Structured vs. unstructured data: Information Management in BigData

Structured vs. unstructured data
Structured vs. unstructured data

Currently, millions and millions of terabytes of information are created every day on the Internet. Therefore, it is very important to manage structured vs. unstructured data, to know them and to take advantage of this gigantic amount of information.

Structured data is information that is stored in the form of relational databases, generally known as SQL databases.

Relational databases are made up of tables, which contain the useful information, plus a series of unique numerical values that form keys, which allow to relate some tables with others; they also contain indexes that allow to order the information and to make certain queries faster.

While the unstructured data, are those that do not have some type of order that allows to make a categorization as it does a sql database. In this category we can find data in the form of text files, pdf, word, emails, images, sound files, chats, tweets, web pages, etc.

Considering the relationship between structured vs. unstructured data, currently there is a higher proportion of unstructured data, containing valuable statistics for different types of organizations, but much more complicated to handle, because they do not contain related information that allows processing them in a fast and orderly manner, as it is done with information from relational databases.

This is where the tools for big data, allow to manage structured and unstructured data, through algorithms specially designed to manage large volumes of information, also by taking significant samples of these large amounts of data, to achieve important statistics for business.

Among these types of algorithms we can find systems that work with neural networks or Bayesian networks, since the management of such information requires systems that can better «understand» data of different types such as images, normal text, mp3, etc.

It also becomes important what is known as the semantic web, where web pages are marked up to make it easier for search engines to recognize the type of data handled by this type of html files. For example, marking products, software applications, geolocation, language, professional services. This markup is handled by a standard microdata schema or in the form of json arrays, or by placing alt tags on photos, making it possible to more easily recognize the content of web pages.

In conclusion, it is important for the IT community to know the whole series of tools, algorithms and methodologies used for the management of structured vs. unstructured data, because there are already millions of data to be analyzed and they contain very relevant information, which can be very useful in the form of statistics for decision making, or for the identification of trends, problems or possible future occurrences of events.

This article is part of the knowledge dissemination system of ITSoftware SAS.

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