Raoul Harris
  • Introduction
  • Technical books
    • Data engineering with Alteryx
    • Deep learning in Python
    • Generative AI in action
    • Generative deep learning
    • Outlier analysis
    • Understanding deep learning
    • Understanding machine learning: from theory to algorithms (in progress)
    • Review: Deep learning: foundations and concepts
  • Technical courses
    • Advanced SQL Server masterclass for data analytics
    • Building full-stack apps with AI
    • Complete Cursor
    • DataOps methodology
    • DeepLearning.AI short courses
    • Generative AI for software development
      • Introduction to generative AI for software development
      • Team software engineering with AI
      • AI-powered software and system design
    • Generative AI with large language models
    • Generative pre-trained transformers
    • IBM DevOps and software engineering
      • Introduction to agile development and scrum
      • Introduction to cloud computing
      • Introduction to DevOps
    • Machine learning in production
    • Reinforcement learning specialization
      • Fundamentals of reinforcement learning
      • Sample-based learning methods
      • Prediction and control with function approximation
  • Non-technical books
    • Management skills for everyday life (in progress)
  • Non-technical courses
    • Business communication and effective communication specializations
      • Business writing
      • Graphic design
      • Successful presentation
      • Giving helpful feedback (not started)
      • Communicating effectively in groups (not started)
    • Illinois Tech MBA courses
      • Competitive strategy (in progress)
    • Leading people and teams specialization
      • Inspiring and motivating individuals
      • Managing talent
      • Influencing people
      • Leading teams
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On this page
  • Models
  • Service models
  • Deployment models
  • Components
  • Infrastructure
  • Storage and content delivery networks
  • Emergent trends and practices
  1. Technical courses
  2. IBM DevOps and software engineering

Introduction to cloud computing

Models

Service models

IaaS (Infrastructure as a Service):

  • Raw computing resources (servers, storage, networking)

  • Maximum flexibility and control

  • Examples: AWS EC2, Google Compute Engine, Azure VMs

PaaS (Platform as a Service):

  • Development and deployment platform

  • Managed runtime environment

  • Examples: Heroku, Google App Engine, Azure App Service

SaaS (Software as a Service):

  • Ready-to-use applications

  • Minimal management needed

  • Examples: Gmail, Salesforce, Microsoft 365

FaaS (Function as a Service):

  • Serverless computing

  • Pay only for actual function execution

  • Examples: AWS Lambda, Azure Functions

Deployment models

Public Cloud:

  • Resources owned/operated by cloud providers

  • Shared infrastructure with other customers

  • Examples: AWS, Azure, Google Cloud

  • Benefits: Cost-effective, scalable, low maintenance

  • Challenges: Less control, potential security concerns

Private Cloud:

  • Dedicated to a single organization

  • Can be on-premises or provider-hosted

  • Examples: VMware, OpenStack, Azure Stack

  • Benefits: More control, better security, compliance

  • Challenges: Higher costs, requires expertise

Hybrid Cloud:

  • Combination of public and private clouds

  • Data/apps shared between environments

  • Examples: Azure Arc, AWS Outposts, Google Anthos

  • Benefits: Flexibility, best of both worlds

  • Challenges: Complex management, integration needs

Multi-Cloud:

  • Using multiple public cloud providers

  • Example: Using both AWS and Azure

  • Benefits: Avoid vendor lock-in, optimize costs

  • Challenges: Complex management, different tools

Community Cloud:

  • Shared by organizations with common interests

  • Example: Government clouds

  • Benefits: Shared costs, common compliance

  • Challenges: Limited availability, specific use cases

Components

Infrastructure

A hypervisor is a software layer between the hardware and the VMs that allows multiple VMs to run on the same physical machine.

Bare metal servers are physical servers dedicated to a single tenant, without virtualization or hypervisor layers.

Types of hypervisor:

  • Type 1 (Bare-metal)

    • Runs directly on the hardware

    • More efficient and secure

    • Used in enterprise/data centres

  • Type 2 (Hosted)

    • Runs on top of OS

    • Easier to set up

    • Used for desktop virtualization

Containers vs. virtual machines:

  • Containers share OS kernel

  • Less resource overhead

  • Less isolation

Storage and content delivery networks

Direct Attached Storage (DAS):

  • Physically connected to one server

  • Fast, simple, and low cost, but limited scalability

Block Storage:

  • Raw storage volumes presented as blocks

  • High performance and bootable

File Storage:

  • Hierarchical storage with folders

  • Easy sharing and familiar structure

Object Storage:

  • Flat structure using unique identifiers

  • Highly scalable and cost-effective, but slow

A content delivery network is a globally-distributed network of servers that delivers web content to users from the nearest geographic location, reducing latency and improving performance. It acts as a cache between users and the origin server.

Emergent trends and practices

Message brokers are middleware that enable services to communicate asynchronously by handling message queuing, routing, and delivery.

Last updated 6 months ago