Top 8 Cloud Computing Projects For All Levels

Organizations have been shifting their IT resources to the cloud, leading to the development of cloud computing. The goal is to find ways to utilize cloud platforms, such as AWS, GCP, and Azure, to build solutions related to an organization’s needs. As such, there is a growing demand for tech professionals, like cloud engineers, with knowledge of cloud environments.

Build up your skills with these cloud computing projects and get started on a career in cloud computing. If you're new to the field, check out our Understanding Cloud Computing course and read our guide on how to become a cloud engineer.

Why Work on Cloud Computing Projects?

The cloud can be a daunting ecosystem that requires direct experience to fully understand. Getting hands-on experience is the best way to learn how cloud environments work. That’s why having cloud computing projects is important. You are able to get experience and build something that can be used during your job searching process.

Beginner Cloud Computing Projects

These beginner projects will get you started on using a cloud platform for simple tasks. These projects are focused on doing things that are normally done on-premises, such as hosting a website just built in the cloud.

1. Static website hosting

Hosting a website on a cloud platform is a fundamental project that showcases a basic understanding of the cloud. This ensures you understand the role cloud computing plays in replacing traditional IT infrastructure while giving you an introduction to the many features of the cloud.

Resources:

A great guide to follow are the top projects for AWS on DataCamp. If you are new to AWS in general, consider taking the introduction to AWS course first to get some experience with the AWS platform. These two combined will get you well on your way for your first cloud computing project!

Skills Learned:

• How to use AWS for web hosting
• Basic HTML and website design
• Basic AWS

2. Serverless email/SMS application

The next step is utilizing the cloud to interact with users. One common feature we see is the ability to contact customers after they have filled out a form. This may be for promotions, confirmations, or reservations. With the cloud, we can manage all this through an API connection. The general goal is to have a static website hosted on a cloud service, which then shares that information with other cloud components which then send out the corresponding email or SMS.

From cloudisfree: flow diagram of how a serverless email/SMS application may look

Resources:

A great source to follow is from cloudisfree, where they guide you through the process of a serverless sending application. This also uses Amazon AWS and should be a straightforward addition to your previous project if you have successfully created a dynamic website. This also a good time to get some stronger AWS skills with the AWS Cloud and Technology services course on DataCamp.

Skills Learned:

• AWS Lambda
• API Gateway
• AWS Step Functions
• Python skills for creating functions
• HTML and JSON for website interactivity

Intermediate Cloud Computing Projects

Now that you have experience with the basics of the cloud, it’s time to take it to the next level with some more practical applications. For these projects, we’ll focus on growing the foundational skills you learned. You should now be able to use the cloud to build production-ready cloud applications following modern architecture.

3. Data analytics in the cloud

A growing portion of cloud computing is becoming dedicated to data analytics. As data volumes increase, organizations are relying more heavily on cloud-based data solutions to find answers to their data questions. As a cloud engineer, you have the opportunity to create some automated analytics using the cloud platform. Due to its scalability, cloud-based analytics solutions are able to grow easily with increasing demand. In this project, you will practice end-to-end deployment of data storage to data analytics. In this way, you fully understand how data will enter the cloud and turn into answers.

Resources:

Check out our guide on getting started with Azure Synapse. If you don’t have much experience with Azure, I recommend first looking at the Understanding Microsoft Azure course and reading some articles on Azure, Microsoft’s cloud solution. It is growing in market share and is poised to be dominant in certain markets compared to AWS and GCP.

Skills Learned:

• Azure Storage Fundamentals
• Azure Synapse
• Data analytics in the cloud

4. 3-tier web app

This is a natural progression from a static website. You create a more complex website architecture that uses the best features of the cloud. A 3-tier web app separates out the web, application, and data tiers. The web layer focuses on the user interface, the application layer focuses on the backend, and the data layer focuses on storing data. This is great because each portion can be scaled independently and allows for separate security for each component. 

Building out a 3-tier web app in the cloud requires an understanding of what cloud products are best suited for each layer and how to connect them into a single service.

From AWS documentation: architecture diagram for a three-tier web app

Resources:

Make sure you have a good understanding of cloud fundamentals. Follow along with this guide on building a 3-tier web application to build out this more complex project.  If you have never built a web app before, consider building something like this Python web app using Bokeh. Try separating the UI into the web layer and the data processing into the application layer.

