What is AWS | Amazon Web Services for Data Science

Overview

• Amazon Web Services (AWS) is the leading cloud platform for implementing machine learning solutions
• Every Data Science Professional Should Learn How AWS Works

Introduction

“Your machine ran out of memory”.

Sounds familiar? It certainly is for me, especially whenever I try to run a complex machine learning algorithm on my personal machine. It's quite a frustrating experience for many data science professionals.. We don't have the unlimited computing power of the tech giants, then, what should we do?

This is where the power of the cloud has transformed data science. And amazon, with your AWS offering, has conquered the data science market like never before.

Cloud computing has seen tremendous growth in recent years. Almost every organization today uses cloud computing for its wide range of services. It is expected that the 70% of all money spent on technology goes to cloud services for business purposes 2020.

Did you know that AWS revenue in the first quarter of 2020 were from $ 10 billion? That's almost double that of its next closest competitor!! Every data science professional, from data science to data analyst, you need to learn AWS and know how it works.

Then, in this article, Let's dive into what AWS is and find out why it's at the forefront of cloud computing services.

Table of Contents

1. What is Amazon Web Services (AWS)?
2. History of Amazon web services
   
3. Services provided by Amazon Web Services
4. Here's why you can't use your local system for all your data tasks
5. How Amazon Web Services Can Help You?

What is Amazon Web Services (AWS)?

AWS is an Amazon cloud computing platform that provides services such as Infrastructure as a Service (IaaS), platform as a service (PaaS) and software packaged as a service (SaaS) according to the pay-as-you-go system. It was launched in 2006, but it was originally used to handle Amazon's online retail operations.

AWS has 3 main products:

1. EC2 (Amazon Elastic Compute Cloud):
   EC2 allows users to rent machines / virtual servers on which they run their own applications. These servers come in different operating systems and Amazon charges you based on computing power and server capacity (namely, hard drive capacity, CPU, memory, etc.) and the duration of the server.
2. glacier
   Glacier is a low-cost online file storage web service. Amazon Glacier is designed for long-term storage of inactive data that does not need to be quickly retrieved.
3. S3 (Amazon Simple Storage Services)
   S3 provides object storage through a web service interface, with scalability and high speed as an advantage.

AWS offers your consumers many benefits:

• Security: AWS provides comprehensive security capabilities to ensure the most demanding requirements.
• Compliance: AWS has extensive controls, audits and extensive security accreditation.
• Hybridism: allows the construction of hybrid architectures that extend the local infrastructure to the cloud.
• Scalability: allows you to scale up and down with ease
• Pay per use: this means that you pay according to the services you use. Useless, pay less. Use more, pay more, but the unit price goes down as the scale increases

Here's an article to help you get started on your AWS journey:

History of Amazon Web Services (AWS)

AWS was initially launched in 2002, but only provided some services. In 2006, AWS launched its cloud products that included Amazon S3 cloud storage, SQS (Simple Queue Service) and EC2 and, in doing so, marked its entry into the online core services industry.

In 2009, AWS saw the international expansion of AWS to Europe, where S3 and EC2 were released. Elastic Block Store (EBS), providing block-level storage, y Amazon CloudFront, a content delivery network, launched and onboard to AWS.

Provides block-level storage for use with Amazon EC2 instances. Amazon Elastic Block Store volumes are networked and independent of the lifetime of an instance.

Over the years, many services were added to the AWS platform, which has made it a profitable and highly scalable platform. Now, AWS has its data centers around the world, including United States, Japan, Europe, Australia and Brazil.

