Do you want to put your cloud computing skills to the test? Explore these creative cloud computing software ideas for big data experts and discover how to master the cloud!
Cloud computing has transformed how we store, handle, and analyze large amounts of data, making it a must-have talent for data scientists and big data experts.
According to a new Meticulous Research analysis, the worldwide cloud computing industry is expected to reach $1,402.7 billion by 2030, with a CAGR of 16.8% between 2023 and 2030.
Another poll revealed that more than 90% of professionals use cloud services daily for corporate operations. As businesses become more reliant on cloud technologies, this move opens up numerous job prospects, particularly in big data and cloud computing.
As a result, acquiring hands-on experience through practical cloud computing projects is now required for anyone wishing to flourish in this profession.
Hackemist welcomes you to discover the best cloud computing projects that will inspire you to harness the power of cloud computing and take your big data abilities to new heights.
Cloud Software Ideas
1. Repair Techniques For Transportable Storage Devices
Consider this scenario: a photographer’s external hard disk, which contains years of valuable work, suddenly fails. The need for repair procedures for portable storage devices becomes clear when the photographer risks losing precious photographs.
In this case, repair measures such as connector replacement or data recovery software are required to recover lost data and restore functionality to the storage device.
These strategies keep essential data accessible and secure, emphasizing their value in protecting valuable information held on portable storage devices.
Create a dataset including user-reported storage device concerns such as HDD failures or data corruption. This dataset can be safely stored and managed using cloud-based storage systems such as Amazon Web Services’ S3. Use AWS Glue for data analysis and repair.
AWS SageMaker provides algorithms for data recovery and restoration, such as RAID configurations and error correction codes (ECC).
2. Migrating MySQL Databases to Cloud AWS
Migrating MySQL databases to AWS has various benefits. It improves scalability by letting databases seamlessly adapt to shifting workloads and storage requirements. AWS’s comprehensive security mechanisms assure data protection and regulatory compliance.
It also increases reliability and saves downtime with automated backups and high-availability capabilities. With AWS-managed services, businesses can concentrate on innovation rather than database management, resulting in enhanced agility and cost savings.
To get started on this project, familiarize yourself with AWS migration services, particularly AWS DMS. Set up a Bastion Host for data security and use AWS SCT for schema conversion. Create an Aurora Postgres instance with RDS and set up DMS SCT between MySQL and Postgres.
Migrate database items, examine migration data, and upload it to AWS S3. Use Glue to establish a Data Catalog, query data using Athena, and prepare data for Timestream. Finally, import data into Amazon Timestream DB and visualize geospatial data with QuickSight to gain full insights.
3. Real-Time Sentiment Analysis
Real-time sentiment analysis allows firms to quickly gather customer feedback from social media and reviews. This rapid insight enables proactive decision-making, the identification of new trends, the prompt resolution of problems, and the optimization of marketing campaigns, eventually improving customer happiness and brand reputation for long-term success.
Cloud computing makes real-time sentiment analysis possible by providing scalable, on-demand computing capabilities for analyzing massive amounts of text data, allowing businesses to derive actionable insights quickly and efficiently.
Begin by creating a data stream utilizing applicable APIs or data connectors in conjunction with AWS Kinesis to collect and store data from social media networks such as Twitter. If you don’t want to scrape data, you can use publicly available social media datasets, like Sentiment140.
Next, use AWS Lambda to trigger the Kinesis stream whenever new data becomes available. Following that, use AWS Comprehend to assess the sentiment of incoming data in real-time, since it can detect the sentiment of text, key phrases, entities, and other important information.
Finally, AWS QuickSight may be used to generate dashboards that display the sentiment distribution of the data, as well as any other relevant metrics.
3. Ecommerce Predictive Analytics
E-commerce predictive analytics employs data analysis and machine learning algorithms to estimate customer behavior, trends, and results in online retail environments. Businesses can optimize sales and customer happiness by evaluating historical data and patterns.
To begin working on this project, learn about ETL on Big Data, as well as staging and Data Lake ideas. Create IAM Roles and Policies, then examine the dataset. Configure the AWS CLI and understand Data Streams, Amazon Kinesis, and Apache Flink. Create a Kinesis Data Analytics Application and use Glue and Athena to specify the partition key. Understand Lambda Functions and then write them for DynamoDB and SNS integration.
Learn DynamoDB data modeling and how to connect Lambda with Kinesis. ETL for Parquet format utilizing Glue DataBrew and Spark, followed by the creation of QuickSight Dashboards for extensive data visualization and analysis.
