A lot has been said and written about Big Data over the past ten years, but it raises questions about how much people know about it. Too often, we just accept the latest buzzword or phrase into our lexicon and use it without fully understanding what it means.
And although there are plenty of resources available out there that go into detail about Big Data, we're going to focus on the concept by paying more in-depth attention to the often-cited "five v's of Big Data." We will review the fundamentals, such as the characteristics of Big Data, its definition, and the Five Vs of Big Data themselves.
So, buckle up, and let’s tackle the basics.
What is Big Data?
Big Data is the collective term describing massive datasets of structured, unstructured, and semi-structured information. This data is collected from a variety of sources and is never-ending. Unfortunately, the data has little to no practical use due to its size and must be collected, analyzed, and processed into useful, actionable information.
Additionally, the nature of Big Data makes it too difficult for traditional data processing software to deal with. Consequently, new tools and disciplines have been developed to deal with Big Data's challenges.
Big Data is mined to acquire insights and is found in predictive modeling, machine learning projects, and other complex analytics applications. Organizations can monetize Big Data by using it to improve operations, offer their customers better service, and develop targeted, personalized marketing campaigns.
Now it’s time to look closely at each of the 5 V’s of Big Data.
The Characteristics of Big Data: Five V’s Explained
The characteristics of Big Data can be best explained with what is known as the five V’s of Big Data. A little alliteration goes far in helping us remember listed items, hence the 5 V’s arrangement.
Let’s start with the chief characteristic, especially since “Big Data” was first coined to describe the enormous amount of information. Thus, the Volume characteristic is the defining criterion for whether we can consider a dataset can be regarded as Big Data or not.
Volume describes both the size and quantity of the data. However, the definition of Big Data can change depending on the computing power available on the market at any given time. But regardless of the type of devices used to collect and process the data, it doesn’t change that Big Data’s volume is colossal, thanks to the vast number of sources sending the information.
Velocity describes how rapidly the data is generated and how quickly it moves. This data flow comes from sources such as mobile phones, social media, networks, servers, etc. Velocity covers the data's speed, and it also describes how the information continuously flows. For instance, a consumer with wearable tech that has a sensor connected to a network will keep gathering and sending data to the source. It’s not a one-shot thing. Now picture millions of devices performing this action simultaneously and perpetually, and you can see why volume and velocity are the two prominent characteristics.
Velocity also factors in how quickly the raw Big Data information is turned into something an organization will benefit from. When talking about the business sector, that translates into getting actionable information and acting on it before the competition does. For something like the healthcare industry, it's critical that medical data gathered by patient monitoring be quickly analyzed for a patient's health.
Variety describes the diversity of the data types and its heterogeneous sources. Big Data information draws from a vast quantity of sources, and not all of them provide the same level of value or relevance.
The data, pulled from new sources located in-house and off-site, comes in three different types:
- Structured Data: Also known as organized data, information with a defined length and format. An Excel spreadsheet with customer names, e-mails, and cities is an example of structured data.
- Unstructured Data: Unlike structured data, unstructured data covers information that can’t neatly fit in the rigid, traditional row and column structure found in relational databases. Unstructured data includes images, texts, and videos, to name a few. For example, if a company received 500,000 jpegs of their customers’ cats, that would qualify as unstructured data.
- Semi-structured Data: As the name suggests, semi-structured data is information that features associated information like metadata, although it doesn't conform to formal data structures. This category includes e-mails, web pages, and TCP/IP packets.
Veracity describes the data’s accuracy and quality. Since the data is pulled from diverse sources, the information can have uncertainties, errors, redundancies, gaps, and inconsistencies. It's bad enough when an analyst gets one set of data that has accuracy issues; imagine getting tens of thousands of such datasets, or maybe even millions.
Veracity speaks to the difficulty and messiness of vast amounts of data. Excessive quantities of flawed data lead to data analysis nightmares. On the other hand, insufficient amounts of Big Data could result in incomplete information. Astute data analysts will understand that dealing with Big Data is a balancing act involving all its characteristics.
Although this is the last Big Data characteristic, it’s by no means the least important. After all, the entire reason for wading through oceans of Big Data is to extract value! So unless analysts can take that glut of data and turn it into an actionable resource that helps a business, it’s useless.
So, value in this context refers to the potential value Big Data can offer and directly relates to what an organization can do with the processed data. The more insights derived from the Big Data, the higher its value.
What’s This About a 6th and 7th V?
Yes, some schools of thought add a sixth and even a seventh V entry to the characteristics of Big Data.
This characteristic shouldn’t be confused with Variety. If you go to a bakery and order the same doughnut every day and every day it tastes slightly different, that’s a measure of variability. The same situation apples to Big Data. If you constantly get different meanings from the same dataset, it can noticeably impact your data homogenization.
Variability considers the idea that a single word can have multiple meanings. For instance, the word “fold” can be used as a verb that describes bending a sheet of paper (but it also is an action word in cooking, so there’s even more variability!). But it could mean a crease, a bend in rocks, or a group of people united in a common interest or belief.
Since Natural Language Processing (NLP) often uses Big Data resources, it’s easy to see how the variability of language could affect AI and ML algorithms.
Terms keep changing, and the variability characteristic reflects this. Old words and meanings get discarded, and new definitions and words emerge. For example, remember that once upon a time, the term "awful" meant "worthy of respect or fear," not as a description of how you feel after drinking that milk that was way past its expiration date.
Humans are a visually oriented species. A picture is worth a thousand words, and charts and graphs can help readers understand huge amounts of complex better than reports riddled with formulae and numbers or endless spreadsheets.
So, the visualization characteristic deals with changing the immense scale of Big Data into something a resource that’s easy to understand and act on.
Visualization has been called Video on a few rare occasions.
And as if this wasn’t enough, you can Google “the 10 Vs of Big Data” and find even more V’s, such as Venue, Vocabulary, and Vagueness. However, this runs the risk of getting things out of hand, so let’s just stop at the five. Still, consider yourself warned!
How Would You Like to Become a Data Engineer?
Whether we’re talking about the characteristics of Big Data — five V’s, six V’s, or even ten V’s — it’s safe to say that the demand for Big Data-related professionals will remain strong. So, if you’re interested in having a career in a Big Data profession, such as a Data Engineer, Simplilearn has the resources you need.
The Caltech Post Graduate Program in Data Science, held in collaboration with IBM, offers masterclasses that impart job-critical skills like Big Data and Hadoop frameworks, and leverage Amazon Web Services' functionality (AWS). In addition, you will learn how to use database management tools and MongoDB through industry projects and interactive sessions. Finally, you will benefit from "Ask Me Anything" sessions conducted by IBM experts.
Glassdoor reports that Big Data Engineers in the United States earn an annual average of $125,531. Additionally, Glassdoor shows that Big Data Engineers in India make a yearly average of ₹754,830.
If the prospect of becoming a Big Data Engineer doesn’t interest you, Simplilearn offers other Big Data career options such as Big Data and Hadoop Training.
Big Data is here to stay and will keep presenting fantastic career opportunities for ambitious candidates who want to go far in today's information-driven world. So visit Simplilearn and get your start on a new, exciting career that offers new challenges, career stability, and excellent compensation and benefits