Updated on 26th Jun, 21 17229 Views


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 analogue 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 any more.

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Here comes the concept of Big Data. Before delving any further into this blog, let’s have a look at the list of topics that will be covered here:
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.

What is Big data

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

Big data sets

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.

contributing in the creation of big data

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 what not.

All of this adds up to the stockpile of data.

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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 like 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.

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Let’s check out each and every one of them, individually.

Characteristics of Big Data Details
Volume Organisations have to constantly scale their storage solutions since big data clearly requires large amount of space to be stored.
Velocity Since big data is being generated every second, organisations need to respond in real time to deal with it.
Variety Big data comes in variety of forms. It could be structured or unstructured, or even in different formats such as text format, videos, images, and more.
Veracity Big data, as large as it is, can contain wrong data too. Uncertainty of data is something organisations have to consider while dealing with big data.
Value Just collecting big data and storing it is of no consequence unless the data is analyzed and a useful output is produced.

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 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 organisations 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

Huge amount of data in organisations can easily become a target for advanced persistent threats, so here lies another challenge for organisations 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.

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Big Data Frameworks

  • Apache Hadoop is a framework that allows parallel data processing and distributed data storage.
  • Apache Spark is a general-purpose distributed data processing framework.
  • Apache Kafka is a stream processing platform.
  • Apache Cassandra is a distributed NoSQL database management system.

Big data frameworks

These are some of the many technologies that are used to handle and manage big data. Hadoop is the most widely used among them. If you wish to learn more about Big Data and Hadoop, along with a structured training program, visit HERE.

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. Some of the hot job profiles are given below:

  • Data Analysts analyze and interpret data, visualize it, and build reports to help make better business decisions.
  • Data Scientists mine data by assessing data sources and use algorithms and Machine Learning techniques.
  • Data Architects design database systems and tools.
  • Database Managers control database system performance, perform troubleshooting, and upgrade hardware and software.
  • Big Data Engineers design, maintain, and support Big Data solutions.

Once we learn Big Data and understand its use, we will come to know that there are many analytics problems we can solve which were earlier not possible due to technological limitation. Organizations are now relying more on this cost-effective and robust method for easy data processing and storage of huge volumes of data.

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