Top 30+ Cloud Computing Projects For Big Data Wizards In 2024

Get Your Hands On The Best Cloud Computing Projects And Take Your Big Data Career To New Heights. | ProjectPro

Top 30+ Cloud Computing Projects For Big Data Wizards In 2024
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Want to put your cloud computing skills to the test? Dive into these innovative cloud computing projects for big data professionals and learn to master the cloud!


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According to a recent report, the global cloud computing market will likely reach $1,402.7 billion by 2030, at a CAGR of 16.8% from 2023 to 2030.

As per another survey, over 90% of the respondents use cloud services and platforms to conduct business operations.

These figures indicate the massive potential for the growth of cloud computing jobs. Furthermore, cloud computing has revolutionized how we store, process, and analyze big data, making it an essential skill for professionals in data science and big data. The rapidly growing ability to harness the power of cloud computing can significantly boost career opportunities in big data and cloud computing as more and more small and medium-scale enterprises are adopting cloud technologies for their big data projects. This has made gaining hands-on experience with cloud computing through practical projects crucial. Cloud computing projects allow data professionals to apply their skills in real-world scenarios, solving complex data challenges using leading cloud platforms like AWS, Azure, and GCP. These projects offer an opportunity to gain practical exposure to cloud computing tools, technologies, and best practices, honing skills in high demand in the job market. This blog will explore the best cloud computing projects for big data experts that will inspire you to explore the power of cloud computing and take your big data skills to the next level.

Before diving straight into the projects, let us understand the significance of working on cloud computing projects for big data professionals.

Why You Must Work On Cloud Computing Projects?

Cloud computing-based mini projects or real-time projects will give you adequate exposure and experience in cloud technologies and other essential skills, such as data analytics, business intelligence, and analytical abilities. In terms of programming languages and frameworks, you can develop Java cloud computing projects, Android cloud computing projects, cloud computing projects in PHP, or any other popular programming language. Cloud computing projects for students also have numerous applications in the academic journey. You can also develop cloud computing projects for final-year engineering or MTech projects using cloud delivery and deployment models. These cloud project ideas can be your go-to guide for a final-year project during your academic period.

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For students and professionals striving to succeed in the data science and big data industry, working on cloud computing projects offers various benefits, some of which are discussed below:

  • Access To Efficient Computing Resources

Students and professionals can work with large datasets and complex data processing tasks without investing in expensive hardware or infrastructure by utilizing the computing power of cloud platforms like AWS, Azure, and GCP. For instance, a data scientist working on an ML project can leverage cloud computing platforms to train and test models on massive datasets, leading to faster results.

  •  Practical Knowledge Of Leading Tools And Technologies

Working on cloud computing projects allows you to explore the current cutting-edge technology used in the big data and data science industry. For example, data enthusiasts can leverage AWS Glue for data extraction and transformation, Amazon S3 for data storage, and Amazon Redshift for data analysis. This practical exposure to cloud-based tools and technology will improve the skill sets of students and professionals and further offer a competitive advantage to them.

  • Advanced Analytics And Big Data Exposure 

Big data and advanced analytics are commonly used in cloud computing projects. This enables professionals and students to gain practical experience working with large complex datasets and data preparation, data analysis, and machine learning—skills that are essential in the data science and big data industries. For instance, a data analyst can make data-driven decisions after analyzing consumer data and generating insights to enhance marketing strategy.

  • Career Growth Opportunities

Over 72,000 cloud computing jobs in the United States indicate that cloud computing skills are highly in demand in the data science and big data industry. By working on cloud computing projects, students and professionals may develop their skill sets, acquire practical experience, and showcase their knowledge to potential recruiters. This can lead to career advancement opportunities, such as promotions and job offers from leading organizations.

Now that you know the significant benefits of practicing cloud computing projects for students and working professionals, let us walk you through some exciting projects that will help you explore the vast possibilities of cloud computing in big data.

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30+ Best Cloud Computing Projects For Practice In 2024

Below is a list of unique cloud computing projects divided into various categories, such as cloud computing projects ideas for beginners, intermediate-level and advanced-level professionals, cloud computing projects using AWS, cloud computing projects for students, etc. You can work on these cloud computing project ideas to develop and improve your skills in the field of cloud computing along with other big data technologies.

Easy Cloud Computing Projects For Beginners

Below are a few beginner-friendly cloud computing projects ideas for those just starting with this powerful technology-

1. E-Learning App

E-learning has been a popular learning method, and its usability has increased after the outbreak of the Covid-19 pandemic. The project aims to develop and deploy a cloud-based e-learning application with 24x7 availability.

