Introduction to Cloud Computing
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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
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.
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
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
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
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
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
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)
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
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
AWS: Largest service portfolio, mature ecosystem
Azure: Strong enterprise integration, Windows compatibility
Google Cloud: Superior AI/ML capabilities, Kubernetes expertise
DigitalOcean: Developer-friendly, simpler interface
IBM Cloud: Enterprise focus, strong hybrid solutions
Alibaba Cloud: Dominant in Asia-Pacific
Oracle Cloud: Database and enterprise applications
Salesforce Cloud: CRM and business applications
VMware Cloud: Virtualization specialist
Red Hat OpenShift: Container platform