4. Serverless Pipeline
A serverless pipeline is a set of automated actions or steps for processing and deploying software applications that eliminate the need to manage servers or infrastructure. It uses cloud service technologies such as AWS Lambda and Azure Functions to execute operations in reaction to events or triggers, resulting in scalable and cost-effective development workflows.
This project will demonstrate how to build a serverless pipeline with the AWS CDK and other AWS serverless technologies, including AWS Lambda and Glue.
You’ll have a deeper understanding of the AWS CDK and its numerous commands. You will first set up an AWS Cloud9 environment, and then clone the GitHub repository. This cloud-based project will help you learn about the Lambda stack, the Glue pipeline stack, and the Glue database stack.
After deploying the AWS CDK pipeline, you will do additional analysis with Amazon Athena and produce visualizations with Amazon QuickSight.
5. Movie Recommendation Engine
A movie recommendation engine is a system that recommends films to consumers based on their tastes and behaviors. Cloud computing helps recommendation engines by offering a scalable infrastructure for processing large amounts of user data, enabling personalized recommendations, and improving user experience through on-demand access and seamless scalability.
This cloud engineering project entails creating a cloud-based recommendation engine with Amazon SageMaker and Amazon EC2. Use an example dataset that contains user activity data, such as the MovieLens dataset, which has both movie ratings and user behavior information.
Store the dataset in Amazon S3, then use Amazon Glue to extract, transform, and load the data into Amazon SageMaker.
Create the recommendation model in Amazon SageMaker using the factorization machine approach, which effectively processes massive datasets and provides customers with reliable predictions. You will also utilize Amazon EC2 to build a scalable web service that makes real-time suggestions to users based on their actions.
The recommendation engine will be deployed using Amazon API Gateway to establish a RESTful API that communicates with the recommendation model.
6. Data Governance
Data governance refers to the set of policies, processes, and controls that guarantee data is successfully managed throughout its lifecycle. It establishes roles, responsibilities, and procedures for data management, such as data quality, security, compliance, and privacy, to maximize value and reduce risks inside a business.
To begin, create an Azure Purview account for effective data asset management. Then, construct and scan Purview collections to organize and analyze the data. Explore the Purview user interface to become familiar with its features.
Understand the Purview glossary and assets to ensure proper data management. Learn about Purview’s access control and how to adopt best practices for effective governance. Finally, grasp Azure Purview’s limits so you may make informed judgments during project development.
7. Remote-Controlled Smart Devices
Remote-controlled smart devices are internet-connected items that can be operated remotely using smartphones or other devices. They use cloud computing for data storage, processing, and remote access. Cloud platforms allow users to operate devices from any location, access data, receive updates, and seamlessly interact with other smart home systems.
Begin developing this fascinating cloud project by picking various smart devices, such as smart lamps, plugs, or thermostats, that allow for remote control and integration with cloud platforms. Next, these devices can be linked to a cloud-based IoT service, such as Azure IoT Hub, for remote control and management.
Cloud-based services such as Azure Stream Analytics can be used to handle and analyze data provided by selected smart devices, providing important insights into device usage, energy consumption patterns, and user behavior. Create a web or mobile application utilizing cloud-native development tools like Azure App Service to interface with smart devices via the IoT service, allowing users to operate them from anywhere.
8. Passenger Survival Prediction
The Titanic Passenger Survival Prediction dataset includes demographic information about Titanic passengers, as well as ticket class and survival status. It offers useful insights into examining and forecasting survival probabilities depending on a variety of criteria.
The Titanic Passenger Survival Prediction project provides newcomers with excellent hands-on experience in data analysis, machine learning, and cloud computing, which will help them improve their practical abilities and portfolio while implementing data science projects on the cloud.
For this Azure cloud computing project, you can use the Titanic dataset, which provides information about Titanic passengers and their survival rates. The dataset will be stored first in Azure Data Lake Storage, then cleaned and transformed using technologies such as Azure Databricks, and finally stored in a structured fashion using Azure SQL Database.
You will use Azure Data Lake Analytics to do data analytics and exploratory data analysis. After cleaning and converting the data, Azure Machine Learning allows you to create ML models. You can use techniques such as linear regression, decision trees, and neural networks to estimate whether a passenger would survive based on their features.
9. IoT Data Processing System
An IoT Data Processing System uses cloud computing to rapidly manage and analyze huge amounts of sensor data from connected devices, enabling real-time monitoring, intelligent automation, and seamless connection with other cloud-based services for increased functionality and scalability.
