Top 40+ Cloud Computing Projects to Boost Your Cloud Skills

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!

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. According to a recent report by Meticulous Research, the global cloud computing market will likely reach $1,402.7 billion by 2030, at a CAGR of 16.8% from 2023 to 2030. Another survey shows over 90% of professionals use cloud services daily for business operations. This shift presents abundant career opportunities, especially in big data and cloud computing, as businesses increasingly rely on cloud technologies. Therefore, gaining hands-on experience through practical projects in cloud computing is now essential for anyone looking to excel in this field. 

This blog invites you to explore the best cloud computing projects 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 projects will give you adequate exposure and experience in cloud technologies and other essential skills, such as data analytics, business intelligence, and analytical abilities. But why go to lengths and work on such projects? Here are a few pointers to motivate you:

• Cloud computing projects provide access to scalable computing resources on platforms like AWS, Azure, and GCP, enabling a data scientist to work with large datasets and complex tasks without expensive hardware.

• Working on cloud computing projects offers hands-on experience with cutting-edge tools like AWS Glue, Amazon S3, and Redshift, enhancing data extraction, storage, and analysis skills, giving a competitive edge in the job market.

• Projects allow students and professionals to develop expertise, gain practical experience, and increase chances of career advancement and job opportunities.

Besides these, it is essential to remember that cloud computing is a bonus skill as you can use your existing skills to build projects like Java cloud computing projects, Android cloud computing projects, cloud computing projects in PHP, or any other popular programming language. Prasad Rao, principal solutions architect at AWS, highlights the same in this post:

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

40+ Best Cloud Computing Projects For Practice In 2024

Below is a list of unique cloud projects divided into various categories for a smoother browsing experience. You can pick any of these cloud computing project ideas to develop and improve your skills in the field of cloud computing along with other big data technologies. 

1) Hosting A Static Website

2) Repair Techniques For Transportable Storage Devices

3) Migration of MySQL Databases to Cloud AWS

4) Website Monitoring

5) Real-time Sentiment Analysis

6) E-commerce Predictive Analytics

7) Serverless Pipeline

8) Covid-19 Data Querying System

9) Movie Recommendation Engine

10) Customer Segmentation

11) Attendance System

12) Bus Ticketing And Payment System

13) Hosting A Dynamic Website

14) Real-Time Streaming

15) Web Server Log Processing

16) Data Governance

17) Remote-Controlled Smart Devices

18) Passenger Survival Prediction

19) Explore Cloud Functions

20) Analytics Dashboard

21) IoT Data Processing System

22) Data Redundancy Removal System

23) Big Data Processing Pipeline

24) Online Blood Bank System

25) Online Book Store System

26) Smart Chatbot

27) Taxi Service Data Analysis

28) Secure Text Transfer Application

29) University Campus Online Automation

30) Rural Banking

31) Android Offloading

32) Hybrid Cryptography

33) Smart Traffic Management

34) E-bug Tracker

35) Personal Cloud with Raspberry Pi

36) E-Learning App

37) Data Warehousing

38) Data Analytics Pipeline

39) Covid Tracking Pipeline

40) NYC Service Request Data Analysis

AWS Cloud Computing Project Ideas

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

1) Hosting A Static Website 

A static website consists of web pages whose content remains fixed and unchanged unless manually updated by a developer. It typically showcases basic information without dynamic or interactive elements, such as text, images, and multimedia. These websites are more straightforward to create and cost-effective to host and are suitable for displaying content that does not require frequent updates or user interaction. AWS is well-suited for hosting static websites, offering scalable storage with Amazon S3 and enhanced performance through CloudFront.

Project Idea: To build this AWS project, start designing and developing the static website using HTML, CSS, and JavaScript. Then, the cloud storage service Amazon S3 will host the website's static files, ensuring high availability and scalability. Amazon CloudFront can be used 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.