You can develop a simple Java-based cloud computing app offering e-learning solutions. Learning, sharing, and reusing will be the major components of this app. The app will include live sessions and offline learning with learning material in the SQL database synced with the front end. You can further extend the application functionalities by including gamification of AI-based recommendations. For example, you can sync online libraries and databases with the app and provide the students with recommendations based on their browsing and learning history.

2. Online Blood Bank System

This cloud computing blood banking project's goal is to provide real-time updates on the availability of blood as per the blood group details. City blood banks have deployed their websites and mobile apps to search for blood based on the blood group. 

The system will be deployed as a website using the public cloud model. For this online blood bank system project, you can use PHP as the programming language to develop the front end and MySQL for data storage. You can also include chatbots in this system to provide instant updates to the patients and their families on the availability of blood.

3. Online Book Store System

This one comes under the public cloud computing projects category, and its goal is to create an online bookstore system. Amazon started as an online bookstore and then expanded to a multi-national retail firm offering thousands of product categories. Independent bookstores and Oxford bookstores are online bookstore systems deployed as a public cloud service.

You can work on this project using ASP.Net or C# as the programming language and store the database sets in the SQL database. The bookstore system will include the book title, author, description, cost, and availability status. You can deploy the functionalities like login, register, browse books, search books, buy books, cancel orders, track orders, and log out on this system. You can sync the book inventory management in the back end to provide the real-time availability status of the books. To further enhance the system functions, you can integrate discussion forums and social media integration for the bibliophiles to interact with one another.

4. Build A Smart Chatbot

With this project, you can develop cloud computing and Artificial Intelligence skills. The goal is to provide real-time and instant replies to the queries put forward by the customers. E-commerce websites, such as Amazon, have this feature enabled. Also, food delivery apps, such as Zomato, have implemented chatbots to provide instant customer questions.

You can use retrieval-based or generative-based models to work on the chatbot application. You must pre-define the input patterns to deploy the chatbot function on a commercial website. You can include the list of responses and map them with the keywords and questions. You can also work on the chatbot application using sequential neural networks, but the answers will not be pre-defined in this case.

Cloud Computing Projects For Intermediate

Below are a few intermediate cloud computing projects for those familiar with this technology and looking to understand better how to build a cloud computing project-

5. Taxi Service Data Analysis

The cloud project aims to analyze the data of cab service to assist the organization's ineffective strategy development and decision-making. Popular taxi services like Uber and Ola use such cloud-based analytics applications for data-driven decision-making.

With this project, you can acquire and improve your Cloud Computing and data analytics skills. Many cab and taxi services function based on a mobile app. You can easily acquire the data from such an application to plot a passenger's trip in a day or over a month. You can then develop analytics codes and algorithms to provide the statistics based on individual passenger/driver data or location-based analysis. You can generate a heatmap collecting all the information and create a map of a particular city's rides.

6. Secure Text Transfer Application

This is one of the most innovative intermediate-level cloud project ideas. The project aims to securely share textual information to ensure privacy, confidentiality, and integrity. Many banking applications use secure data exchange apps and functions to share information using certain channels.

Data security and cloud computing are the areas focussed on in this project. You will use encryption as the security technique to preserve the information properties. Diffie-Hellman key exchange is a suitable algorithm for carrying out encryption and decryption. This is because it will serve for private and public keys involved in encryption and decryption. The two-way encryption technique used in the project will strengthen security. You can also exchange images securely by utilizing the application. Using SQL database for data storage is recommended as it has built-in security tools and features. You can use Azure cloud servers to activate the entire process.

7. Cloud-based Bus Pass System

This cloud-based project aims to maintain 24x7 availability for commuters to obtain a bus pass for a hassle-free commute. Numerous public transport services now use a cloud-based ticketing system to streamline the service and offer a better experience.

In this cloud-based project, you can simultaneously work on Cloud Computing and Internet of Things (IoT) technology. RFID tags and sensors are the primary elements in the project, and you can develop a cloud-based application to scan the RFID tags on the bus pass. The application shall include functionalities such as recharging the bus pass, renewing the pass, updating the pass, checking the balance, etc. You shall include the option to pay using credit/debit cards, e-wallets, or net banking for the payment gateway.

