Unstructured Data Case Study

CHAPTER - I

INTRODUCTION

1.1 What is Unstructured Data? Unstructured data is a generic label for describing data that is not contained in a database or some other type of data structure . Unstructured data can be textual or non-textual. Textual unstructured data is generated in media like email messages, PowerPoint presentations, Word documents, collaboration software and instant messages. Non-textual unstructured data is generated in media like JPEG images, MP3 audio files and Flash video files. If left unmanaged, the sheer volume of unstructured data that’s generated each year within an enterprise can be costly in terms of storage. Unmanaged data can also pose a liability if information cannot be located in the event of a compliance

Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media. Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure. Traditional database systems are also designed to operate on a single server, making increased capacity expensive and finite. As applications have evolved to serve large volumes of users, and as application development practices have become agile, the traditional use of the relational database has become a liability for many companies rather than an enabling factor in their business. Big Data databases, such as Mongo DB, solve these problems and provide companies with the means to create tremendous business value.

1.4 Unstructured Data in a Big Data Environment

Unstructured data is data that does not follow a specified format for big data. If 20 percent of the data available to enterprises is structured data, the other 80 percent is unstructured. Unstructured data is really most of the data that you will encounter. Until recently, however, the technology didn’t really support doing much with it except storing it or analyzing it manually.

1.4.1 Sources of unstructured big

It’s critical that organizations adopt a NoSQL platform that allows them to keep their very high volume data in the context of their applications. Some, but not all, NoSQL solutions provide modern architectures that can tackle the type of applications that require high degrees of scale, data distribution, and continuous availability. Data center support, and as is more common, multiple data center support, should be a use case with which a NoSQL environment complies. It’s not just what your big data needs look like today but also out to greater time horizons that decisions should be made.

1.7.8 Analytics and Business Intelligence A key strategic driver of implementing a NoSQL database environment is the ability to mine the data that is being collected so as to derive insights that puts your business at a competitive advantage. Extracting meaningful business intelligence from very high volumes of data is a very difficult task to achieve with traditional relational database systems. Modern NoSQL database systems not only provide storage and management of business application data but also deliver integrated data analytics that deliver instant understanding of complex data sets and facilitate flexible