Services Used: Amazon CloudFront, AWS S3

2) Repair Techniques For Transportable Storage Devices

Consider a scenario where a photographer's external hard drive containing years of valuable work suddenly fails. The need for repair techniques for transportable storage devices becomes evident as the photographer risks losing irreplaceable photos. In this situation, repair techniques such as connector replacement or data recovery software are essential to retrieve the lost data and restore functionality to the storage device. These techniques ensure that critical data remains accessible and protected, highlighting their importance in safeguarding valuable information stored on transportable storage devices.

Project Idea: Create a dataset containing users' issues with storage devices, like HDD failures or data corruption. Cloud-based storage services like AWS S3 can be used to store and manage this dataset securely. Use AWS Glue for data analysis and repair techniques. Implement algorithms for data recovery and repair, such as RAID configurations or error correction codes (ECC) offered by AWS SageMaker. 

Services Used: AWS Glue, AWS S3, AWS Glue, AWS SageMaker

Start your AWS journey with ProjectPro’s AWS Learning Path

3) Migration of MySQL Databases to Cloud AWS

Migrating MySQL databases to AWS offers numerous benefits. It enhances scalability, seamlessly allowing databases to adjust to changing workloads and storage requirements. AWS's robust security measures ensure data protection and compliance with regulatory standards. Additionally, it improves reliability and reduces downtime through automated backups and high-availability features. With AWS's managed services, businesses can focus on innovation rather than database management, leading to increased agility and cost savings.

Project Idea: To implement this project, begin by familiarizing yourself with AWS migration services, especially AWS DMS. Implement a Bastion Host for data security and use AWS SCT for schema conversion. Create an Aurora Postgres instance using RDS and deploy DMS SCT between MySQL and Postgres. Migrate database elements, analyze migration data, and load it into AWS S3. Utilize Glue to create a Data Catalog, query data with Athena, and prepare data for Timestream. Finally, load data into Amazon Timestream DB and visualize geospatial data using QuickSight for comprehensive insights.

Source Code: Migration of MySQL Databases to Cloud AWS using AWS DMS 

Services Used: AWS DMS, AWS Athena, Amazon Timestream DB, Amazon QuickSight, AWS SCT.

4) Website Monitoring

Website monitoring involves continuously tracking a website's performance and availability. It checks server uptime, page load times, and transactional processes to ensure optimal functionality. Website monitoring helps minimize downtime and maintain a positive user experience by promptly detecting issues. This project involves utilizing AWS services for website monitoring. 

Video: https://www.youtube.com/watch?v=_vKUU5SmJSw 

Project Idea: The solution includes deploying EC2 instances for server management and serverless computing, incorporating MySQL databases with Amazon RDS and Aurora, and implementing real-time data streaming via Amazon Kinesis. You will learn how to implement data analytics with Kinesis Data Analytics, log streaming, and notification services using Amazon SNS. The project emphasizes end-to-end testing of AWS Lambda functions and integration with DynamoDB for data storage. 

Services Used: Amazon DynamoDB, Amazon Kinesis, AWS Lambda, AWS EC2, Amazon RDS, Amazon Aurora, Amazon SNS

Source Code: AWS Project-Website Monitoring using AWS Lambda and Aurora 

5) Real-time Sentiment Analysis

Real-time sentiment analysis enables businesses to gauge customer opinions from social media and reviews swiftly. This immediate insight supports proactive decision-making, identifying emerging trends, addressing concerns promptly, and optimizing marketing strategies, ultimately enhancing customer satisfaction and brand reputation for sustainable business growth. Cloud computing facilitates real-time sentiment analysis by providing scalable, on-demand computing resources for processing vast amounts of text data, enabling businesses to derive actionable insights quickly and efficiently.

Project Idea: Start by using relevant APIs or data connectors along with AWS Kinesis to create a data stream to collect and store data from social media platforms like Twitter. If you do not want to scrape data, you can use publicly available social media datasets, such as the Sentiment140 dataset. Next, use AWS Lambda to trigger the Kinesis stream when new data is available. After that, utilize 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. Finally, AWS QuickSight can be used to create dashboards that show the sentiment distribution of the data, along with any other relevant metrics.