Skills Learned:

• Python web apps
• 3-Tier Web app
• Cloud-based web development

Advanced Cloud Computing Projects

These advanced projects are more about using the cloud to its full potential. These projects are focused on building end-products and advanced technical demos with cloud products.  

5. Serverless machine learning

One of the more pivotal purposes cloud computing has served is expanding the machine learning capabilities of companies. Machine learning requires a lot of computational power which would require a lot of on-premise servers. Not all companies have the physical space or resources to purchase that much infrastructure. 

Thanks to the cloud, companies are able to perform serverless machine learning by leveraging cloud products like AWS Lambda or GCS BigQuery. Building out a serverless machine learning project shows your knowledge of modern trends and advanced skills with the cloud.

Resources:

Make sure you have a firm understanding of cloud architecture for data science and machine learning. You can then get started with the AWS guide for serverless image processing.  This particular project focuses on using Amazon Rekognition for image processing (specifically facial recognition). Make sure to try out this tutorial on AWS step functions as they are utilized heavily in this project as well. Add on a few other AWS products like AWS Lambda, DynamoDB, and EventBridge, you will have a complete image recognition project!

Skills Learned:

• Serverless Machine Learning
• AWS Rekognition
• AWS Lambda
• AWS Step Functions
• AWS DynamoDB
• AWS EventBridge

6. Cloud-based chatbot

With more people going online for services like shopping and banking, web-based customer service is even more important. Companies are leaning into AI chatbots to minimize overhead and help with simpler questions. Because of the ability to scale quickly, customers will face minimal delays when looking for answers to their problems. This allows companies to reduce the amount of customer service agents needed to serve their support needs and reduce wait times. Thanks to easily deployable products like Amazon Lex, there are plenty of options for getting some practice.

Resources:

Make sure you have a firm understanding of cloud architecture for data science and machine learning and its benefits for AI. Amazon offers a straightforward guide for getting started quickly with Amazon Lex. To keep things simple, this project uses Amazon CloudFormation for a straightforward web template. This deployment should be straightforward as it builds on many of the skills used in the serverless machine learning project above.

Skills Learned:

• Cloud Chatbot
• AI on the cloud
• Amazon Lex

Cloud Computing Open Source Projects

Finally, we have some projects that are focused on open-source cloud. These open-source projects allow you to build a cloud environment somewhat from scratch and in a more customizable way. These projects will demonstrate that you fully understand how cloud systems are constructed from servers and security to end-user connection.

7. OpenStack

OpenStack is one of the larger open-source cloud platforms. It allows users to build completely custom cloud stacks using the open-source software available. Just because it is open-source does not mean it is not robust. Large companies such as Rakuten, T-Mobile, and Target all have custom components built in OpenStack to run their operations. Learning this tech stack can prove to be extremely valuable.

Resources:

There are many resources out there about how to use OpenStack. Start by reading the documentation from OpenStack on how to get OpenStack and receive some training. Make sure to look up some YouTube videos and read up on basic start guides with OpenStack. This is a great opportunity to review the Cloud Computing course at DataCamp for reminders on foundational infrastructure for the cloud.

Skills Learned:

• Building cloud from scratch
• Open-source cloud projects
• Usage of OpenStack

8. OpenNebula

An alternative to OpenStack is Open Nebula. This project focuses on a more monolithic single-server architecture as a means of managing virtual machines and containers. It is a simpler means of deploying a custom cloud with quick deployment and intuitive set-up. If you are interested in utilizing hypervisors and virtual containers as the foundation of your cloud, then Open Nebula is a great choice.

Resources:

OpenNebula is a little less popular than OpenStack so finding resources may be a bit more challenging. The OpenNebula docs are a great starting point for getting up and running quickly while learning the foundations of the tool. Follow that with a course on Docker and you are well on your way to creating a container-based cloud infrastructure.

Skills Learned:

• Building cloud focused on virtualization
• Hands-on experience with open source cloud
• Usage of OpenNebula
• Docker

Summary

Here is a quick look at all the projects we’ve discussed above and how they might fit into your own learning plan.

Conclusion

We’ve seen that there are numerous projects out there to get you started on cloud computing. All of these are great options for building out a portfolio in cloud computing. Make sure to fully understand each project and be able to speak confidently about them so you can truly show your expertise. Make sure to continue to grow your knowledge on the fundamentals and potential certifications. Here are some resources to get started:

• 5 Best Cloud Certifications
• Google Cloud Certification
• Top 12 AWS Projects