8. University Campus Online Automation

This is an interesting cloud computing project for engineering students. The goal is to automate university campus operations to streamline student enrolments and registrations, attendance management, class scheduling, and grading functionalities. All the major universities worldwide, such as New York University, University of Sydney, etc., implement cloud-based systems for managing campus activities.

You can use Java and SQL Server as the programming language and database for the front-end and back-end of the system, respectively. The end-users of this system will be the students, admin, and faculty. You can replicate one or multiple campus activities of your college or university. For example, you can focus on the talent management and placement cell, including student registration, posting vacancies, applications for an open vacancy, and status updates. You can also combine multiple university campus activities, such as training and placement, student enrolments, attendance management, and class schedule.

Kickstart your data engineer career with end-to-end solved big data projects for beginners.

Advanced Cloud Computing Projects For Experienced

Below are three advanced cloud computing projects for those looking to become an expert at using cloud technology-

9. Rural Banking Using Cloud Computing

If you are looking for any major projects on cloud computing, then you should work on this rural banking cloud project. This project aims to develop a cloud-based banking system for rural areas wherein banking facilities and amenities are not up to the mark to offer ease of banking to the people residing in rural areas. Regional rural banks, rural bank apps, and Agri rural banks are real-world rural banking cloud apps.

You can work on this rural banking project with the public cloud as the delivery model. PHP can be the preferred programming language to develop the application owing to the robustness and flexibility offered by the language. Functional and non-functional requirements will be crucial for this cloud-based project. You can include the functionalities like login, registration, open account, view balance, account statement, transfer funds, query, and logout.

On the non-functional side, you must prioritize security, usability, and availability as the primary system qualities. For protection, you can include one-time passwords for login to improve access control and authentication. You shall also use digital signatures and data encryption algorithms in the system, such as Advanced Encryption Standard (AES).

10. Android Offloading Computing Over Cloud

This cloud-based project aims to prevent automated offloading used by application developers. Many application developers prefer to have access to such an application to design better mobile and web apps using the Android framework.

With this framework, you can offer the end-users the ability to improve the application capabilities based on static analysis. Users can select a specific process or a file for the process as a cloud with encryption. The timestamp will be calculated based on the selections. The application will then make an automated analysis and offload the parts as required. Timestamp statistics will enable the organization to make data-driven decisions. One of the critical areas you must consider is that the application will work and respond based on the data provided. You must maintain and improve the data quality at all times.

11. Secure File Storage Using Hybrid Cryptography

This secure file storage system project aims to safeguard files using hybrid Cryptography. Banking applications and systems use such applications for detecting data leaks and protecting databases or any crucial information.

You can use Blowfish to encrypt the files, as it can perform encryption with utmost accuracy and speed. For decryption, we suggest using symmetric algorithms. The hybrid technique can offer exceptional cloud security even on the remote server. After working on such data security cloud projects, you can add data security to your skillset, which is high on-demand due to the increased frequency of security risks and attacks. The application of cryptography will convert the data sets into unreadable formats. The file storage system will embed the security key in an image by LSB so that the security of the key is never compromised.

12. Cloud-Based Smart Traffic Management

This is one of the easiest cloud computing project ideas you can explore. This smart traffic management project aims to obtain real-time traffic data and manage traffic to avoid road congestion. Cloud computing and Big Data are the primary technologies used in this application. You can work on this cloud-based project to improve the traffic infrastructure, improve response time, and reduce traffic congestion. Wireless sensor networks will also be involved, along with location-based services.

You shall work on vehicle routing algorithms and predictive analytics techniques to determine the bottlenecks and suggest real-time route optimization details. The use of wireless networks will provide access to location details and real-time traffic information. Use the Hadoop ecosystem to implement the three-layer framework comprising open-source components. Decision-making and support will be performed using data mining and feature extraction. These will then be implemented over a web app for the end-user to access the system.

Cloud Computing-Based Mini Projects

Looking for some exciting and unique cloud computing mini projects? Here are a few unique and simple cloud-computing projects for students-

13. Serverless Website on AWS

This cloud computing project idea aims to develop a secure and usable serverless website using Amazon Web Services. AWS cloud services are used in numerous business and industrial applications.

You can use AWS DynamoDB, Lambda, and S3 to work on this cloud-based project. Serverless cloud computing design will enable you to quickly develop and launch the website. You can create an e-commerce website using these services, or if you are a student, develop a website for your college/university. You can also work on public utility projects using these cloud services offered by Amazon. Scalability, security, and availability will be offered as in-built qualities using AWS.

