Introduction to Cloud Computing

1. What is Cloud Computing?

Cloud computing is the provision of technological services like storage, compute, databases, networking, software, and more - over the internet with pay-as-you-go pricing by companies usch as Amazon, Google, Microsoft, etc. The three most common services are

  • Storage: Save and store data (structured, e.g excel files, and unstructured, e.g images, audio, etc.)

  • Compute: Provide processing power

  • Databases: Store more of structured data

1.1. Cloud computing characteristics

  • Virtualization:

    • Instead of Physical server -> multiple virtual servers

    • Maximizes the output of individual servers

    • Economies of scale

  • Scalability: Vertical (adding more power), Horizontal (adding more machines)

    • Easily add and remove resources as you need them

    • Scale resources as necessary

  • Cost: Only pay for resources when you are using them (Pay as you go)

    • No capital expenses of:

      • Buying hardware and software

      • Managing on-site infrastructure

  • Speed: Immediate access to ready-to-go cloud resources.

    • On-demand resourcing,

    • Fast set-up time,

    • Deploy services in a matter of minutes

  • Performance: Access to fast and efficient computing resources

    • Cloud gives access to:

      • Worldwide network of data centers

      • Fast and efficient computing hardware

  • Growth: Grow using a wide range of resources and services

    • On-demand resourcing limits growth constraints

    • Provision resources across a global network

  • Reliability: Guaranteed durability and availability of data and services

    • Data is duplicated across data centers

    • Availability is ensured even in cases of natural disasters

  • Security: Secure storage and management of your data

    • External party responsible for security

    • Particularly risky for businesses in highly regulated sectors

    • Cloud is becoming more and more secure

    • In some cases, an on-premise solution might be preferred. The best solution depends on the use case.

1.2. Key benefits:

  • Pay-as-you-go pricing

  • Scalability to handle varying workloads

  • No upfront infrastructure costs

  • Automatic software updates and maintenance

  • Access from anywhere with internet connection

1.3. Why Businesses Choose Cloud?

The cloud has revolutionized how companies operate. Startups can now compete with industry giants without massive infrastructure investments. They pay only for what they use, like electricity or water utilities.

A small business can start with minimal resources and scale up instantly during busy periods. No more guessing how much computing power they'll need – they can adjust on demand.

Cloud

On-premise

Scalable

Less scalable

Fast set-up speed

Takes time to set up

Pay-as-you-go

Ongoing costs

Servers can be cheaper or more secure in some cases

1.4. Cloud Service models

There are three primary service models in cloud computing:

  • Infrastructure as a Service (IaaS)

  • Platform as a Service (PaaS)

  • Software as a Service (SaaS)

IaaS (Infrastructure as a Service) offers virtual infrastructure (servers, storage, networks) and pay based on usage. Ideal for businesses requiring tight security and direct infrastructure control. Examples include Amazon EC2 and Google Compute Engine.

PaaS (Platform as a Service) Development platforms with pre-configured infrastructure for building and deploying applications. Best for teams developing custom software who want to focus on coding rather than managing servers. Examples include Heroku and Google App Engine.

SaaS (Software as a Service) Ready-to-use applications accessed through the internet. Perfect for small businesses needing team collaboration tools and web/mobile apps like Gmail, Zoom, or Dropbox.

Cloud Service Models vs On-premise Comparison:

There are many other service models as well. More functionality is being abstracted and offered as cloud service packages. Examples include

  • Hardware as a Service (HaaS)

  • Database as a Service (DBaaS)

  • Disaster Recovery as a Service (DRaaS)

  • Network as a Service (NaaS)

  • FaaS (Function as a Service): Focuses on single function (part of a software)

    • e.g., identity authentication, payment transactions

2. Cloud Deployment Models

There are three main deployment types, including private, public, and hybrid, depending on how much control a business needs over the cloud environment

Private Cloud

  • Exclusive infrastructure for single organization

  • Greater control over resources and security

  • Higher upfront costs

  • Can be on or off-premises

  • Uses virtualization for on-demand computing

Public Cloud

  • Shared infrastructure managed by providers (AWS, Azure, Google)

