Haewon, Byeon
Department of Digital Anti-aging Healthcare (Bk21), Inje University, Republic of Korea
Abstract
Data processing and analysis are critical components of modern business and scientific research. With the increasing amount of data being generated every day, it is essential to have powerful tools and techniques to handle and analyze big data. Big data refers to data sets that are too large and complex for traditional data processing and analysis techniques. To process and analyze big data, various tools and techniques have emerged over the years, including distributed computing frameworks, NoSQL databases, machine learning algorithms, and data visualization tools. These tools and techniques help organizations extract valuable insights and patterns from big data, which can be used for decision-making, business optimization, and scientific research. In this context, Hadoop, Apache Spark, MapReduce, NoSQL databases, data visualization tools, machine learning algorithms, and natural language processing (NLP) techniques are some of the most commonly used tools and techniques for big data processing and analysis.