Vishesh Arora — May 20, 2021
Beginner Big data Structured Data Unstructured Data

This article was published as a part of the Data Science Blogathon

Introduction

We produce a massive amount of data each day, whether we know about it or not. Every click on the internet, every bank transaction, every video we watch on YouTube, every email we send, every like on our Instagram post makes up data for tech companies.

With such a massive amount of data being collected, it only makes sense for companies to use this data to understand their customers and their behavior better. This is the reason why the popularity of Data Science has grown manifold over the last few years.

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Structured vs. Unstructured Data

Before we deep dive into the nuances of Big Data, it is important to understand the different kinds of data, namely structured and unstructured data.

Structured data includes quantitative data that is stored in an organized manner. It consists of numerical and text data. It is easy to analyze and process structured data. It is generally stored in a relational database and can be queried using Structured Query Language (SQL).

Unstructured data includes qualitative data that lacks any predefined structure and can come in a variety of formats (images, mp3 files, wav files, etc.). Unstructured data is said to lack “structure”. It is stored in a non-relational database and can be queried using NoSQL.

There can be semi-structured data as well, which lies somewhat in between structured and unstructured data.

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What is Big Data?

Big data is exactly what the name suggests, a “big” amount of data. Big Data means a data set that is large in terms of volume and is more complex. Because of the large volume and higher complexity of Big Data, traditional data processing software cannot handle it. Big Data simply means datasets containing a large amount of diverse data, both structured as well as unstructured.

Big Data allows companies to address issues they are facing in their business, and solve these problems effectively using Big Data Analytics. Companies try to identify patterns and draw insights from this sea of data so that it can be acted upon to solve the problem(s) at hand.

Although companies have been collecting a huge amount of data for decades, the concept of Big Data only gained popularity in the early-mid 2000s. Corporations realized the amount of data that was being collected on a daily basis, and the importance of using this data effectively.

What are the 5 Vs of Big Data?

Doug Laney introduced this concept of 3 Vs of Big Data, viz. Volume, Variety, and Velocity.

Volume refers to the amount of data that is being collected. The data could be structured or unstructured.

Velocity refers to the rate at which data is coming in.

Variety refers to the different kinds of data (data types, formats, etc.) that is coming in for analysis.

Over the last few years, 2 additional Vs of data have also emerged – value and veracity.

Value refers to the usefulness of the collected data.

Veracity refers to the quality of data that is coming in from different sources.

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Applications in the real world

Big Data helps corporations in making better and faster decisions, because they have more information available to solve problems, and have more data to test their hypothesis on.

Customer experience is a major field that has been revolutionized with the advent of Big Data. Companies are collecting more data about their customers and their preferences than ever. This data is being leveraged in a positive way, by giving personalized recommendations and offers to customers, who are more than happy to allow companies to collect this data in return for the personalized services. The recommendations you get on Netflix, or Amazon/Flipkart are a gift of Big Data!

Machine Learning is another field that has benefited greatly from the increasing popularity of Big Data. More data means we have larger datasets to train our ML models, and a more trained model (generally) results in a better performance. Also, with the help of Machine Learning, we are now able to automate tasks that were earlier being done manually, all thanks to Big Data.

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Demand forecasting has become more accurate with more and more data being collected about customer purchases. This helps companies build forecasting models, that help them forecast future demand, and scale production accordingly. It helps companies, especially those in manufacturing businesses, to reduce the cost of storing unsold inventory in warehouses.

Big data also has extensive use in applications such as product development and fraud detection.

How to store and process Big Data?

The volume and velocity of Big Data can be huge, which makes it almost impossible to store it in traditional data warehouses. Although some and sensitive information can be stored on company premises, for most of the data, companies have to opt for cloud storage or Hadoop.

Cloud storage allows businesses to store their data on the internet with the help of a cloud service provider (like Amazon Web Services, Microsoft Azure, or Google Cloud Platform) who takes the responsibility of managing and storing the data. The data can be accessed easily and quickly with an API.

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Hadoop also does the same thing, by giving you the ability to store and process large amounts of data at once. Hadoop is an open-source software framework and is free. It allows users to process large datasets across clusters of computers.

Challenges

1. Data growth

Managing datasets having terabytes of information can be a big challenge for companies. As datasets grow in size, storing them not only becomes a challenge but also becomes an expensive affair for companies.

To overcome this, companies are now starting to pay attention to data compression and de-duplication. Data compression reduces the number of bits that the data needs, resulting in a reduction in space being consumed. Data de-duplication is the process of making sure duplicate and unwanted data does not reside in our database.

2. Data security

Data security is often prioritized quite low in the Big Data workflow, which can backfire at times. With such a large amount of data being collected, security challenges are bound to come up sooner or later.

Mining of sensitive information, fake data generation, and lack of cryptographic protection (encryption) are some of the challenges businesses face when trying to adopt Big Data techniques.

Companies need to understand the importance of data security, and need to prioritize it. To help them, there are professional Big Data consultants nowadays, that help businesses move from traditional data storage and analysis methods to Big Data.

3. Data integration

Data is coming in from a lot of different sources (social media applications, emails, customer verification documents, survey forms, etc.). It often becomes a very big operational challenge for companies to combine and reconcile all of this data.

There are several Big Data solution vendors that offer ETL (Extract, Transform, Load) and data integration solutions to companies that are trying to overcome data integration problems. There are also several APIs that have already been built to tackle issues related to data integration.

The future of Big Data

The volume of data being produced every day is continuously increasing, with increasing digitization. More and more businesses are starting to shift from traditional data storage and analysis methods to cloud solutions. Companies are starting to realize the importance of data. All of these imply one thing, the future of Big Data looks promising! It will change the way businesses operate, and decisions are made.

EndNote

In this article, we discussed what we mean by Big Data, structured and unstructured data, some real-world applications of Big Data, and how we can store and process Big Data using cloud platforms and Hadoop.

The author of this article is Vishesh Arora. You can connect with me on LinkedIn.

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