Services Used: AWS Kinesis, AWS Lambda, AWS QuickSight

6) E-commerce Predictive Analytics

E-commerce predictive analytics uses data analysis and machine learning algorithms to forecast customer behavior, trends, and outcomes in online retail settings. By analyzing past data and patterns, businesses can make informed decisions regarding inventory management, pricing strategies, and personalized marketing campaigns to optimize sales and enhance customer satisfaction.

Project Idea: To build this project, begin by grasping ETL on Big Data and the concepts of staging and Data Lake. Set up IAM Roles and Policies, then analyze the dataset. Configure AWS CLI and comprehend Data Streams, Amazon Kinesis, and Apache Flink. Create a Kinesis Data Analytics Application and utilize Glue and Athena to define the Partition Key. Understand Lambda Functions, then create functions for DynamoDB and SNS integration. Learn DynamoDB Data Modeling and integrate Lambda with Kinesis. Perform ETL for Parquet format using Glue DataBrew and Spark, and finally, create QuickSight Dashboards for comprehensive data visualization and analysis.

Source Code: Build an Analytical Platform for eCommerce using AWS Services 

Services Used: AWS S3, AWS Glue, AWS Athena, AWS Cloud9, Apache Flink, Amazon Kinesis, Amazon SNS, AWS Lambda, Amazon CloudWatch, QuickSight, Apache Zepplin, Amazon DynamoDB, AWS Glue DataBrew

7) Serverless Pipeline 

A serverless pipeline is a sequence of automated tasks or stages for processing and deploying software applications without the need to manage servers or infrastructure. It leverages cloud servicing tools, like AWS Lambda and Azure Functions, to execute tasks in response to events or triggers, enabling scalable and cost-efficient development workflows.

Project Idea: This 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

Services Used: AWS S3, Amazon Lambda, Amazon Aurora, AWS Glue, Amazon Athena, Quicksight, AWS CDK

8) Covid-19 Data Querying System

A data querying system efficiently retrieves specific information from databases or datasets, enabling users to extract relevant data based on criteria. It supports data analysis, reporting, and decision-making by accessing and manipulating large volumes of data, facilitating insights and informed decisions across industries and domains.

Project Idea: 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. This project is essential if you are looking for Python cloud computing projects.

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

Services Used: AWS S3, Amazon Lambda, Amazon Aurora, AWS Glue, Amazon Athena, Quicksight, AWS CDK

9) Movie Recommendation Engine 

A movie recommendation engine is a system that suggests films to users based on their preferences and behavior. Cloud computing aids recommendation engines by providing a scalable infrastructure to process vast amounts of user data, enabling personalized recommendations and enhancing user experience with on-demand access and seamless scalability.

Project Idea: This cloud engineer project involves 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 includes movie ratings and user behavior data. Store the dataset in Amazon S3 and use Amazon Glue to extract, transform, and load the data from S3 to Amazon SageMaker. 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 offers 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.

Services Used: Amazon S3, Amazon SageMaker, Amazon GLue, Amazon API Gateway, Amazon EC2

Unlock the ProjectPro Learning Experience for FREE

10) Customer Segmentation

Customer segmentation divides a customer base into groups with similar characteristics or behaviors. It gives invaluable insights into user behavior, enabling more targeted and effective marketing campaigns and ultimately enhancing customer engagement and driving business growth.

Project Idea: Use Amazon Glue to extract and process data from CRM, marketing, and analytics sources. Store the processed data in AWS S3, then analyze it using Redshift with advanced SQL queries for customer segmentation. Utilize SageMaker to forecast customer behavior with machine learning models. Train and deploy these models, leveraging SageMaker's built-in algorithms like decision trees or logistic regression. 

Services Used: AWS S3, AWS SageMaker, AWS Glue.

