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AWS (Amazon Web Services) is a cloud computing platform that provides computing power, database storage, content delivery and other functionality to individuals, companies and governments. Companies that use AWS save money, as the costs are typically less than operating their own servers. Even better, AWS costs are predictable, so that users can make financial plans based on their AWS costs.

Data scientists need to know AWS. With the rising need for data-based solutions, personal computers may not provide enough computing power for complex ML algorithms or enough memory to store the data. AWS allows data scientists to run their algorithms quickly at affordable costs.

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What Is AWS?

Amazon Web Services (AWS) is a global cloud computing infrastructure provider. Amazon launched it as a cloud service in 2006 to handle online retail operations within the company.

The first cloud products on AWS were Amazon S3 cloud storage, EC2, and the Simple Queue Service (SQS), which handled most of the core online services required.

With time, more services and cloud-based products have been incorporated into the AWS platform, making it highly scalable and cost-effective.

These products allow for computing, storage, networking, analytics, mobile, databases, developer tools, IoT, enterprise applications, security, and management tools.

Large enterprises trust AWS to power their workload in all categories, including data processing, web, and mobile applications, storage, warehousing, game development, etc.

AWS has expanded its reach globally, with data centers across 25 geographic regions and services available in over 80 zones. The coverage continues to grow as more regions add up to the list.

How Is AWS Used For Data Science?

AWS comes packaged with excellent features useful in data science.

Business models at all levels find these cloud services useful as they pay less for using the cloud compared to buying servers, and they are more productive because of the less maintenance cost involved.

A scientist familiar with AWS is at an advantage because:

  • They can easily set up their work infrastructure fast and easily. For example, if you’re to manually set up a Hadoop cluster using Spark, it may take days. But with AWS, the setup takes minutes to prepare.
  • They spend less on the tools. The pay-on-the-go feature on AWS helps you pay for only what you use. For example, you pay for Hadoop clusters only when you need them.
  • There are no manual maintenance practices like manual data back-up as AWS keeps the systems updated.
  • The products they develop on the cloud are ready for launching without interventions from engineers.

In data science, AWS services have proven to be indispensable as they significantly simplify the work for the data scientist. Here are some of the services and how they are used for data science.

Elastic Compute Cloud (EC2)

For a data scientist, the computer’s computing capacity for efficient workflow is supreme. But in some instances, the computing power in the devices isn’t enough to handle the tasks, and that’s where EC2 comes in.

EC2 provides secure and scalable cloud computing capacity, which data scientists can tap into and expand their data processing capacity. You can scale the system up or down to your preference by changing the bandwidth, memory, the number of virtual CPUs, etc.

When handling huge data scientist tasks and your local system lacks enough computing power to run the task, you can scale up to complete the project then scale down to cut out on the costs.

EC2 requires no maintenance from your side, so it’s reliable and usable at any time.

Simple Storage Service (S3)

Data scientists use the AWS S3 service to store data on the cloud and retrieve it at any time. The space is highly scalable to accommodate any data size and cost-effective for a data scientist to afford it.

The data is stored in a coded form with files based on buckets and keys. The user-friendliness of the service is not too good but working with APIs makes it easier. When retrieving a file, the APIs ask for the bucket and key to identify it.

S3 is highly reliable, with a durability delivery of 99.99%, to ensure data is always accessible and retrievable.

Relational Database Service (RDS)

RDS offers cloud database services where you can store your project data in a virtual relational database. It supports MySQL, SQL Server, PostgreSQL, ORACLE, and other SQL-based database frameworks.

With AWS, the relational database takes a few minutes to configure the parameters and be up running. It is anchored on the EC2 servers, so it is highly scalable to increase computing power and storage space.

Some versions of PostgreSQL and MySQL like Amazon Aurora have automated scaling up abilities when more space is needed. Factors like storage, data transfer, and computing power determine the costs of the RDS servers.

RedShift

This is the data warehouse for storing big data. It’s more like RDS where you can use SQL queries, only that it can handle much more data than RDS. It works by distributing data across a framework of different servers in a cluster.

A data scientist handling an extensive data workload can store it in the highly scalable Redshift and extract it with no downtime. Scaling up increases the number of clusters so that the data is further distributed, thus increasing the speed of accessing it.

It has three distribution styles for tables: ALL, KEY, and EVEN. These three styles help you organize and distribute the data across the servers depending on the data type.

Elastic MapReduce (EMR)

EMR sets up Hadoop clusters with Spark for distributed data storage. The best thing about using EMR service is the ability to use it on-demand.

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For example, you can set it up to access the code and data from a specified source like S3, run it on the cluster, and transfer the results somewhere else like RDS for storage, then stop the cluster.

Data scientists tend to use this AWS service more often because of the cut-out costs on the cluster. Moreover, it’s easy to use and good for reformatting, cleaning up, and analyzing big data.

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Where To Learn AWS For Data Science?

AWS is overwhelming for a beginner, mainly because of the many services they offer. However, the only way to move forward and master AWS is to start somewhere.

Here are some of the places you can learn AWS for data science.

The Official AWS Training Website

AWS has created many courses with free resources on both theoretical and practical work on different services. It’s a great place to learn, especially if you want a certificate.

AWS free tier

The AWS free tier gives you access to popular services for the first 12 months so that you can experiment with them free of charge.

Although access is limited to some services, those accessible are enough to give you hands-on experience with the cloud services in real life.

AWS Courses on edX

These courses are loaded with high-quality information, demos, practice exercises, and concept reviews to give you the value you need. Certification options are available at a small fee.

AWS YouTube Channel

The channel offers you an opportunity to learn and practice directly, especially if you’re using services accessible on the free tier account. The resources include how-to videos and tutorials, demos, and other useful videos from conferences.

AWS courses on Udemy

Udemy offers both free and paid courses for all levels of trainees. Some courses are created to focus on beginners who have little or no knowledge of AWS.

Qwicklabs

Qwicklabs’ preconfigured cloud environment gives you the freedom to learn AWS by trying out any services in an organized manner. The tutorials are useful as they introduce you to each service and provide you with the instructions for each task.

AWS podcast

The official AWS podcast will share with you stories of AWS users, updates on the services, and advice on hot topics. You can listen to the podcast anywhere you are as it is available on Google Podcast.

Conclusion

Cloud computing is becoming increasingly popular, especially in companies relying on end-to-end data-driven products like machine learning for better business.

A data scientist with good knowledge and experience in cloud services like AWS is likely to be preferred by hiring managers in a business model.