Our world has never been more technologically advanced. Technology is continuously bombarding us in all aspects of our lives. Mobile phones, social networks, streaming videos, and IoT (Internet of Things) have all contributed to the massive growth in data in recent decades.
If we can exploit, process, and show it properly, these data can become a useful means of imparting information and growing an organization. For example, we can figure out why a company ranks where it does in relation to competitors, make forecasts for future sales, and gain extensive market knowledge.
This article looks at the fundamentals of Big Data by going through the core principles, applications, and tools that any aspiring data scientist should be familiar with.
What is Big Data?
Big Data, a popular term recently, has come to be defined as a large amount of data that can’t be stored or processed by conventional data storage or processing equipment. Due to the massive amounts of data produced by human and machine activities, the data are so complex and expansive that they cannot be interpreted by humans nor fit into a relational database for analysis. However, when suitably evaluated using modern tools, these massive volumes of data provide organizations with useful insights that help them improve their business by making informed decisions.
Types of Big Data
As the Internet age continues to grow, we generate an incomprehensible amount of data every second. So much so that the number of data floating around the internet is estimated to reach 163 zettabytes by 2025. That’s a lot of tweets, selfies, purchases, emails, blog posts, and any other piece of digital information that we can think of. These data can be classified according to the following types:
Structured data has certain predefined organizational properties and is present in structured or tabular schema, making it easier to analyze and sort. In addition, thanks to its predefined nature, each field is discrete and can be accessed separately or jointly along with data from other fields. This makes structured data extremely valuable, making it possible to collect data from various locations in the database quickly.
Unstructured data entails information with no predefined conceptual definitions and is not easily interpreted or analyzed by standard databases or data models. Unstructured data accounts for the majority of big data and comprises information such as dates, numbers, and facts. Big data examples of this type include video and audio files, mobile activity, satellite imagery, and No-SQL databases, to name a few. Photos we upload on Facebook or Instagram and videos that we watch on YouTube or any other platform contribute to the growing pile of unstructured data.
Semi-structured data is a hybrid of structured and unstructured data. This means that it inherits a few characteristics of structured data but nonetheless contains information that fails to have a definite structure and does not conform with relational databases or formal structures of data models. For instance, JSON and XML are typical examples of semi-structured data.
Characteristics of Big Data
As with anything huge, we need to make proper categorizations in order to improve our understanding. As a result, features of big data can be characterized by five Vs.: volume, variety, velocity, value, and veracity. These characteristics not only assist us in deciphering big data but also gives us an idea of how to deal with huge, fragmented data at a controllable speed in an acceptable time period so that we can extract value from it, do real-time analysis, and respond promptly.
The prominent feature of any dataset is its size. Volume refers to the size of data generated and stored in a Big Data system. We’re talking about the size of data in the petabytes and exabytes range. These massive amounts of data necessitate the use of advanced processing technology—far more powerful than a typical laptop or desktop CPU. As an example of a massive volume dataset, think about Instagram or Twitter. People spend a lot of time posting pictures, commenting, liking posts, playing games, etc. With these ever-exploding data, there is a huge potential for analysis, finding patterns, and so much more.
Variety entails the types of data that vary in format and how it is organized and ready for processing. Big names such as Facebook, Twitter, Pinterest, Google Ads, CRM systems produce data that can be collected, stored, and subsequently analyzed.
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The rate at which data accumulates also influences whether the data is classified as big data or regular data. Much of this data must be evaluated in real-time; therefore, systems must be able to handle the pace and amount of data created. The processing speed of data means that there will be more and more data available than the previous data, but it also implies that the velocity of data processing needs to be just as high.
Value is another major issue that is worth considering. It is not only the amount of data that we keep or process that is important. It is also data that is valuable and reliable and data that must be saved, processed, and evaluated to get insights.
Veracity refers to the trustworthiness and quality of the data. If the data is not trustworthy and/or reliable, then the value of Big Data remains unquestionable. This is especially true when working with data that is updated in real-time. Therefore, data authenticity requires checks and balances at every level of the Big Data collecting and processing process.
The world around us is continuously changing; we now live in a data-driven era. From social media posts to the pictures we upload, big data applications are everywhere. Since Big Data is being created on a massive scale, it could become an important asset for many companies and organizations, helping them to come up with new insights and enhance their businesses.