Hybrid Cloud Architecture For Data-Intensive Applications

Abstract

The hybrid cloud architecture has transformed the deployment and management of data-intensive applications, notably in India, where the digital economy is quickly increasing. According to a NASSCOM analysis, the Indian cloud industry is expected to develop at a 30% CAGR from 2020 to 2025, reaching $7.1 billion by then. Traditionally, enterprises relied on on-premises infrastructure, which frequently presented difficulties such as limited scalability, high maintenance costs, and inefficient resource usage. Prior to the introduction of machine learning and AI, these old systems battled with data silos, manual data processing, and rigid data management, resulting in inefficiencies in handling enormous amounts of data and delayed decision-making processes. This example highlights the need for a more nimble, efficient, and scalable approach to data management. The expanding complexity and volume of data in areas such as healthcare, banking, and e-commerce motivates researchers, as timely insights have a substantial impact on results and profitability. Proposed systems based on machine learning and AI can address these issues by providing intelligent data processing, predictive analytics, and automated decision making. Organizations can improve their data-intensive applications by integrating advanced algorithms with hybrid cloud architectures, which improve speed, reduce latency, and provide real-time insights, eventually optimizing resource allocation and encouraging creativity. Thus, hybrid cloud architecture, fueled by machine learning and AI, offers a disruptive answer for enterprises looking to succeed in a data-driven landscape.