Why is AWS used

In this tutorial, we shall learn what is AWS and why is it so widely used. We shall also learn the benefits of using Amazon Web Services.

Why is AWS used?

In this tutorial, we will learn what AWS (Amazon Web Services) is and why one must use it. We will also be taking a look at the benefits of using Amazon Web Services.

Learn How to Build a Data Pipeline in Snowflake 

Amazon Web Services (AWS) is the cloud platform offered by Amazon.com Inc (AMZN). AWS is made up of many different cloud computing products and services. It offers everything from servers, storage, and computing right up to mobile development, email, networking, and security. AWS offers three main products –

1. Amazon’s storage system called S3
2. A cost-effective cloud storage service called Glacier
3. Amazon’s virtual machine service called EC2


Following are the benefits of using AWS –

• It is Easy to use
AWS is meant to let application providers, ISVs, and vendors host their applications rapidly and securely, whether they are existing or new SaaS-based apps. To gain access to AWS's application hosting platform, you can make use of the AWS Management Console or different web services APIs.

• It is Flexible
AWS gives you the flexibility to choose your OS, programming language, web application platform, database, and other services. AWS provides a virtual environment in which you can deploy the programs and services required by your application. This facilitates the migration of existing apps while still allowing for the creation of new ones.

• It is Secure
AWS uses an end-to-end approach to secure and fortify the infrastructure, including physical, operational, and software safeguards.

• It is Cost-Effective
There are no long-term commitments or upfront costs, and you only pay for the computing power, storage, and other resources that you use.

• It is scalable and performs well.
Using AWS technologies such as Auto Scaling and Elastic Load Balancing, your application may scale up or down based on demand. Because of Amazon's huge infrastructure, you have access to processing and storage resources whenever you need them.

• It is Reliable
With AWS, you have access to a worldwide computing infrastructure that has been honed for over a decade.

What Users are saying..

profile image

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd
linkedin profile url

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain... Read More

Relevant Projects

Build a Spark Streaming Pipeline with Synapse and CosmosDB
In this Spark Streaming project, you will learn to build a robust and scalable spark streaming pipeline using Azure Synapse Analytics and Azure Cosmos DB and also gain expertise in window functions, joins, and logic apps for comprehensive real-time data analysis and processing.

GCP Project to Learn using BigQuery for Exploring Data
Learn using GCP BigQuery for exploring and preparing data for analysis and transformation of your datasets.

Building Data Pipelines in Azure with Azure Synapse Analytics
In this Microsoft Azure Data Engineering Project, you will learn how to build a data pipeline using Azure Synapse Analytics, Azure Storage and Azure Synapse SQL pool to perform data analysis on the 2021 Olympics dataset.

Python and MongoDB Project for Beginners with Source Code-Part 1
In this Python and MongoDB Project, you learn to do data analysis using PyMongo on MongoDB Atlas Cluster.

Build Serverless Pipeline using AWS CDK and Lambda in Python
In this AWS Data Engineering Project, you will learn to build a serverless pipeline using AWS CDK and other AWS serverless technologies like AWS Lambda and Glue.

Implementing Slow Changing Dimensions in a Data Warehouse using Hive and Spark
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.

Build a big data pipeline with AWS Quicksight, Druid, and Hive
Use the dataset on aviation for analytics to simulate a complex real-world big data pipeline based on messaging with AWS Quicksight, Druid, NiFi, Kafka, and Hive.

AWS Project for Batch Processing with PySpark on AWS EMR
In this AWS Project, you will learn how to perform batch processing on Wikipedia data with PySpark on AWS EMR.

Project-Driven Approach to PySpark Partitioning Best Practices
In this Big Data Project, you will learn to implement PySpark Partitioning Best Practices.

Build an AWS ETL Data Pipeline in Python on YouTube Data
AWS Project - Learn how to build ETL Data Pipeline in Python on YouTube Data using Athena, Glue and Lambda