A Case Study of Big Data Processing in the Cloud Environments with Insights into Advantages, Tools, and Techniques
- Resource Type
- Conference
- Authors
- Chowdary, Pullela Harish; Thapar, Puneet; Alyas, Arsalan Ahmed; Singh, Urvashi
- Source
- 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence) Cloud Computing, Data Science & Engineering (Confluence), 2024 14th International Conference on. :199-204 Jan, 2024
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Radio frequency
Cloud computing
Data analysis
Education
Data visualization
Big Data
Classification algorithms
Cloud Computing
Big Data Analytics Tools
Cloud Platforms
World Data
Healthcare
Case Study
Power BI
Rapid Miner
- Language
- ISSN
- 2766-421X
Every single day, users are increasing as the world has explored technology where data is producing more and more big data and cloud technologies are the reason behind the scene because big data and cloud computing are the booming pieces of knowledge currently with AI helpful for users and for other professional and practical purposes. Nowadays integration of technologies also been an emerging topic it helps in the reduction of disadvantages of particular technology to provide better solutions. This paper talks about what, and why big data and cloud technology in daily lives and where big data in cloud technology is used. This paper examines recently proposed frameworks/models where big data and cloud technologies are utilized together and reviewed in a detailed way from the year 2020 to 2023. What Advantages can be there in utilizing both big data and cloud together is also discussed. This study explains the most used big data analytical tools and widely used cloud platforms and performed a simple case study on two datasets (world data and disease symptoms dataset) to draw some insights into which Rapid Miner and Power BI are used where RF and Gradient Boosted Trees got more accuracy than other classification algorithms with 0.7 accuracy while using disease symptoms dataset and some visualizations are drawn with use of world data.