Big Data Analytics Approach for Structured and Unstructured Data

Abstract

The volume of data in the world is growing very fast and generated from verity of sources like social media, sensors airline industry or scientific data in different formats. Biggest challenge is how to infer meaningful insights from such a variety and big data along with concern of data storage and management of fast-growing data. The size of the databases used in today's enterprises has been growing at exponential rates day by day. Hence, retrieval and extraction of the information is essential works and importance in semantic web areas to fulfil the industries requirement to quickly process and analyseexponential growing the big data. Data torrential from various sources may be structured or unstructured in nature. Structured data refers to a relatively well-organized information but high in volume, which can also analyses by big data tools in better way. As Traditional RDBMS are not very efficient to queries on textual filter condition, In contrast to structured data, unstructured data can be considered as information, which does not, comes in a pre-defined data format, well organized data storage model, or cannot be handle by legacy RDBMS models. It is assumed to be fastest growing type of data, e.g sensors, access logs data, and email data. There are many techniques and software available, which can process and provide efficient storage of structure and unstructured data and help organization to perform analytics on unstructured data. Unstructured data does not well-organized and not stored in predefined manner e.g. logs, web chats. So there is no common framework available to analysis the structure and unstructured data. In this paper the researcher has applied a common approach to analysis and provide the result in better way. Here mainly focus on open-source tools to analysis the data like elastic-search, Java, JSF, Apache tomcat etc.