Azure Cloud Computing Projects for Resume

Here are a few fascinating cloud-based projects in Azure to help you understand the Azure cloud capabilities in detail-

11) Attendance System

An attendance system tracks and records the presence of individuals, often employees or students, at a particular location or event. Cloud computing enhances attendance systems by providing a scalable infrastructure to securely store and process attendance data. Utilizing cloud services, such as Azure, enables easy access to attendance records from anywhere, streamlines data management, and facilitates real-time monitoring and reporting. Additionally, cloud computing-based solutions offer flexibility, cost-effectiveness, and integration with other systems, enhancing the efficiency and effectiveness of attendance-tracking processes for organizations of all sizes.

Project Idea: Set up an Azure cloud platform to host the Cloud-based Attendance System. Download a sample dataset that has captured attendance data using RFID cards or biometric devices, recording in-time, ID number, and out-time upon scanning. Store attendance records securely in Azure SQL Database for easy access and management. Utilize Azure Active Directory for user authentication and access control. Use Azure Functions for real-time tracking and report generation. Implement machine learning algorithms like Isolation Forest or One-Class SVM for anomaly detection and validation to ensure data accuracy and system optimization.

Services Used: Azure SQL Database, Azure Functions

12) Bus Ticketing And Payment System

Many public transport services have adopted cloud-based ticketing systems to optimize operations and enhance customer satisfaction. Such systems enable round-the-clock commuter accessibility and ensure convenient access to bus passes, facilitating hassle-free commutes. 

Project Idea: 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, 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.

Services Used: Azure Functions, Azure Key Vault, Azure SQL Database, Azure Cosmos DB, Azure Active Directory, Azure Machine Learning, Azure Application Insights

Master Microsoft Azure with ProjectPro’s Azure Learning Path

13) Hosting A Dynamic Website 

A dynamic website is a type of website that displays different content to users based on their interactions or preferences. Unlike static websites with fixed content that remains unchanged until manually updated, dynamic websites generate content on the fly in response to user input or database queries. They often incorporate features such as user authentication, interactive forms, content management systems, and e-commerce functionalities. Dynamic websites use server-side scripting languages like PHP, Python, or Ruby, along with databases, to dynamically generate and serve personalized content to users.

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

Services Used: Azure App Service, Azure Cosmos DB, Azure Active Directory, Azure Content Delivery,  Azure Application Insights

14) Real-Time Streaming

Real-time streaming in cloud computing involves the continuous transmission of data from a source to a destination, enabling immediate processing and analysis. It facilitates the delivery of live data streams for applications such as IoT, monitoring, and analytics, allowing for rapid insights and timely decision-making.

Project Idea: Create resource groups and configure a compute cluster in Databricks. Then, mount the dataset in Blob using Scala within Databricks. Gain a deep understanding of Structured Streaming to process streaming data effectively. Create an Event Hub and set up a Snowflake account, enabling seamless data streaming from Databricks into the Event Hub and consumption afterward. Finally, the streamed data will be loaded into Snowflake for comprehensive analysis.

Source Code: Databricks Real-Time Streaming with Event Hubs and Snowflake 

Services Used: Azure Blob Storage, Azure Databricks, Azure Event Hubs, Snowflake

15) Web Server Log Processing

Web server log processing involves analyzing log files generated by web servers to extract valuable insights. It includes parsing log entries to gather information such as user requests, IP addresses, URLs accessed, and response codes. This data is used for various purposes, including website performance monitoring, security analysis, and user behavior tracking.

Project Idea: To begin, set up a Virtual Machine (VM) instance in Microsoft Azure and connect to it using Putty. Install different big data tools on the VM. Learn about log files and why processing them matters. Understand terms like "referrer" and "user agent" and the contents of log files—Discover Flume's role in log data processing and ingestion. Finally, Flume and Spark are used to process log data and analyze it with Hive.

