Table of contents
Recently I attended an basic of cloud computing class organized by AWS. What I learnt from there will be writing here in form of blogs that will be helpful for others too.
So, lets start with...
What is Cloud?
The term "cloud" in the context of computing refers to cloud computing. It is a technology that allows users to stop thinking infrastructure as a hardware instead of think it(use it as) as software.
It provide various feature such as:
Agility: On demand services
Elasticity: Easily scalable on demand
Deploy globally
Cost Saving
Advantages of cloud computing
Instead of investing in and maintaining physical infrastructure, companies can pay for on-demand resources. This pay-as-you-go model offers cost efficiency, scalability, flexibility, and allows organizations to focus on core competencies.
Cloud computing enables businesses to take benefit by efficiently utilizing resources across a vast network of servers. This results in lower costs for infrastructure, maintenance, and operations, making advanced computing capabilities accessible to a wide range of users at a more affordable price.
Stop guessing capacity: Cloud gives free hand to stop thinking about their hardware requirements.
Increase speed and agility.
Cloud gives us freedom of not spending money on running and maintaining data centers/servers.
One can go global in minutes in just few clicks
Trainer also enlightened us with their Gobal AWS infrastructure here is most of that...
AWS global Infrastructure
In the picture you can see Edge location, so what are aws egde locations lets discuss.
Firstly, edge infrastructure refers to the computing infrastructure that is located closer to the "edge" of the network, nearer to the location where data is generated, processed, and consumed. The concept of edge computing aims to reduce latency, improve performance, and enhance efficiency by processing data closer to the source rather than relying solely on centralized cloud data centers.
| AWS Outposts | AWS Local Zones | AWS Wavelength | |
| Overview | AWS infrastructure and services on premises | AWS infrastructure and services in large metro centers | AWS infrastructure and services in Commercial Service Provider (CSP) 5G networks |
| Use cases | Migration, local critical applications, data residency | Migration, low latency, local data processing | Ultra-low latency, local data processing |
| Service Model | Expandable capacity in customer's data center, colocation, on-premises location. | Scalable capacity in facility managed & operated by AWS | Scalable capacity in CSP data center managed and supported by AWS |
Coming back to cloud lets learn about some cloud deployment models.
Cloud deployment models
On premise
When your data is stored on your personal infrastructure and you have to keep it in running state for your works to keep going on it is called an on-premise infrastructure.
Hybrid
When your data is stored on your personal infrastructure as well as on cloud it is an Hybrid infrastructure.
Cloud
When you entire data store on cloud and your work is nothing without internet then it is called as an cloud infrastructure.
Some common cloud terms
Cloud Computing
Cloud computing is a technology that enables access to computing resources, like storage and processing power, over the internet. Users can store data and run applications without relying on local servers. This scalable and on-demand model offers flexibility, cost efficiency, and accessibility to a wide range of services.
Auto Scaling
Auto scaling is a cloud computing feature that automatically adjusts the number of compute resources, such as virtual machines, based on real-time requirements. It ensures optimal performance and cost efficiency by dynamically scaling up or down to match the workload, adapting to varying levels of traffic and usage.
Elasticity
Elasticity concept is similar to the Auto Scaling Concept. It in computing refers to the ability of a system to dynamically adapt and scale resources in response to changing workloads. It ensures flexibility by automatically allocating or deallocating resources, such as storage and processing power, to meet demand, optimizing performance, and minimizing costs in cloud computing environments.
Public cloud
A public cloud is a type of cloud computing service that offers computing resources—like storage, processing power, and applications—over the internet to the general public.
It is managed by third-party providers,
It allows users to access and use shared resources on a pay-as-you-go basis, promoting scalability, flexibility, and cost-effectiveness.
Private cloud
A private cloud is a cloud computing environment exclusively used by a single organization.
Infrastructure as a Service
IaaS provides virtualized computing resources over the internet. Users can rent virtual machines, storage, and networking infrastructure on a pay-as-you-go basis. It offers flexibility and scalability, allowing users to manage and control their applications and data while outsourcing the underlying hardware.
Platform as a Service
PaaS offers a platform that includes tools and services to facilitate application development and deployment.
Developers can focus on coding and building applications without managing the underlying infrastructure.
PaaS provides an environment for developing, testing, and deploying applications, streamlining the development process.
Software as a Service
SaaS delivers software applications over the internet on a subscription basis. Users can access the software through a web browser without the need for installation or maintenance. Examples include email services, customer relationship management (CRM) tools, and collaboration platforms.
Serverless computing
Serverless computing enables developers to build and run applications without managing the infrastructure. It abstracts server management and automatically scales resources (Auto Scaling) based on demand. Developers only pay for the actual compute resources used during the execution of code, rather than maintaining a constant server presence.
Big Data
Big Data refers to the processing and analysis of large and complex datasets. It involves techniques and technologies to handle, store, and analyze data beyond the capabilities of traditional databases. Big Data solutions often include distributed and parallel processing, data storage, and analytics tools to extract valuable insights from vast datasets.
I can surely say here the introduction to cloud ends and in the next blog we will learn about Introduction to AWS Services: Networking and Security.