USAGE OF HADOOP AND MICROSOFT CLOUD IN BIG DATA ANALYTICS: AN EXPLORATORY STUDY

This paper explores the how Hadoop and Microsoft cloud are used in big data analytics and how it changes in the big data sector. It also discusses these initiatives and how programs address large-scale data analysis and address the concerns of increasingly increasing data files. Big data is a method used to process, transmit and high-speed evaluate large amounts of data [1]. Big data may be informed by an understanding of organized, unstructured, and semi-structured data, which leads to the decline of traditional methods of data processing. Different inputs and the device are used to produce the data at various speeds. After reviewing the basics, further research (Hadoop and Cloud services) is carried out by exploring complementary tools and programs that promote cloud Hadoop [1]. Hadoop is planned to reach thousands of machines from single servers. In the early days of the Internet, the duo decided to invent a means of returning site search results more quickly by the distribution of data and measurements across various machines so that several functions could be performed simultaneously. A cloud-based architecture applies to aggregating elements such as software programs, middleware, and storage servers used for cloud computing. This helps to design, implement and manage cloud-based systems rigorously and is also an effective model for large-scale-up and computerization of the automatically allocated resources. Big Data Analytics (BDA) provides cloud architecture data storage tools to store, analyze and process an enormous amount of data [2]. This paper provides an explorative-based study of the Hadoop and Microsoft cloud framework to understand how it can be used in Big Data analytics. Major companies such as Amazon, IBM Google, and Microsoft have adopted these technologies and their frameworks ideally tailored to their work for researchers, IT analysts, readers, and market users to ensure a successful outcome