Source Code: Web Server Log Processing using Hadoop in Azure 

Services Used: Microsoft Azure, Hadoop, Hive, Flume, Spark

16) Data Governance

Data governance is the framework of policies, processes, and controls that ensure data is managed effectively throughout its lifecycle. It defines roles, responsibilities, and procedures for data management, including data quality, security, compliance, and privacy, to maximize its value and minimize risks within an organization.

Project Idea: To start, create an Azure Purview account for efficient data asset management. Then, create and scan Purview collections to organize and analyze data. Explore the Purview user interface to familiarize yourself with its functionalities. Understand the Purview glossary and assets to ensure accurate data management. Learn about access control in Purview and implement best practices for effective governance. Finally, understand the limitations of Azure Purview to make informed decisions during the project development.

Source Code: Getting Started with Azure Purview for Data Governance 

Services Used: Azure Logic Apps, Azure Storage Account, Azure Data Factory, Azure SQL Databases, DBeaver, Azure Purview

17) Remote-Controlled Smart Devices

Remote-controlled smart devices are Internet-connected gadgets controlled remotely via smartphones or other devices. They use cloud computing for data storage, processing, and remote access. Cloud platforms enable users to control devices from anywhere, access data, receive updates, and integrate seamlessly with other smart home systems.

Project Idea: 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, these devices can be connected to a cloud-based IoT service, like Azure IoT Hub, to enable remote control and management. Use cloud-based services like 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 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

Services Used: Azure IoT Hub, Azure Stream Analytics, Azure App Service

18) Passenger Survival Prediction

The dataset for Titanic Passenger Survival Prediction contains information about passengers aboard the Titanic, including demographics, ticket class, and survival status. It provides valuable insights for analyzing and predicting survival probabilities based on various factors. The Titanic Passenger Survival Prediction project offers beginners valuable hands-on experience in data analysis, machine learning, and cloud computing, enhancing their practical skills and portfolio in deploying data science projects on the cloud.

Project Idea: 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. 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 based on their characteristics.

Services Used: Azure Data Lake Storage, Azure SQL Database, Azure Machine Learning, Azure Data Lake Analytics

GCP Cloud Computing Projects

This section has projects on cloud computing that can be implemented using cloud services provided by Google Cloud Platform.

19) Explore Cloud Functions 

Cloud Functions are event-driven serverless compute services that enable developers to run code without provisioning or managing servers, allowing for rapid development and scaling of applications in response to demand.

Project Idea: In this beginner-friendly cloud computing project, you will explore GCP's cloud services, 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 a 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

Services Used: Google Cloud Storage, Compute Engine, Pub/Sub

20) Analytics Dashboard

Cloud computing simplifies Analytics Dashboard creation by providing scalable infrastructure and managed data processing, storage, and visualization services. It streamlines development and deployment processes while ensuring accessibility and flexibility for users. Creating a cloud-based analytics dashboard using SQL-based cloud services like Google Data Studio is an effective technique for analyzing and visualizing data from multiple cloud sources. 

Project Idea: We suggest building a Google Looker analytics dashboard for this project. You can use datasets from sources like Google Analytics, Facebook Ads, or Salesforce, commonly stored in a cloud-based database- Google Cloud SQL. You must use SQL queries or APIs to connect to these databases for data retrieval. Use Google Cloud Storage to store and manage the data. Next, you must create a data source in Google Looker and connect to it using Google Cloud Dataflow 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 Google BigQuery 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 Google Looker.

Services Used: Google Looker, Google Cloud SQL, Google Cloud Storage, Google BigQuery

21) IoT Data Processing System

Leveraging cloud computing, an IoT Data Processing System efficiently manages and analyzes large volumes of sensor data from connected devices, enabling real-time monitoring, intelligent automation, and seamless integration with other cloud-based services for enhanced functionality and scalability. For this exciting cloud computing project, you will develop a cloud-based IoT data processing system using an SQL-based cloud database- Google Cloud IoT Core. You can use publicly available IoT datasets like the IoT Sensor Dataset from the UCI Machine Learning Repository, which contains sensor data from temperature, humidity, light, and carbon dioxide sensors in a university building. 

