All you need to know about big data’s size

Have you ever heard of the term “big data”? You’ve probably heard it. Big Data has been a topic of conversation for the previous four to five years. Do you truly comprehend what big data implies, how it affects our lives, and why employers are looking for professionals with big data expertise?

Big Data Tutorial

I will offer you a thorough grasp of big data in this big data tutorial and guest posting. Visit the Big Data Course to learn more.

The topics I will cover in this big data tutorial are listed below:

What is Big Data, exactly?

Driving Forces of Big Data

Characteristics of Big Data

Many forms of big data

Several instances of Big Data

The uses for big data

The difficulties of big data

What is Big Data, exactly?

Big Data is a term used to describe a collection of enormous and intricate data collections. Utilizing the tools for managing databases that are available or using traditional data processing software, they are challenging to manage and store. The challenge lies in gathering, organizing, storing, transferring, analyzing, and displaying the data.

Driving Forces of Big Data

Numerous factors are causing the amount of data on the planet to grow quickly. Numerous sources and our regular activities generate a lot of information. Since the invention of the internet, the entire world has gone online, and everything we do leaves a digital trail. The rate has quickened as more smart objects are being brought online and data volume has increased. Social media platforms, sensor networks, digital photographs and videos, mobile phone transactions, purchase records, web logs, health records, archival security for the military, business-complicated scientific research, and more are the main sources of big data. These data sources together contain roughly a quintillion bits of information. The amount of data will increase to 40 Zettabytes in 2020, which is the same as multiplying each sand grain on earth by 75.

Characteristics of Big Data

Volume Velocity, Variety Value, Veracity, and Reliability are the five characteristics that make up Big Data.


The term “volume” refers to the “quantity of data,” which is growing rapidly every day. Computers, people, and their activities on social media all generate tremendous amounts of data. According to research, the amount of data generated in 2020 might reach up to forty Zettabytes (40,000 Exabytes), a 300-fold increase from 2005. 


The rate at which various sources provide data each day is referred to as velocity. Huge and continuous data flows are present. Currently, there are 1.03 billion daily active users (Facebook DAU) on mobile, which is a 22% growth from the previous year. This demonstrates how quickly data is generated every day and how many people are using Facebook on social media. You can gain insights and take real-time actions based on data if you can keep up with the pace and volume.


There are many sources that contribute to Big Data, and the types of data that they provide vary. It could be unstructured, partially structured, or both. As a result, a variety of data is produced every day. Previously, we would collect the data through databases and Excel, but now it can also come in the form of audio, video, sensor data, and more, as shown in the figure below. Since this type of unstructured data is difficult to collect, store, mine, and analyze, it presents issues in all of these areas.


Following the discussion of volumes, velocity, variety, and veracity, there is still one more V to consider when studying big data, namely value. Massive amounts of data are wonderful, but unless we can use them to create something valuable, they aren’t worth having access to. I’m talking about converting it into value; is it helping the businesses who are analyzing enormous volumes of data succeed? Are businesses using big data seeing a great return on their investments? It is unsuccessful if it does not increase their earnings by utilising Big Data.

Many forms of big data


Structured data is defined as data that has been processed and saved in a preset format. Structured data is a type of data that is stored in relational database management systems (RDBMS). Because it has a set format, processing structured data on it is straightforward. To manage these types of Data, Structured Query Language (SQL) is widely employed.


Data that lacks a formal structure, such as an underlying table definition in a relational DBMS, is referred to as semi-structured data. To differentiate semantic pieces, it is structured according to attributes like tags and other markers, making it simpler to analyze. Semi-structured data can be found in XML documents, commonly referred to as JSON documents.


Without being changed to a structured format, the data that lacks shape cannot be recorded in RDBMS and cannot be examined. It is referred to as unstructured data. Unstructured examples include text files and multimedia files like audio, video, and image files. Unstructured data is expanding more quickly than other categories, according to experts, and around 80% of the information in a company is unstructured.

Several instances of Big Data

  • More than a million consumer transactions can be handled by Walmart in an hour.
  • More than 30 petabytes of user-generated data are stored, retrieved, and analyzed by Facebook.
  • Every single day, more than 230 million tweets are produced.
  • In the entire world, more than five billion people use mobile devices for calling, texting, tweeting, and surfing.
  • Every minute of every day, the YouTube community posts a new video for 48 hours.
  • Amazon manages the daily clickstream data of fifteen million customers in order to provide product recommendations.
  • Every day, 294 billion emails are sent. Services look at these statistics to identify spam.
  • Nearly 100 sensors are included in modern cars, and they track tire pressure and fuel levels. Every car generates a lot of sensor data.

The uses for big data

Without mentioning the people who gain from Big Data applications, we cannot talk about data. Applications for big data are used in almost every industry.

Smarter Healthcare: Using petabytes of patient data The company is able to gather useful data and create programs that can predict a patient’s condition in advance.

Telecom Telecom businesses gather information, evaluate it, and offer solutions for a range of problems. In order to maintain constant contact with their customers, telecom companies have been using Big Data applications to significantly reduce the loss of data packets that happens when networks are overloaded.

Retail: One of the greatest consumers of big data, retail has some of the slimmest profit margins. The advantage of using big amounts of data for retail is the capacity to study consumer behavior. The recommendations made by Amazon’s recommendation engine are based on previous browsing patterns of the user.

Large volumes of manufacturing data analyzed in manufacturing analytics can assist decrease component failures, improve product quality, increase efficiency, and help save time and money.

High Search Quality Every time we acquire data from Google, we also generate data to back it up. Google keeps track of this information and uses it to improve search results.

The difficulties of big data

Excellent Data The fourth letter of V, or veracity, is where the problem is in this case. This information is disorganized, contradictory, and incomplete. Companies in the United States lose $600 billion each year due to incomplete data.

Discovery The Discovery of HTML0 Finding an unmarked needle in a haystack is how one gains insight into big data. It is quite challenging to analyze petabytes and gigabytes of data using strong algorithms to find patterns and insight.

Security Security is another issue because the information is so large. This involves authenticating users by limiting access based on the user’s identity, keeping track of previous access attempts, properly encrypting data, and other measures.

Lack of Talent Big Data initiatives abound across large organizations, but it can be difficult to assemble a talented team of data scientists, developers, and analysts with sufficient domain knowledge.

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