What is BIG DATA?

Introduction, Characteristics, Types

Big data is a very important section which became popular since the 19th century. Nowadays all major industries such as healthcare, retail, manufacturing, financial, banking etc use big data to achieve their goals. This review covers an introduction to big data, characteristics and types.

Introduction

Most definitions of Big Data focus on the volume. The size is a challenge, but there are two other main characteristics, they are variety & velocity. The three Vs of big data create a complete definition, and they busted the myth about the volume. The growth of technology and human research of the data moved data handling to an extreme level. Characterized by volume, velocity, variety, veracity and value (5Vs). Generally, big data is massive and wide-ranging, more complex data sets. Consequently, business organizations could not manage them with general data processing software. However large data sets detect business issues which were not tackled before.

Characteristics of BIG DATA

BIG DATA can be described by the following characteristics

Volume: Recently, industries are facing problems with voluminous data. The mass of data becomes larger day by day. It increases in size from terabytes to petabytes

Variety: There are so many types of data. Data forms are text, picture, audio, video, sensor data and log data etc. The main advantage is that we can discover new insights when analyzing all data types in the same movement.

Velocity: Velocity means how fast data collects. Sometimes even one minute becomes harmful to the output. Big Data velocity is dealing with the speed at which data flows from the source like social media, networks, sensors, applications, mobile devices, etc.

Types of BIG DATA

Big Data comes with multiple types. Examples are numeric data, text documents, audio records, videos records, pictures etc. Data structures can be divided into three main structures. They are structured data, semi-structured data and unstructured data. And all three structures are usually mixed together. Most of the data is structured and unstructured. Therefore, industries require different techniques and different tools for analyzing tasks. Distributed computer environment (DCE) and massive parallel processing (MPP) that allows paralleled data ingestion and analysis.

Structured data: Most data (80–90%) falls under the structured data. Structured data primarily shows tables and the other data sources of relational databases. The common examples are Excel sheets or SQL databases. Each example has ordered columns and rows.

Semi-structured data: Semi-structured data is a type of structured data, but it does not show the proper structure of data models which are linked with relational databases. The common examples are JavaScript Object Notation (JSON) and Extensible Markup Language (XML).

Unstructured data: These types of data have no inborn structure. Unstructured data is commonly so heavy because it is not easy to understand. The popular examples for unstructured data include texts, numbers, PDF files, photos, audio records and videos.

Conclusion

Data is incredibly powerful when it is used correctly. All major industries take advantage of big data to make efficient decision making. Benefits like saving time, reducing costs, increasing sales and loyalty etc. Big data leads our lives to extreme levels that our lives cannot reach.