14. E-bug Tracker

This is one of the most exciting cloud computing project ideas for students. This cloud-based project aims to detect and track the type and origin of a bug on a website or an app. Backlog and Zoho bug tracker are real-world applications designed on similar lines.

You can include three modules in this project- admin, staff, and customer. Admin can immediately contact the staff members and the customers if a bug is detected and implement the corresponding solutions. Customers and staff members can send the details of the bug to the admin. Python is recommended as the language to work on this project. In this app, you can include bug case flow status details and share real-time updates with staff members and customers.

15. Personal Cloud with Raspberry Pi

This personal cloud project aims to give developers an in-depth understanding of the cloud server and its functioning. Cloud servers are used as public, private, and hybrid clouds for personal and business applications. In this cloud-based project, you will build a personal cloud server. You will need Raspberry Pi and a Micro SD card to develop a private cloud, and the hard drive will act as the primary cloud storage in this private cloud project.

Cloud Computing Projects With Source Code For Final Year Students

Below are three innovative cloud computing projects for final year students looking to gain cloud computing skills-

16. Data Leaks Detection Project

To build the Data Leaks Detection Project, you will acquire or generate a sample dataset containing sensitive information, such as personal identifiable information (PII) or credit card numbers. Next, use cloud storage services like Azure Blob Storage or Google Cloud Storage to store and manage the dataset securely. You will employ cloud-based data analytics services like Azure Data Factory or Google Cloud Dataflow to identify patterns and anomalies. To detect potential data leaks, you will use machine learning algorithms like supervised classification (e.g., logistic regression, random forests) or unsupervised techniques (e.g., clustering, anomaly detection) offered by Azure Machine Learning or Google Cloud AI Platform. You can easily integrate cloud-based monitoring tools like Azure Monitor to continuously monitor data access and activities, helping identify potential data breaches.

Source Code- Data Leaks Detection Project

17. Data Redundancy Removal System

You will start building the Data Redundancy Removal System by obtaining a sample dataset with duplicate or redundant records. Use cloud-based storage services like Azure Blob Storage, or Google Cloud Storage to store and manage the dataset securely. You will employ cloud-based data analytics services like Azure Data Factory, or Google Cloud Dataflow to preprocess the data, identify duplicate records using algorithms like Levenshtein distance or cosine similarity, and remove redundant entries. Consider using distributed computing and parallel processing capabilities provided by cloud platforms like Azure HDInsight, or Google Cloud Dataproc for large-scale data redundancy removal. You can further implement version control and logging using cloud-based monitoring tools like Azure Monitor or Google Cloud Monitoring.

Source Code- Data Redundancy Removal System

18. Remote-Controlled Smart Devices

You will start building this exciting cloud project by selecting various smart devices, such as smart bulbs, plugs, or thermostats, that support remote control and integration with cloud platforms. Next, you will connect these devices to a cloud-based IoT service, like AWS IoT Core or Azure IoT Hub, to enable remote control and management. You will use cloud-based services like AWS IoT Analytics or Azure Stream Analytics to process and analyze the data generated by the selected smart devices and gain valuable insights into device usage, energy consumption patterns, and user behavior. Build a web or mobile application using cloud-native development tools such as AWS Amplify or Azure App Service to interact with smart devices through the IoT service, allowing users to control their devices from anywhere remotely.

Source Code- Smart Home System

Machine Learning And Cloud Computing Projects Using AWS

Below are a few exciting machine learning cloud-based projects using AWS you should practice-

19. Real-time Social Media Sentiment Analysis

To build this interesting cloud computing project, you will use AWS Kinesis to ingest and process real-time data from social media platforms like Twitter, Facebook, and Instagram. You will then use AWS Comprehend to perform sentiment analysis on the incoming data, classifying it as positive, negative, or neutral. Using the relevant APIs or data connectors, you will use AWS Kinesis to create a data stream to collect and store data from social media platforms like Twitter. 

You can also use publicly available social media datasets, such as the Sentiment140 dataset, which contains 1.6 million tweets labeled as positive or negative. You can also use AWS Lambda to trigger the Kinesis stream when new data is available. Next, you will use AWS Comprehend to analyze the sentiment of the incoming data in real-time, as it can identify the sentiment of the text and key phrases, entities, and other relevant information. You will use AWS QuickSight to create dashboards that show the sentiment distribution of the data, along with any other relevant metrics.

