What is structured data in big data?

As capital is required to run a business, data is equally important. Data is the utmost requirement of a business. Without valid and fruitful information, business cannot carry out its operations effectively. Your last post on Facebook, video watched on YouTube, emails, all are considered as data. Putting simply, structured data in big data collection of information.

Big data means large amount of data. Big data is complex due to presence of huge variety of data and therefore it becomes difficult to collect, store, maintain, analyze and visualize. Companies process big data to extract useful information from it and use it for decision making.

Big data are of three types, namely:

Here in this article we will discuss about structured data.

What is structured data in big data?

As the name says, structured data is any data which is properly organized, with well-defined length and format, data model or ?schema?. It can be easily retrieved and is stored in excel sheets, databases, or in tabular form in CSV files (Common Separated Values), having fixed number of columns and rows with clearly defined attributes.

Structured data is highly organized and absolutely perspicuous for machine language.

Structured data is simple to use and easy to understand, store, format, execute, process, analyze and query. Structured data can be easily accessed and used by a computer program or by any user. Structured data is a Meta data that is hidden to the user but readable by search engines.

Structured data contributes about 20 percent to big data. Structured data is generally managed by SQL (Structured Query Language) for managing, analyzing, querying the data stored in RDBMS (Relational Database Management System) and spreadsheets.

Example of structured data

To understand better, as under the attribute ?Name? all names are written and under the attribute ?Email Id? all email ids are written, it is kind of similar to an employee table. Structured data does not require much effort as things are presented in a structured manner.

The examples of structured data are spreadsheets, traditional Relational Database Management System (RDBMS) having well-defined rows and columns with definite attributes such as date, name, address, mobile number, email id, stock information, marks obtained, etc.

Characteristics of structured data in big data

  • Structured data is well organized.
  • Structured data conforms to data model and its structure is easily identifiable.
  • Structured data resides in fixed number of fields within a file.
  • Structured data is easy to access and query.
  • Structured data have easily addressable elements, so it is easy to analyze and process.
  • In structured data, similar entities are grouped together to form relation.
  • In structured data, entities in the same group have same attributes.
  • Data is recorded in the form of rows and columns. For example, Database.

Sources of Structured data

  • Structured Query Language (SQL) Databases
  • Spreadsheets such as Excel
  • Various online forms
  • Sensors such GPS (Global Positioning System)
  • Medical devices
  • Online Transactional Processing (OLTP) Systems
  • Network and web server logarithms

Advantages of structured data in big data

  • Structured data is highly organized and that helps in easy access and storage of data.
  • Due to high level of organization, data mining is easy; information can be very easily extracted from data.
  • Security can be easily ensured to structured data.
  • Operations such as updating, adding, deleting is easy due to well defined form of data.

Disadvantages of structured data in big data