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What is Big Data Analytics?

We first need to ask ourselves what Data is. Data is any kind of information available to us in form of numbers, words, pictorial representations etc.; information that we perceive and derive meaning from. In coding and software languages, data becomes the numerical values that we come across. As the name suggests, Big Data is the kind of data that is too big, sometimes so vast that it cannot be easily perceived. So how do we handle such big data available to us? We use Big Data Analytics. This technique is used to perceive large amounts of data to derive meaningful information from it.

For example, you are required to monitor the shopping habits of consumers. You develop a software that records how the consumer takes in the market and what drives them to choose an item over the other. This type of study is done on a large group, therefore the data available to you will be extensive to support your study. Big Data Analytics technique enables you to make sense of the data and conclude the results from it. Online consumer market giants like Amazon and Google use this technique to analyze your preferences and help you find the items best suited to you.

Is Big Data really that big?

Yes. With 7.8 billion people inhabiting the earth and engaging each day with each other either directly or indirectly, the data they produce in the process is bound to be big. Half of this number is on the internet, interacting with various sites. According to a report by Forbes, 2.4 quintillion bytes of data is produced each day. Facebook, the biggest social media site, saw 2.7 billion monthly active users in the second half of 2020, according to a Statista article.

What are the characteristics of Big Data?

There are 3Vs of Big Data that help companies grow in their domains.

Volume: Volume is the data that is generated. As we discussed earlier, there is an exponential growth of data every second in the world. Companies buy cloud storage to record this substantial data in order to analyze this at a later stage.

Velocity: Velocity is the speed at which the data is generated and received. This is the calculation of the speed at which the data flows on sites, such as application forms, social media sites, etc.

Variety: This refers to the sources, types, quality, and various other factors that distinguish the data. This can range from structured to non-structured data such as images, audios, gifs, transactions etc.

Why is Big Data Analytics important?

In today’s world where consumer is the focus of every industry, keeping track of behaviours and habits becomes important. Companies need to be up-to-date with information in order to cater to the mass. With over 1gb data produced every second, companies need to find a way to analyze it quicker and more efficiently. The technique enables the organisations to tap into newer business opportunities and keep themselves ahead in the game in this competitive world. This not only helps them to reduce cost and make decisions faster, but also gage the right products according to the demand, ensuring satisfaction at the consumer’s end. Also, there is a need to keep record of what is happening around the world and what minor or major events take place and when.

How to learn Big Data Analytics?

In order to learn Big Data Analytics, one starts with the basic tools such as scripting and cloud. Many online portals and websites provide courses on Big Data Analysis. It can be taken up as a subject during your college semester or as an additional skill building course. Xebia Academy’s Master in Big data Architect provides an indepth leaning of essential tools, with hands-on training sessions and rigorous learning methodologies.

But in order to make a fulfilling career out of Big Data Analytics, you need to recognize the expectations, requirements and the skills needed to excel in the field.

-Understanding of data tools and skills is important. You need to be well-versed in Big data concepts, learning how tables, data and everything else works.

-Learning Python, Java, Scale can enable you to progress further in your journey of learning Big Data Analytics.

-UNIX, LINUX, and scripting are the tools that you need to develop an understanding of.

Choosing Big Data Analytics as a career option

As the world runs on data and its analysis, choosing to be a part of the process can be beneficial. This is a profession that is in high demand and requires efficient individuals to drive the projects. The career is easy to start, with the requirement of languages such as Java and Python and scripting before starting.

Companies such as HP, IBM, Microsoft, American Express, BDO, Capital One, Netflix, etc. use big data to cater to the demands of the market and run their businesses. Big Data has a high demand in the world and if you possess the correct skill-set, you can be a part of such giant firms.

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