20. E-commerce Predictive Analytics

This is one of the most beginner-friendly cloud computing project ideas. You first need to identify and collect relevant data from an e-commerce platform. You can use sample datasets provided by AWS, such as the publicly available Amazon Customer Reviews Dataset, or create a dataset using an e-commerce platform's transactional data. You will store the data in Amazon S3 and then use Amazon Redshift to analyze and transform the data to prepare it for further processing. Next, you will use Amazon SageMaker to build and train machine learning models to predict customer behavior, such as product preferences or purchase patterns. You will use logistic regression or Random Forest algorithms to build the ML models. You can also optimize the models using SageMaker's automatic model-tuning feature. After training the models, you can deploy them using Amazon SageMaker's hosting services to predict customer behavior based on new data. Finally, you will use Amazon QuickSight to visualize the insights from predictive analytics and create interactive dashboards to analyze customer behavior trends and patterns.

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21. Personalize Marketing Campaigns With Customer Segmentation

Customer segmentation is a key marketing strategy for personalizing promotions and campaigns for different customer segments. In this AWS cloud computing project, you will leverage the power of AWS services like AWS Glue, AWS Redshift, and Amazon SageMaker to perform customer segmentation and personalize marketing campaigns. You will first extract and process customer data from numerous sources, such as CRM systems, marketing automation tools, and web analytics platforms, using AWS Glue. You will define data extraction and transformation jobs in AWS Glue to clean and prepare the data for further analysis. Next, you will store the processed data in Amazon S3 and use AWS Redshift to perform data analysis and segmentation. You can build and manage a data warehouse with AWS Redshift, and it allows you to run advanced SQL queries to analyze data. To generate targeted customer segments, you will use SQL queries to group customers based on several factors, like demographics, purchasing patterns, or engagement. You will use Amazon SageMaker to create and deploy machine learning models for forecasting customer behavior after creating customer segments. You can use machine learning algorithms, such as decision trees, logistic regression, or clustering algorithms, to train and deploy the ML models on Amazon SageMaker.

Big Data And Cloud Computing Projects With SQL

Here are a few innovative cloud computing project ideas with SQL you might want to explore-

22. Build A Cloud-based Data Warehousing Solution

This is one of the easiest cloud-based projects using SQL. In this cloud-based project, you will build a cloud-based data warehousing solution using SQL and cloud computing services. You can use public datasets like New York City Taxi and Limousine Commission (TLC) trip data or the World Bank's Climate Change Knowledge Portal. You will then use cloud computing services like Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage to store and manage the data. Next, you will create a database schema using SQL. You can use tools like SQL Workbench, DBeaver, or Aqua Data Studio to create and manage the database schema. You will use cloud computing services like Amazon Redshift, Google BigQuery, or Microsoft Azure SQL Data Warehouse to create the data warehouse. Once you have created the data warehouse, you will use SQL to load the data into the database and perform analysis. You can use SQL queries to aggregate, transform, and visualize the data. You can also use ML algorithms like regression or clustering to gain insights from the data.

23. Build A Cloud-Based Analytics Dashboard

Creating a cloud-based analytics dashboard using SQL-based cloud services like AWS QuickSight or Google Data Studio is an effective technique for analyzing and visualizing data from multiple cloud sources. In this cloud computing SQL project, you will build an analytics dashboard using AWS QuickSight. You can use datasets from sources like Google Analytics, Facebook Ads, or Salesforce, commonly stored in cloud-based databases such as Amazon RDS, Google Cloud SQL, or Microsoft Azure SQL Database. You must use SQL queries or APIs for data retrieval to connect to these databases. You will then use cloud computing services like Amazon S3 or Google Cloud Storage to store and manage the data. Next, you must create a data source in AWS QuickSight and connect to it using AWS Glue to extract and transform the data. You will then create a data model using SQL to prepare the data for analysis. You can also use tools like Apache Spark or AWS Athena to transform and analyze the data. Once the data is ready, you can create visualizations using various chart types and customizations to create interactive dashboards in AWS Quicksight.

24. Build A Cloud-Based IoT Data Processing System

For this exciting cloud computing project, you will develop a cloud-based IoT data processing system using SQL-based cloud databases like AWS IoT Core or Google Cloud IoT Core. You can use publicly available IoT datasets like the IoT Sensor Dataset from UCI Machine Learning Repository, which contains sensor data from temperature, humidity, light, and carbon dioxide sensors in a university building. You will first collect and store the data using cloud computing services like AWS IoT Core or Google Cloud IoT Core. Next, you will use tools like SQL Workbench, DBeaver, or Aqua Data Studio to create and manage the database schema. You will then use cloud computing services like Amazon RDS or Google Cloud SQL to create the SQL-based cloud databases. Once you have created the cloud databases, you will use SQL queries to analyze the sensor data stored in the cloud databases. For instance, you can use aggregate functions to calculate average, maximum, or minimum values for temperature and humidity over a specific period. You can also perform data transformations and joins to combine data from multiple sensors or sources.