  • Quick to deploy, minimal investment

  • Easily scalable

  • No direct hardware access

  • Internet accessible

Hybrid Cloud

  • Combines multiple cloud models

  • Allows storing sensitive data privately while using public cloud services

  • Enables cloud bursting during peak demands

  • Cost-effective for handling seasonal spikes

Other Models

  • Multi-cloud: Uses services from multiple providers for flexibility and reduced vendor lock-in

  • Community Cloud: Shared infrastructure for organizations with common interests/requirements

2.1. Cloud Computing Roles

  • Data Scientist - Run analyses on the cloud

  • Machine Learning Engineer - Train and deploy machine learning models on the cloud

  • Data Engineer - Build pipelines on the cloud to ingest, transform, and store big data

  • Data Analyst - Access data on the cloud via business intelligence tools

  • Cloud Architect

    • Design cloud infrastructure for a given business problem

    • Plan the deployment of the infrastructure

    • Ensure scalability and optimized for cost

  • Cloud Engineer

    • Build, maintain and monitor cloud services

    • Migrating operations to the cloud

  • DevOps Engineer

    • Software Development + IT Operations

    • Ensure the reliability, availability, and scalability of the cloud through software development and automation

  • Security Engineer

    • Spec security requirements

    • Test and assess security of data on the cloud

    • Responsible for protecting organization and user data

3. Service Providers

3.1. Major Cloud Providers

  1. Amazon Web Services (AWS): Market leader, offers 200+ services including EC2, S3, Lambda

    • Storage: S3 (object storage), EBS (block storage)

    • Compute: EC2 (virtual servers), Lambda (serverless)

    • Database: RDS (relational), DynamoDB (NoSQL)

    • ML: SageMaker (ML platform), Rekognition (computer vision)

  2. Microsoft Azure: Strong enterprise integration, AI/ML services

    • Storage: Blob Storage, Disk Storage

    • Compute: Virtual Machines, Azure Functions

    • Database: Azure SQL, Cosmos DB

    • ML: Azure Machine Learning, Cognitive Services

  3. Google Cloud Platform (GCP): Known for data analytics, Kubernetes (an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications across clusters of hosts)

    • Storage: Cloud Storage, Persistent Disk

    • Compute: Compute Engine, Cloud Functions

    • Database: Cloud SQL, Cloud Firestore

    • ML: Vertex AI, TensorFlow Enterprise

Summary Table: AWS, Azure, GCP

Cloud Provider

Features

Storage

Compute

Database

Machine Learning (ML)

Amazon Web Services (AWS)

Market leader, offers 200+ services

S3 (object storage), EBS (block storage)

EC2 (virtual servers), Lambda (serverless)

RDS (relational), DynamoDB (NoSQL)

SageMaker (ML platform), Rekognition (computer vision)

Microsoft Azure

Strong enterprise integration, AI/ML services

Blob Storage, Disk Storage

Virtual Machines, Azure Functions

Azure SQL, Cosmos DB

Azure Machine Learning, Cognitive Services

Google Cloud Platform (GCP)

Known for data analytics, Kubernetes

Cloud Storage, Persistent Disk

Compute Engine, Cloud Functions

Cloud SQL, Cloud Firestore

Vertex AI, TensorFlow Enterprise

3.1.1. Key Differentiators

  • AWS: Largest service portfolio, mature ecosystem

  • Azure: Strong enterprise integration, Windows compatibility

  • Google Cloud: Superior AI/ML capabilities, Kubernetes expertise

3.2. Specialized Providers

  • DigitalOcean: Developer-friendly, simpler interface

  • IBM Cloud: Enterprise focus, strong hybrid solutions

  • Alibaba Cloud: Dominant in Asia-Pacific

  • Oracle Cloud: Database and enterprise applications

3.3. Industry-Specific:

  • Salesforce Cloud: CRM and business applications

  • VMware Cloud: Virtualization specialist

  • Red Hat OpenShift: Container platform

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