Project Idea: First, collect and store the data using Google Cloud IoT Core. Next, use tools like SQL Workbench, DBeaver, or Aqua Data Studio to create and manage the database schema. Use Google Cloud SQL to create the SQL-based cloud databases. Once you have made the cloud databases, 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.

Services Used: Google Cloud IoT Core, Google Cloud SQL

22) Data Redundancy Removal System

With cloud computing, a Data Redundancy Removal System optimizes data storage by leveraging scalable cloud storage solutions and advanced data deduplication techniques. It minimizes duplicate data and maximizes storage efficiency while ensuring data integrity and accessibility.

Project Idea: Build the Data Redundancy Removal System by obtaining a sample dataset with duplicate or redundant records. Cloud-based storage services like Google Cloud Storage can be used to store and manage the dataset securely. You will employ cloud-based data analytics services like 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 Google Cloud Dataproc for large-scale data redundancy removal. You can implement version control and logging using cloud-based monitoring tools like Google Cloud Monitoring.

Source Code: Data Redundancy Removal System

Services Used: Google Cloud Dataflow, Google Cloud Dataproc, Google Cloud Monitoring

23) Big Data Processing Pipeline

A Big Data Processing Pipeline is a series of automated data processing tasks designed on cloud infrastructure. It enables the ingestion, transformation, analysis, and visualization of massive datasets, facilitating timely insights and informed decision-making for businesses across various industries.

Project Idea: 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. Use publicly available datasets like the New York Taxi or Wikipedia datasets for this cloud computing project. Store the dataset in Google Cloud Storage and then process it using Google Cloud Dataflow. Use Apache Beam to write your data processing pipelines in Java or Python. 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. Employ ML algorithms to perform predictive analytics on the data to improve the data processing pipeline in this cloud-based project. Create and deploy your models using Google Cloud ML Engine, a managed service for creating and training machine learning models.

Services Used: Google Cloud Dataflow, Google Cloud ML Engine, Google BigQuery, Google Cloud Storage, Google Data Studio

Easy Cloud Computing-Based Projects For Beginners

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

24) Online Blood Bank System

An Online Blood Bank System is a centralized platform connecting blood donors with recipients, facilitating efficient and timely blood donations. Donors can register, schedule donations, and receive notifications, ensuring a seamless donation process. Many city blood banks have deployed their websites and mobile apps to search for blood based on blood groups. Even Facebook now enables users to set alerts for blood bank requests, enhancing the reach and efficiency of blood donation efforts.

Project Idea: An automated cloud computing blood banking system can be deployed as a website using the public cloud model. The front end can be developed using PHP, and the data storage can be handled using MySQL. Chatbots can also be included in this system to facilitate searching for available blood types, requesting donations, receiving timely assistance, optimizing blood supply management, and saving lives.

25) Online Book Store System

Online bookstores like Amazon revolutionized the retail industry by offering vast selections, convenient browsing, and personalized recommendations, transforming how people discover and purchase books worldwide. These platforms provide access to diverse literature and serve as hubs for community engagement and knowledge dissemination, fostering a culture of reading and learning. Independent and Oxford bookstores are examples of such systems deployed as public cloud services, further expanding access to literature for users everywhere.

Project Idea: You can build an online bookstore system using ASP.Net or C# as the programming language and store the database sets in the SQL database. The bookstore system will include the 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 of 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 so that bibliophiles can interact.

26) Smart Chatbot

Integrating advanced features like chatbots has become increasingly prevalent across various e-commerce platforms and service-oriented applications. 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. All such bots resolve customer queries in real-time, thanks to cloud computing.

Project Idea: This project aims to provide real-time and instant replies to the queries put forward by the customers. 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.

Launch Your Cloud Career with ProjectPro’s GCP Learning Path

Top Cloud Computing Projects For Intermediate Professionals

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-

27) Taxi Service Data Analysis

The taxi service industry is a prime example of utilizing cloud-based analytics applications for informed decision-making. Leading players like Uber and Ola have demonstrated the transformative potential of these tools in optimizing operations and improving service quality.