For this intriguing cloud computing project, you will create a cloud-based IoT data processing system that uses Google Cloud IoT Core, a SQL-based cloud database. You can use publicly available IoT datasets, such as the UCI Machine Learning Repository’s IoT Sensor Dataset, which comprises temperature, humidity, light, and carbon dioxide sensor data from a university building.
First, capture and store data using Google Cloud IoT Core. Next, use SQL Workbench, DBeaver, or Aqua Data Studio to construct and administer the database schema. To develop SQL-based cloud databases, use Google Cloud SQL.
Once you’ve created the cloud databases, perform SQL queries to analyze the sensor data stored there. For example, you can utilize aggregate functions to calculate the average, maximum, and minimum temperature and humidity values over a certain period of time.
You may also use data transformations and joins to combine information from various sensors or sources.
Cloud Computing Projects with Source Code
1. Attendance System
The attendance system tracks an employee’s or student’s attendance and stores the data in the cloud. When it scans the card, it records data such as the in-time, ID number, and out-time. This will help an administrator remove or add users, as well as measure how many hours an employee or student spends on the premises.
2. Bus Ticketing and Payment Systems
As part of this pilot, passengers will be able to purchase bus passes online. The site offers bus booking, scheduling, and payment options, and riders can purchase a bus pass by logging into the portal using their login credentials.
3. Host a Dynamic Website
Users can interact with a dynamic web page in a variety of ways. Using a dynamic website will significantly help firms achieve their goals. In this project, you will develop a dynamic website on Amazon Web Services (AWS) using client- and server-side languages such as CSS, PHP, HTML, ASP, and JavaScript.
4. Host a Static Website on AWS or other clouds
In this cloud services project, you will use Amazon S3, a simple web-based cloud repository supplied by Amazon, to store static web pages. (docs, blog sites, and so on). You will use front-end technologies like HTML and CSS to help with the development of this project.
5. Websites Without Servers
Using a serverless cloud computing architecture allows you to launch solutions faster and more efficiently. Furthermore, serverless websites offer various advantages, including scalability, the option to charge customers based on their use of serverless environments like DynamoDB, API, S3, and consumption.
Cloud Computing Projects GitHub
Here are some cloud computing projects with source code from GitHub for people who want to try their hands on some unique cloud-based projects:
1. Data Analytic Pipeline
This Github project will provide you with cdk scripts and example code for building end-to-end data pipelines that replicate transactional data from MySQL DB to Amazon OpenSearch Service via Amazon Kinesis and Amazon Data Migration Service (DMS). You will set up an Aurora MySQL Cluster and Amazon Kinesis Data Streams for the AWS DMS target endpoint.
You will establish a sample cloud database (testdb) and table (retail_trans). Next, you’ll set up Amazon OpenSearch Service and Amazon Kinesis Data Firehose. You’ll use an SSH tunnel to gain remote access to the Amazon OpenSearch Cluster.
2. Covid Tracking Pipeline
This cloud-based project will make use of the COVID tracking dataset from the Azure Open Datasets collection. You will learn how to use and adjust a preconfigured ingestion template for data ingestion and transformation. You will download and save the pipeline template to Blob storage. After the pipeline has been loaded and debugged, both (raw and curated datasets) will be created/copied to the destination path specified on the Azure Data Lake Store account.
Next, download and import the Synapse notebook from Azure Open Datasets into Azure Synapse. Include the creation of a Spark Database in the notebook and store the dataset as a table. You can use SQL On-demand to retrieve this Spark-created table because it has shared information.
3. NYC Service Request Data Analysis
This cloud-based project seeks to build a batch data pipeline that continuously collects, converts, and loads data from NYC 311 Service Requests into a data warehouse, allowing you to visualize key findings. This project is one of the top cloud computing projects in Python, with source code on this blog, so don’t miss it.
For this project, use the 311 dataset from the New York City Open Data Portal. You will use the Python Pandas module to retrieve data from the Socrata API, convert it to a dataframe with the necessary data types, and then put it into BigQuery.
Terraform will be used to quickly manage infrastructure setup and changes, as well as Docker to containerize the code. You’ll learn how to use Cloud Run Jobs to perform perfect flows in a serverless environment. For this project, you will utilize Google BigQuery as a data warehouse and Google Looker Studio to create a dashboard. You’ll also use Prefect OSS and Prefect Cloud to coordinate, monitor, and schedule server deployments.
Conclusion
Cloud computing has emerged as an essential tool for professionals looking to enhance their careers and keep ahead of the competition in the big data sector. As the industry’s demand for cloud expertise grows, obtaining hands-on experience working on cloud projects is critical to displaying your abilities and standing out.