Best Cloud Computing Projects In Python With Source Code

Here are a few fascinating cloud-based projects in Python with source code for those willing to explore cloud-based projects using Python programming language-

25. Covid-19 Data Querying Using Python and AWS

This is one of the easiest AWS cloud computing project ideas you can practice. In this cloud-based project, you will use AWS Athena, a serverless SQL query engine, to analyze the Covid-19 database that contains timestamps, posts, and comments related to Covid. You will learn to use AWS Glue to generate tables and experiment with different Athena joins. You will build tables using Python and crawlers in the AWS Glue Data Catalogue. Working on such a project will give you a better understanding of how the pricing of AWS Athena varies according to file size. Other services used in this cloud-based project include Amazon S3, Amazon CloudWatch, etc. You will store the dataset (CSV file) in S3 buckets using AWS S3, and CloudWatch monitors the log files for your data and allows you to analyze them as and when necessary. 

Source Code: Covid-19 Data Querying Using Python and AWS

26. Explore Cloud Functions using Python And GCP

In this beginner-friendly cloud computing project, you will explore the Cloud Services of GCP, such as Cloud Storage, Cloud Engine, and PubSub. This project will introduce you to the Google Cloud Console and teach you the Cloud Storage concepts and classes. You will start working on this cloud-based project by installing Python and other dependencies, and then you will learn how to create a Service Account and set up Gcloud SDK. This project will show you how to set up GCP Virtual Machine and SSH configuration. Working on such a project will give you a better understanding of the Pub/Sub Architecture, Pub/Sub Topic, and the implementation of Pub/Sub notification using GCS.

Source Code: Explore Cloud Functions using Python And GCP

27. Build Serverless Pipeline Using AWS CDK And Lambda In Python

This interesting cloud computing project will teach you how to build a serverless pipeline using AWS CDK and other AWS serverless technologies like AWS Lambda and Glue. You will get a better understanding of AWS CDK and its various commands. You will create an AWS Cloud9 environment and then clone the GitHub repository in the AWS Cloud9 environment. This cloud-based project will help you better understand the Lambda stack, the Glue pipeline stack, and the Glue database stack. Once you deploy the AWS CDK pipeline, you will perform further analysis using Amazon Athena and create visualizations using Amazon QuickSight.

Source Code: Build Serverless Pipeline using AWS CDK and Lambda in Python

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Cloud Computing Engineering Projects

Below are some top cloud computing projects for final-year students you can practice to become an expert at using cloud technology-

28. Movie Recommendation Engine Using AWS

One of the most exciting cloud-based project ideas is the movie recommendation engine. This cloud engineer project entails building a cloud-based recommendation engine using Amazon SageMaker and Amazon EC2. Use a sample dataset that contains user behavior data, such as the MovieLens dataset, which contains movie ratings and user behavior data. You will store the dataset in Amazon S3 and use Amazon Glue to extract, transform, and load the data from S3 to Amazon SageMaker. You will build the recommendation model in Amazon SageMaker using the factorization machines approach since it efficiently processes large datasets and can provide users with accurate predictions. You will also use Amazon EC2 to create a scalable web service that provides real-time recommendations to users based on their behavior. You will deploy the recommendation engine using Amazon API Gateway to create a RESTful API that interfaces with the recommendation model.

29. Titanic Survival Prediction Using Azure Data Lake And Machine Learning

Working on this interesting cloud-based project will help you understand the Azure cloud capabilities in detail. For this Azure cloud computing project, you can use the Titanic dataset, which contains information about passengers on the Titanic and whether they survived. You will first store the dataset in the Azure Data Lake Storage, clean and transform it using tools like Azure Databricks, and then store it in a structured format using Azure SQL Database. You will use Azure Data Lake Analytics to perform data analytics and exploratory data analysis on it. After cleaning and transforming the data, you can build ML models using Azure Machine Learning. You can use various algorithms like linear regression, decision trees, neural networks, etc., to predict whether a passenger will survive or not based on their characteristics.

