Published Paper Details:

In the era of unprecedented data generation, the realm of Big Data analytics has emerged as a cornerstone for extracting valuable insights. This research paper delves into the dynamic domain of real-time Big Data analytics, shedding light on both the advancements that propel its growth and the formidable challenges that impede its seamless integration. The paper commences with an exploration of the technological strides that have propelled real-time Big Data analytics into the forefront of data-driven decision-making. From the evolution of distributed computing frameworks to the advent of cutting-edge machine learning algorithms, an in-depth analysis is presented to illustrate the transformative impact of technological advancements on the efficacy of real-time analytics. However, as the landscape of real-time Big Data analytics evolves, it encounters formidable challenges. This paper meticulously examines the hurdles that impede the smooth execution of real-time analytics processes. Issues such as data security, scalability, and the intricacies of stream processing are dissected, providing a comprehensive understanding of the challenges that demand innovative solutions. Furthermore, the research critically evaluates existing solutions and methodologies employed to address the identified challenges. It explores the synergy between real-time analytics and emerging technologies like edge computing and artificial intelligence, offering insights into the potential avenues for overcoming existing limitations. In conclusion, this comprehensive review synthesizes the state-of-the-art advancements and challenges in real-time Big Data analytics, providing a holistic understanding of the current landscape. By amalgamating technological progress with the intricacies of implementation, this research paper serves as a valuable resource for researchers, practitioners, and organizations navigating the complex terrain of real-time Big Data analytics.