Project Idea: This cloud project aims to analyze the data of cab services to assist the organization's ineffective strategy development and decision-making—as 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 from all the information and map a particular city's rides.

28) Secure Text Transfer Application

Securely sharing textual information is imperative, especially in the banking sector, where countless transactions occur every second, emphasizing the need for privacy, confidentiality, and integrity. Cloud computing facilitates this through enhanced secure data exchange, offering encrypted channels and robust authentication mechanisms, thereby safeguarding the privacy and integrity of shared information.

Project Idea: This project focuses on data security and cloud computing. Encryption is a security technique for preserving information properties. Diffie-Hellman key exchange is a suitable algorithm for encryption and decryption, as it will serve the private and public keys involved in encryption and decryption. An SQL database for data storage is recommended, as it has built-in security tools and features. You can also use Azure cloud servers to automate the entire process.

29) University Campus Online Automation

All the major universities worldwide, such as New York University, University of Sydney, etc., implement cloud-based systems for managing campus activities. Such systems automate university campus operations to streamline student enrolments and registrations, attendance management, class scheduling, and grading functionalities. 

Project Idea: You can build such an automated system using 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 schedules.

Advanced Projects on Cloud Computing

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

30) Rural Banking

Banking services often need to be improved in rural areas. A Cloud-based banking system can provide rural residents with more accessible banking services. Real-world examples such as regional rural banks, rural banking apps, and Agri rural banks demonstrate how cloud technology transforms rural banking, offering communities improved access and financial inclusion.

Project Idea:  You can work on this rural banking project with the public cloud as the delivery model. PHP can be the preferred programming language for developing 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 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. To protect against this, you can include one-time passwords for login to improve access control and authentication. You should also use the system's digital signatures and data encryption algorithms, such as the Advanced Encryption Standard (AES).

31) Android Offloading 

Android offloading involves transferring computing tasks from Android devices to remote servers or the cloud to enhance performance and save battery. Cloud computing supports this by offering scalable resources that can handle intensive tasks efficiently. Android devices can conserve energy and processing power by offloading tasks to the cloud. Additionally, cloud services provide access to extensive storage and data processing capabilities, enabling complex tasks without burdening the device.

Project Idea: This 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 can be calculated based on the selections. The application must 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 works and responds based on the data provided. Lastly, you must maintain and improve the data quality at all times.

32) Hybrid Cryptography

Banking applications rely on robust security measures to detect data leaks and protect sensitive database information. This project aims to demonstrate the construction of a secure file storage system leveraging hybrid cryptography techniques. Implementing this solution on cloud computing infrastructure ensures enhanced data protection and confidentiality, which are essential for safeguarding critical files and maintaining the integrity of sensitive information.

Project Idea: 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. 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 key's security is never compromised. After working on such data security cloud projects, you can add data security to your skillset, which is highly on-demand due to the increased frequency of security risks and attacks.

33) Smart Traffic Management

This intelligent traffic management project is designed to acquire real-time traffic data and mitigate road congestion effectively. By leveraging cloud computing and big data technologies, the project offers opportunities to enhance traffic infrastructure, reduce response times, and alleviate congestion. Additionally, integrating wireless sensor networks and location-based services further augments the project's capabilities in achieving efficient traffic management.

Project Idea: You shall work on vehicle routing algorithms and predictive analytics techniques to determine bottlenecks and suggest details for real-time route optimization. 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 can be performed using data mining and feature extraction. These will then be implemented over a web app so the end-user can access the system.

Cloud Computing Mini Projects

Are you looking for some exciting and unique cloud computing mini project topics? Here are a few unique and simple cloud computing projects for final-year students looking to gain cloud computing skills-

34) E-bug Tracker

An e-bug tracker identifies and traces the source and nature of bugs within websites or applications. Real-world examples like Backlog and Zoho Bug Tracker follow similar principles, offering efficient bug detection and tracking solutions in software development projects.