30. Building A Big Data Processing Pipeline Using GCP

In this cloud engineer project, you will build a big data processing pipeline using GCP services like Google Cloud Dataflow, Google BigQuery, and Google Cloud Storage. For this cloud computing project, you can use publicly available datasets like the New York Taxi or Wikipedia datasets. You will store the dataset in Google Cloud Storage and then process it using Google Cloud Dataflow. You will use Apache Beam to write your data processing pipelines in Java or Python. You will store the processed data in Google BigQuery for easy querying and analysis. You will also use Google Data Studio to create interactive visualizations and dashboards to explore your data. You can also employ ML algorithms to perform predictive analytics on the data to improve the data processing pipeline in this cloud-based project. You will create and deploy your models using Google Cloud ML Engine, a managed service for creating and training machine learning models.

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Cloud Computing Projects GitHub

Below are some cloud project ideas from Github for those willing to try their hands on some unique cloud-based projects-

31. Build Data Analytics Pipeline Using Amazon Data Migration Service (DMS)

This Github project will offer you cdk scripts and sample code on implementing end-to-end data pipelines for replicating transactional data from MySQL DB to Amazon OpenSearch Service through Amazon Kinesis using Amazon Data Migration Service (DMS). You will create an Aurora MySQL Cluster and Amazon Kinesis Data Streams for the AWS DMS target endpoint. You will create a sample cloud database (i.e., testdb) and table (retail_trans). Next, you will deploy Amazon OpenSearch Service and Amazon Kinesis Data Firehose. You will remotely access the Amazon OpenSearch Cluster using SSH tunnel.

Source Code: Build Data Analytics Pipeline Using Amazon DMS

32. Covid Tracking Pipeline Using Azure Data Pipelines And Azure Synapse

This is one of the most common and interesting cloud project ideas in Github. You will use the Covid tracking dataset from the Azure Open Datasets collection for this cloud-based project. You will learn how to use and easily customize this prepackaged ingestion template for data ingestion and transformation purposes. You will download and move the pipeline template to Blob Storage. After loading and debugging the pipeline, both (raw and curated) datasets will be created/copied to the destination path you specify on the Azure Data Lake Store account. Next, you will download and import the Synapse notebook from Azure Open Datasets into Azure Synapse. Add the creation of a Spark Database in the notebook and save the dataset as Table. Due to the shared metadata, you can access this Spark-created table using SQL On-demand.

Source Code: Covid Tracking Pipeline Using Azure

33. NYC Service Request Data Analysis Using GCP And Docker

This cloud-based project aims to create a batch data pipeline that continually extracts, transforms, and loads data from NYC 311 Service Requests into a data warehouse while enabling you to visualize key insights. You will use the 311 dataset from the NYC Open Data portal for this project. You will use the Python Pandas library to fetch data from the Socrata API, transform it into a dataframe with appropriate data types, and load it to BigQuery. You will use Terraform to easily manage infrastructure setup and changes and Docker to containerize the code. You will learn how to execute the Prefect flows in a serverless execution environment using Cloud Run Jobs. For this project, you will use Google BigQuery as the data warehouse and Google Looker Studio to build a dashboard. You will also use Prefect OSS and Prefect Cloud to orchestrate, monitor and schedule the server deployment process.

Source Code: NYC Service Request Data Analysis Using GCP And Docker

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Azure Cloud Computing Projects

Here are a few fascinating cloud-based projects in Azure for those willing to explore cloud-based Microsoft Azure projects-

34. Attendance System

The first step in the Cloud-based Attendance System is to set up an Azure cloud platform to host the system. Then, you can use RFID cards or biometric devices to capture attendance data. When an employee or student scans their card, the system will record their in-time, ID number, and out-time. You can use Azure SQL Database to store attendance records securely in the cloud. This will enable easy access and management of attendance data for administrators. To manage user authentication and access control, you can use Azure Active Directory. For real-time tracking of attendance and generating reports, you can employ cloud-based data analytics services such as Azure Functions. These serverless functions can process attendance data and provide insights into how many hours an employee or student has spent on the premises. To ensure data accuracy and optimize system performance, you may use machine learning algorithms for anomaly detection or data validation. For instance, you could apply outlier detection algorithms like Isolation Forest or One-Class SVM to identify abnormal attendance patterns.

