Structured and Unstructured Big Data Analytics

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 varietyful 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, industries requirement to quickly process and analyze the big data volumes for business decision making and customer insights has also grown exponentially. Data pouring from various sources may be can be structured or unstructured in nature. Structured data refers to a relatively well-organized information, which can be further inserted into traditional RDBMS. As Traditional RDBMS are efficient and easy queries by simple, straightforward search algorithms or SQL queries. 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 stored well into relational tables. It is assumed to be fastest growing type of data, e.g. image, sensors data, web chats, social networking messaging data, video, documents, log files, and email data. There are many techniques and software available, which can process and provide efficient storage of 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. The variety and on ordered nature of data makes storage methods and structure makes execution a time and resource-consuming affair. Advancement into technology has open floodgates to push huge volume of unstructured type of data. Multimedia data is one of the example of unstructured big data, which spans all over the Internet. This needs high execution capability to extract useful information. Rapid processing of multimedia data such as video is important for e.g. criminal investigations, surveillance monitoring, news analysis, sports analytics domain, emotion extraction, etc. Hence, analysis of multimedia data in minimum timeframe is one of the latest research areas. Therefore, we have researched techniques for analyzing unstructured data to extract meaningful information hidden in the big data. In addition, we will describe about various techniques and software used to Manage, process unstructured big data in efficient manner, and increases the performance of complexity analysis.