Big Data Article

Introduction

Big Data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications.

It is often characterised by the 3V's (Volume, Variety, and Velocity). These were first identified by Doug Laney, in a report published in 2001. 3 More Vs (Veracity, Value, Variability) were added since then.

Although big data doesn't equate to any specific volume of data, big data deployments often involve terabytes, petabytes, and even exabytes of data captured over time.

Features

The 6 V's of big data

Volume - The amount of data from myiad sources.
Variety - The types of data: structured, semi structured and unstructured.
Velocity - The speed at which big data is generated.
Veracity - The degree to which big data can be trusted.
Value - The business value of the data collected.
Variability - The ways in which the big data can be used and formatted.

Big data also encompasses a wide variety of data types, including the following:

Structured data in databases data warehouses based on Structured Query Language (SQL)
Unstructured data, such as text and document files held in Hadoop clusters or NoSQL database systems
Semistructured data, such as web server logs or streaming data from sensors

Pros/Cons

Big Data has its pros and cons:

Pros	Cons
Exposure - Through Data Visualisation, graphics can be used to reach a variety of stakeholders, including those with less technical expertise and less time to grasp things. These apps work as participatory methods that work to increase the exposure of the inferences to a wider audience as a reporting method that reaches a variety of people and can be useful for them to arrive at beter businesses.	Cost - More often than not, Data Visualization applications cost a decent sum of money, and it may not be possible for companies, especially small ones to spend a lot of resources upon purchasing them.
Understanding - Explaining an insight into the business may be more appealing and easy to understand through graphics and charts when compared to a written document comprising text and numbers, and thus can attract a wider audience.	Distraction - At times, the Data Visualization apps create reports and charts laden with highly complicated and fancy graphics, which may be tempting for the users to focus more on form than on function, if the primary goal is to add visual appeal, then the overall value of the graphic is minimal.

Extra Programs

Data Visualization, The Internet of Things
Data Analysis
Haskell, Erlang, SQL, Databases

Sources

Summary

In conclusion:

The value and effectiveness of big data depends on the workers tasked with understading the data and formulating the proper queries to direct big data analytics projects. Some big data tools meet specialized niches and enable less technical users to use everyday business data in predictive analytics applications. Other technologies help businesses implement a suitable compute infrastructure to tackle big data projects, while minimizing the need for hardware and distributed software know-how.