Project Idea: This project can include three modules: admin, staff, and customer. If a bug is detected, the admin can immediately contact the staff and customers and implement the corresponding solutions. Customers and staff members can send the bug details to the admin. Python is recommended as the language for this project. In this app, you can include bug case flow status details and share real-time updates with staff members and customers.

35) Personal Cloud with Raspberry Pi

Cloud servers are utilized across public, private, and hybrid cloud environments, catering to both personal and business applications. They offer scalable, flexible, and cost-effective solutions, accommodating diverse needs ranging from individual users to large enterprises seeking robust computing resources and infrastructure.

Project Idea: This mini project on cloud computing will give readers an in-depth understanding of the cloud server and its functioning as they build a personal cloud server. To develop a private cloud, you will need a Raspberry Pi and a Micro SD card. The hard drive will be the primary cloud storage in this private cloud project.

Big Data and Cloud Computing Projects With SQL

Here are a few innovative cloud engineer projects with SQL you might want to explore-

36) E-Learning App

E-learning has been a popular learning method, and its usability has increased after the outbreak of the Covid-19 pandemic. Learning apps offer flexible, personalized learning experiences accessible anytime, anywhere, breaking down barriers to education. Through interactive features and adaptive content, they engage users and enhance retention. Another benefit of such apps is allowing users of all backgrounds to pursue knowledge and skill development at their own pace. 

Project Idea: Using cloud computing, you can develop and deploy a Java-based app offering e-learning solutions with 24x7 availability. Learning, sharing, and reusing will be the significant components of this app. The app can 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 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.

37) Data Warehousing

Data warehousing is collecting, storing, and managing large volumes of structured and unstructured data. It involves organizing data into a centralized repository for analysis, reporting, and decision-making purposes. Data warehouses facilitate efficient data retrieval, integration, and analysis for business intelligence and strategic insights. 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. 

Project Idea: Cloud computing services like Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage can be used to store and manage data. Next, 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. Cloud computing services like Amazon Redshift, Google BigQuery, or Microsoft Azure SQL Data Warehouse can be used to design the data warehouse. Once you have created the data warehouse, 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.

Cloud Computing Projects on GitHub

Below are some cloud computing projects with source code from GitHub for those willing to try their hands on some unique cloud-based projects-

38) Data Analytics Pipeline

This Github project will offer you cdk scripts and sample code for implementing end-to-end data pipelines to replicate 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

39) Covid Tracking Pipeline 

This is one of the most common and interesting cloud project ideas in Github. 

Project Idea: For this cloud-based project, you will use the Covid tracking dataset from the Azure Open Datasets collection. 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 to the notebook and save the dataset as a table. Due to the shared metadata, you can access this Spark-created table using SQL On-demand.

Source Code: Covid Tracking Pipeline Using Azure

40) NYC Service Request Data Analysis

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 critical insights. This project is one of the best cloud computing projects in Python with source code in this blog, so do not skip it.

Project Idea: 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

Kickstart Your Cloud Computing Journey With Our Cloud Computing Projects PDF

It can be highly challenging for an individual to build all the projects mentioned above. The good news is that you don’t have to because, in cloud computing, one is expected to be a master of a few tools rather than a Jack of all. So, pick your niche and polish the skills you already have to master cloud computing, as Laura Hyatt, Senior Cloud Architect at Google, suggested in her LinkedIn post below.

Excel In Big Data With Cloud Computing Projects By ProjectPro

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. 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 Azure, AWS, 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!

FAQs on Cloud Computing Projects

1. How to develop 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.

• Cloud monitoring and management tools are used to track and improve the efficiency and cost of the application or solution.

• 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.

2. What are some common use cases for cloud computing in projects?

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.

3. How do I choose the right cloud computing provider for my project?

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. 

4. What are the different types of cloud computing services available for projects?

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

About the Author

ProjectPro

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

Meet The Author