35. Bus Ticketing And Payment System

You can start building this Azure-based cloud project by setting up an Azure cloud environment to host the system. Then, design a user-friendly web or mobile application that allows customers to browse, choose, and buy tickets. For the backend, you will use Azure Functions to handle ticket bookings and process payments securely with Azure Key Vault for encryption. You will manage ticket inventory and schedules using Azure SQL Database or Azure Cosmos DB for data storage. To ensure smooth ticket transactions, you will integrate Azure Active Directory for user authentication and access control. For data analytics and insights, you will use Azure Application Insights to monitor system performance and user behavior. Additionally, you can use Azure Machine Learning to analyze ticket booking patterns and forecast demand during peak times, enabling efficient resource allocation.

36. Host A Dynamic Website Using Azure

To host a dynamic website using Azure services, you will start by developing the website using HTML, CSS, and JavaScript. You will use a server-side technology like ASP.NET, Node.js, or Python with Flask for dynamic content. You will deploy the website on Azure App Service that scales automatically to accommodate traffic. For data storage, you will use Azure SQL Database or Azure Cosmos DB to store dynamic content, user data, or any other relevant information. You must also ensure data security by integrating Azure Active Directory for user authentication and access control. To enhance website performance and user experience, you will use Azure Content Delivery Network (CDN) to cache and deliver content from nearby servers. Additionally, integrate Azure Application Insights to monitor website performance, identify bottlenecks, and track user behavior.

AWS Cloud Computing Project Ideas

Below are a few unique AWS cloud computing project ideas if you are more comfortable working with AWS-

37. Host A Static Website Using AWS

To build this cloud-based project, you must start by designing and developing the static website using HTML, CSS, and JavaScript. Use cloud storage services like Amazon S3 to host the website's static files, ensuring high availability and scalability. You will leverage Amazon CloudFront as a content delivery network (CDN) to cache and distribute website content to users from edge locations. You must also register a domain name through Amazon Route 53 and configure DNS settings to route traffic to the S3 bucket hosting the website. Implement Amazon Certificate Manager (ACM) to secure the website with SSL/TLS encryption.

38. Repair Techniques For Transportable Storage Devices

To build this innovative project using cloud computing, you can start by creating a dataset containing various issues faced by users with storage devices, like HDD failures or data corruption. Use cloud-based storage services like AWS S3 to store and manage this dataset securely. You must leverage cloud-based data analytics services like AWS Glue for data analysis and repair techniques. You can use algorithms for data recovery and repair, such as RAID configurations or error correction codes (ECC), offered by cloud-based machine learning platforms like AWS SageMaker. 

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Cloud computing has evolved as a crucial tool for professionals willing to advance their careers and stay ahead of the competition in the big data industry. With the increasing demand for cloud skills in the industry, gaining hands-on experience working on cloud projects is crucial to showcase your abilities and stand out from the crowd. ProjectPro offers over 250 end-to-end solved data science and big data projects, including various cloud computing projects that allow you to work with popular cloud platforms like AWS, Azure, and GCP and gain practical knowledge of the latest big data tools and other technological advancements. By working on these projects from the ProjectPro repository, you can showcase your ability to design, develop, and deploy cloud-based systems to recruiters. If you are a big data professional aiming to improve your cloud skills, explore cloud computing projects by ProjectPro to gain valuable experience and enhance your career prospects. Start your journey toward becoming a cloud expert today!

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FAQs on Cloud Computing Projects

Below are the steps to develop cloud computing projects-

  • Define the project scope and requirements.

  • Choose a cloud computing platform, such as AWS or Azure, and select the required services.

  • Set up the necessary infrastructure and resources, including networking, storage, and virtual machines.

  • Build the application or solution using programming languages, frameworks, and tools.

  • Test the application or solution before deploying it to the cloud platform.

  • Use cloud monitoring and management tools to track and improve the application's or solution's efficiency and cost.

  • Ensure data privacy, security, and compliance by following best practices and industry standards.

  • Consider user feedback and business requirements to enhance the application or solution continuously.

Some common use cases for cloud computing in projects include data management and storage, application development and deployment, disaster backup and recovery, virtual desktops and workspaces, artificial intelligence, and machine learning.

You can choose the right cloud computing provider for your project by considering the project's requirements, budget, scalability needs, and support for the programming languages and frameworks you want to use. Additionally, consider the previous track records of several providers for reliability, pricing and payment options, security and compliance features, scalability, adaptability, and accessibility to the various tools and services you require for your project. 

The different cloud computing services available for projects include Software as a Service, Infrastructure as a Service, and Platform as a Service.

 

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