Efficient Big Data Processing Framework for Real-Time Analytics

Abstract

In today's fast-changing digital landscape, massive amounts of data are generated every second. This "big data" explosion comes from a variety of sources, including social media, IoT devices, financial systems, and healthcare platforms. Managing, processing, and analyzing this data in real-time has become critical for firms looking to gain actionable insights and preserve a competitive advantage. "Real-time analytics" refers to the practice of analyzing data as it is generated, allowing organizations to respond quickly to changing conditions. An effective big data processing system is required to enable such analytics, ensuring that large amounts of information are not only stored but also processed quickly enough to drive prompt decisions.

Big data is often defined by its volume, velocity, and variety, known as the "three Vs" of big data. A vast amount of data can be petabytes or exabytes in size, with velocity referring to the quick rate at which new data is created and diversity indicating the various forms that big data can take, such as structured, unstructured, or semi-structured. Real-time analytics refers to the instantaneous analysis of data as it enters a system, with results produced rapidly enough to impact present activities.