Modern Business Data Analysis and Data Visualization: A Real-Time Fusion Study

Abstract

In contemporary data science and analytics, data clustering is a small bucket that divides computation among various child nodes. The network’s capacity, specialized tools, and applications that cannot be trained quickly are among these methods’ drawbacks. In addition, the IoT-formed Big Data raw data can result in highly heterogeneous and unstructured data. This kind of data is difficult to analyze for real-time analytics. Real-time analytical challenges can be reduced by making computational values available locally rather than via distributed resources. Most of the time, it takes a long time and a lot of money to run these teams and skill sets. As an alternative, provide tools that let end users, professionals in the industry, and data scientists directly create and deploy complex data analytics application solutions with less technical knowledge. It highlights key advantages, disadvantages, and potential future directions by contrasting various current research and practice approaches to assisting end users with data analytics.