SCALABLE DATA ANALYTICS USING CLOUD PLATFORMS

Abstract

Abstract - The rapid growth of digital technologies, Internet of Things (IoT), social media, enterprise applications, and online services has resulted in the generation of massive volumes of data. Traditional data analytics systems often face limitations in terms of storage capacity, processing power, scalability, and cost efficiency when handling large-scale datasets. Cloud platforms have emerged as a powerful solution by providing flexible, scalable, and cost-effective computing resources for advanced data analytics. Scalable data analytics using cloud platforms enables organizations to process, store, analyze, and visualize large datasets by leveraging cloud computing technologies such as distributed computing, data lakes, artificial intelligence, machine learning, and serverless analytics. Cloud-based analytics platforms allow organizations to dynamically increase or decrease resources according to workload requirements, improving performance and operational efficiency. This paper discusses the concept, objectives, architecture, components, technologies, applications, benefits, challenges, and future trends of scalable data analytics using cloud platforms.

Keywords: Cloud Computing, Data Analytics, Big Data, Scalability, Machine Learning, Data Lakes, Distributed Computing, Artificial Intelligence, Cloud Platforms, Business Intelligence.