Big Data Tutorial For Beginners

By Author: John Smith
Total Articles: 1
Comment this article

Introduction

With the evolution of the Internet, the ways how businesses, economies, stock markets, and even the governments function and operate have also evolved, big time. It has also changed the way people live. With all of this happening, there has been an observable rise in all the information floating around these days; it’s more than ever before. This outburst of data is relatively new. Before the past couple of years, most of the data was stored on paper, film, or any other analog media; only one-quarter of all the world’s stored information was digital. But with the exponential increase in data, the idea of storing it manually just does not hold appeal anymore.

What is Big Data?

The conventional way in which we can define big data is, It is a set of extremely large data so complex and unorganized that it defies the common and easy data management methods that were designed and used up until this rise in data.

Big data sets can’t be processed in traditional database management systems and tools. They don’t fit into a regular database network.

But, how is big data even getting ...
... created?

Do we have any role in that?

To find the answers to these questions, let’s move on to the next topic.

How are we contributing to the creation of Big Data?
Every time one opens an application on his/her phone, visits a web page, signs up online on a platform, or even types into a search engine, a piece of data is gathered.

So, whenever we turn to our search engines for answers a lot of data is created and gathered.

But as users, we are usually more focused on the outcomes of what we are performing on the web. We don’t dwell on what happens behind the scenes. For example, we might have opened up our browser and looked up for ‘big data,’ then visited this link to read this blog. That alone has contributed to the vast amount of big data. Now imagine, the number of people spending time on the Internet visiting different web pages, uploading pictures, and whatnot.

Characteristics of Big Data
There are some terms associated with big data that actually help make things even clearer about big data. These are essentially called the characteristics of big data and are termed as volume, velocity, and variety, giving rise to the popular name 3Vs of big data, which I am sure we must have heard before. But, if it feels new to you, do not worry. We are going to discuss them in detail here. As people are understanding more and more about the ever-evolving technological term, big data, it shouldn’t come as a shock if more characteristics are added to the list of the 3Vs. These are called veracity and value.

If you wish to learn more about Big Data and Hadoop, along with a structured training program, visit our Hadoop classes in Pune.

Challenges of Big Data

It must be pretty clear by now that while talking about big data one can’t ignore the fact that there are some obvious challenges associated with it. So moving forward in this blog, let’s address some of those challenges.

Quick Data Growth
Data growing at such a quick rate is making it a challenge to find insights from it. There is more and more data generated every second from which the data that is actually relevant and useful has to be picked up for further analysis.

Storage
Such a large amount of data is difficult to store and manage by organizations without appropriate tools and technologies.

Syncing Across Data Sources
This implies that when organizations import data from different sources the data from one source might not be up to date as compared to the data from another source.

Security
A huge amount of data in organizations can easily become a target for advanced persistent threats, so here lies another challenge for organizations to keep their data secure by proper authentication, data encryption, etc.

Unreliable Data
We can’t deny the fact that big data can’t be 100 percent accurate. It might contain redundant or incomplete data, along with contradictions.

Miscellaneous Challenges
These are some other challenges that come forward while dealing with big data, like the integration of data, skill and talent availability, solution expenses, and processing a large amount of data in time and with accuracy so that the data is available for data consumers whenever they need it.

Technologies and Tools to Help Manage Big Data
Before we go further into getting to know technologies that can help manage big data, we should first get familiar with a very popular programming paradigm called MapReduce.

What it does is, it allows performing computations on huge data sets on multiple systems in a parallel fashion.

MapReduce mainly consists of two parts: the Map and the Reduce. It’s kind of obvious! Anyway, let’s see what these two parts are used for:

Map: It sorts and filters and then categorizes the data so that it’s easy to analyze it.
Reduce: It merges all data together and provides the summary.

Applications of Big Data
Big data has many applications in various industries. Let’s find out some of them in brief.

Fraud Detection
Big data helps in risk analysis and management, fraud detection, and abnormal trading analysis.

Advertising and Marketing
Big data helps advertising agencies understand the patterns of user behavior and then gather information about consumers’ motivations.

Agriculture
Big data can be used to sensor data to increase crop efficiency. This can be done by planting test crops to record and store the data about how crops react to various environmental changes and then using that data for planning crop plantation, accordingly.

Job Opportunities in Big Data
Knowledge about big data is one of the most important skills required for some of the hottest job profiles which are in high demand right now and the demand in these profiles won’t be dropping down any time sooner, because, honestly, the accumulation of data is only going to increase over time, increasing the number of talents required in this field, thus opening up multiple doors of opportunities for us.

Want to know more then, Learn Big Data from our tutorial

Having more than 3 years of experience in AI/ML & Data Science with expertise in providing Enterprise Performance Engineering & integrated solutions.