AWS Global Infrastructure Map

Services provided by Amazon Web Services

AWS provides the following services in the respective domains:

1. Computer Services:
   • EC2 (Elastic Compute Cloud)
   • EX (Elastic Container Service for Kubernetes)
   • Lambda
   • Amazon LightSail
   • Elastic bean stem
2. Database services:
   • Neptuno
   • RDS
   • Aurora
   • RedShift
   • DynamoDB
   • ElastiCache
3. Security services:
   • KMS (key management service)
   • AWS IAM (identity and access management)
   • Inspector
   • WAF (web application firewall)
   • Cloud Directory
   • Certificate manager
   • Organizations
   • Armor
   • You have
   • guard
4. Warehousing services:
   • Amazon Glacier
   • S3 (simple storage service)
   • AWS Snowball
   • Stretch Block Store
5. Migration services:
   • Snowball
   • DMS (Database migration service)
   • SMS (Server migration service)
6. Analytical services:
   • Kinesis
   • QuickSight EMR (elastic map reduction)
   • Data pipeline
   • CloudSearch
   • Athena
   • ElasticSearch
7. Management tools:
   • CloudWatch
   • CloudFormation
   • CloudTrail
   • OpsWorks
   • Config
   • AWS autoscaling
8. Courier services:

To learn more about the services provided by AWS, Click here.

At this stage, have a broad understanding of what AWS is. Then, Let's shed some light on why companies require their data scientists to know about AWS.

Here's why you can't use your local system for all your data tasks

Do you remember when you were sitting idle waiting for the system to respond? Then, We highlight a list of problems that your local systems must be able to overcome:

1. The system in which you implement tasks has a low processing power that will affect your punctuality. You must have noticed this while processing large volumes of data and I'm pretty sure thoughts of a centrally managed external system must have crossed your mind.
2. Large data sets do not fit in IDE system memory, that is necessary for model analysis or training. Remember when your Jupyter notebook got stuck?
3. It costs a lot in terms of both time and money to install and maintain your own hardware

How Amazon Web Services Can Help You?

I'm sure many of you are still wondering why you should use AWS. Why not opt ​​for something else (like Google GCP)? Let me answer this by providing the following benefits for AWS:

1. Easy to use

   AWS has a very well documented user interface that eradicates the requirement for on-site servers to meet IT demands. This facilitates the implementation of programs, software from time to time. AWS meets all your needs.

2. Various tools

   Earlier in this article, we saw the diverse range of services that AWS has to offer. It's the all-in-one solution for your IT and cloud requirements, considering its efficiency.

3. Computing Capacity

   You don't need to worry about whether large data sets will fit in your IDE's system memory or not..

4. Infrastructure

   AWS Global Cloud Infrastructure is the most extensive and reliable cloud platform, that offers more than 175 full-featured data center services globally. Whether you need to deploy your application workloads around the world with a single click or you want to build and deploy specific applications closer to your end users with single-digit millisecond latency, AWS gives you cloud infrastructure where and when you need it. easily.

5. Prices

I feel like this will act as the most compelling points!! AWS is one of the cheapest platforms for cloud service. This is really useful for small businesses to run and grow without having to put a lot of working capital into servers..

Gartner Magic Quadrant 2020 for cloud infrastructure and platform services

Why Companies Emphasize AWS Knowledge for Their Data Scientists?

Regardless of the company you work for, cloud infrastructure will become an important part of your daily data science regimen because companies have leaned more towards cloud computing to find solutions.

According to a report by Indeed.com, AWS increased from a share of 2,7% in technological skills in 2014 yet 14,2% in 2019. That's a change from 418%!

This is due to the pricing model that AWS works on. AWS works on a pay-as-you-go model and charges by the hour or by the second. It also offers an option to reserve a specific amount of computing capacity at discounted rates..

What's more, AWS considers potential consumers who cannot pay for their services. For them, provides AWS Free Tier service, allowing them to gain hands-on experience with AWS services completely free of charge.

All the companies, whether big or small, they want to save costs. Small businesses save server purchase costs and conglomerates gain authenticity and productivity. AWS services are also very powerful. On the one hand, when it takes days to set up a Hadoop cluster with Spark, AWS does it in a few minutes.

Final notes

In today's competitive world, having hands-on experience with cloud services like AWS provides a huge advantage in a data science career. AWS is now very popular with businesses and their experience with such cloud computing platforms highlights their skills during the hiring process..

Here are some additional resources you should consider:

I hope this article serves as a strong argument to support why cloud computing is necessary for data scientists.. Please use the comment section below if you have any ideas to share or general inquiries.

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