if there is an intrusion in one VM, it is fully isolated from the rest.

AWS uses its own proprietary Nitro hypervisor. The AWS Nitro System is the underlying platform for its next-gen EC2 instances, which help to improve performance while further reducing cost. Traditionally, hypervisors protect the physical hardware and BIOS virtualizes the CPU, storage, and networking, and provide a rich set of management capabilities. With the AWS Nitro System, you can break apart those functions, offload them to dedicated hardware and software, and reduce costs by delivering practically all of the resources of a server to EC2 instances.

System administration staff

An on-premises implementation may require a full-time system administration staff and a process to ensure that the team remains fully staffed. Cloud providers can handle many of these tasks by using cloud services, allowing you to focus on core application maintenance and functionality and not have to worry about infrastructure upgrades, patches, and maintenance.

By offloading this task to the cloud provider, costs can come down because the administrative duties can be shared with other cloud customers instead of having a dedicated staff. You will learn more details about system administration in Chapter 8, Drive Efficiency with Cloud Automation, Monitoring, and Alerts.

This ends the first chapter of the book, which provided a foundation on the cloud and AWS. As you move forward with your learning journey, in subsequent chapters, you will dive deeper and deeper into AWS services, architecture, and best practices.

Summary

This chapter pieced together many of the technologies, best practices, and AWS services we cover in the book. As fully featured as AWS has become, AWS will certainly continue to provide more and more services to help enterprises, large and small, simplify the information technology infrastructure.

In this chapter, you learned about cloud computing and the key differences between the public and private cloud. This lead into learning more about the largest public cloud provider, AWS, and you learned about AWS’s market share and adoption.

We also covered some reasons that the cloud in general and AWS, in particular, are so popular. As we learned, one of the main reasons for the cloud's popularity is the concept of elasticity, which we explored in detail. You learned about AWS services growth over the year along with it’s key differentiators from other cloud providers. Further, you explored AWS terminology compared to other key players like Azure and GCP. Finally, you learned about the benefits of AWS and the reasons behind its popularity.