Big Data types Structured, Unstructured & Semi-structured
Writer : Mis. Rossela Ilkan
There has never been a time when technology has been so advanced in our world. We are constantly being bombarded by technology in every aspect of our lives. There has been a tremendous increase in data in recent decades due to the widespread use of mobile phones, social networks, streaming video, and IoT (Internet of Things).
It's possible to use these data to educate and grow an organization if we are able to exploit, process, and display them correctly. We can, for example, figure out why a business is ranked where it is in relation to competitors, make sales forecasts for the future, and gain extensive market knowledge through these techniques.
Here, we'll go over the fundamentals of Big Data by examining the core principles of Big Data, as well as its applications and tools.
What is Big Data?
Newly coined "Big Data" refers to data that can't be stored or processed by conventional data storage and processing equipment. Humans and machines are generating enormous amounts of data, and as a result, the data are becoming increasingly complex and expansive, making it impossible for them to be analyzed in a relational database. Large amounts of data can provide organizations with valuable insights that help them make better business decisions when properly analyzed with modern tools.
Types of Big Data
It's impossible to comprehend the amount of data we produce as the Internet era continues to grow. A staggering 163 zettabytes of information is expected to be disseminated online by the year 2025, according to current estimates. What a staggering amount of data we've generated in the form of social media posts and other forms of electronic communication. According to the following categories, these data can be broken down:
There are predefined organizational properties and a structured or tabular schema for structured data that makes it easier to analyze and sort. All fields are discrete and can be accessed either individually or in combination with data from other fields because they are predefined. Structuring the database allows you to quickly gather data from multiple locations in the system.
An unstructured dataset is one that does not have predefined conceptual definitions and cannot be analyzed using standard databases or data models. Information like dates, numbers, and facts make up a large portion of big data's unstructured content. Big data examples include video and audio files, mobile activity, satellite imagery, and NoSQL databases, to name a few. We contribute to the ever-expanding horde of unstructured data by taking photos and watching videos on social media platforms like Facebook and Instagram.
There are two types of data: structured and unstructured. There are some characteristics that structured data shares with unstructured data, but it also contains information that does not conform to relational databases or other formal data models. Semi-structured data includes formats such as JSON and XML.
Characteristics of Big Data
To gain a better grasp of something this large, we must first classify it properly. Because of this, the five Vs of big data: volume, variety, velocity, value, and veracity can be summarized as follows: In addition to helping us decipher big data, these characteristics also give us an idea of how to deal with huge, fragmented data at a controllable speed and within a reasonable time frame so that we can extract value from it, perform real-time analysis, and respond promptly.
The size of a dataset is always going to be a distinguishing factor. The amount of data generated and stored by a Big Data system is referred to as its volume. Data sets in the petabyte and exabyte range are being discussed. Processing such vast amounts of data calls for processing power far beyond the capabilities of a typical laptop or desktop CPU. Think of Instagram or Twitter as an example of a high-volume dataset. When it comes to social media, people spend a lot of time interacting with one another and sharing their thoughts. There is a huge amount of potential for analysis, finding patterns, and so much more with these ever-expanding datasets.
There are many different types of data that can be processed in a variety of ways. There is a lot of data that can be collected, stored, and then analyzed from big names like Facebook and Twitter, Pinterest, Google Ads, and CRM systems.
Depending on the rate at which data accumulates, a piece of data may be considered big data or regular data. Systems must be able to handle the rate and volume of data generated in order to analyze this information in real time. In other words, as processing speeds increase, more data will be available, but this also means that the speed at which that data is processed must increase.
Another important issue to consider is value. Keeping and processing a large amount of data isn't the only thing that matters. Data that is valuable and reliable, and data that must be saved, processed, and evaluated in order to gain insights, is also included in the definition of data.
The data's trustworthiness and quality are referred to as "veracity." The value of Big Data is unassailable if the data it contains is not trustworthy or reliable. With real-time data, this becomes even more important. There must be checks and balances throughout the Big Data collecting and processing process in order to ensure data integrity.
Everything around us is changing at a rapid pace; we are now in an era fueled by data. Big data applications can be found in everything from our social media updates to the photos we post. A massive amount of data is being generated, making it a valuable asset for many businesses and organizations, allowing them to come up with new ideas and improve their operations.
Is Big Data being utilized by anyone? There are Five Uses for This Information.
Big Data is better understood by those who use it. Here are a few examples of such businesses:
It is already making a huge difference in the healthcare industry thanks to Big Data. Health care providers and HCPs are now able to provide more customized care to their patients thanks to advances in predictive analytics. In addition, Big Data and AI-driven technologies like wearable fitness monitors, telemedicine, and remote monitoring are improving people's lives.
Today, Big Data is also being used to improve education. There are a plethora of online educational courses available, so learning is no longer restricted to the four walls of a traditional classroom. Digital courses powered by Big Data technologies are being invested in by academic institutions in order to aid the development of budding learners.
Big Data is used by the banking industry to detect fraud. Tools based on Big Data can quickly identify fraudulent activity such as credit/debit card fraud and other types of data manipulation errors in real time.
In manufacturing, improving supply strategies and product quality are the most significant advantages of Big Data, according to the TCS Global Trend Study. This helps to create a transparent infrastructure in the manufacturing sector, allowing for the prediction of inconsistencies and incompetences that could harm the business.
Big Data is being used by IT companies around the world, one of the largest users of the technology, to improve their own operations, boost employee output, and reduce operational risks. ML and AI, along with Big Data technologies, are enabling the IT industry to find new solutions to even the most difficult problems.
- How Big Is Big Data
- Big Data Tools Characteristics
- Examples And Use Cases Of Big Data
- About Big Data
- Big Data Types
- What Is Big Data Technology
- What Is Big Data Analytics