What are big data applications?

The term "Big Data" has recently gained popularity to describe large volumes of data that cannot be stored or processed by conventional means.

The …

Nov. 21, 2022 1 minute
What are big data applications?,Machine Learning (ML)

The term "Big Data" has recently gained popularity to describe large volumes of data that cannot be stored or processed by conventional means.

The vast amounts of human and machine-generated data are so complex and expansive that they cannot be analyzed by humans or stored in relational databases.

However, when analyzed effectively with modern tools, these enormous volumes of data can provide organizations with valuable insights that can help them improve their business.

Types of Big Data

We generate incredible amounts of data every second as the Internet age grows. Internet data is expected to reach 163 zettabytes by 2025. That's a lot of tweets, selfies, purchases, emails, blog posts, and anything else that can be thought of. According to their type, these data can be categorized as follows:

Structured data: Data that is structured has specific predefined organizational properties and can be sorted and analyzed more quickly than data that is unstructured.

Unstructured data: "unstructured data" refers to information with no predefined conceptual definition and is not easily analyzed or interpreted by standard databases or models.

Semi-structured data: An amalgamation of structured and unstructured data, semi-structured data is a hybrid of the two.

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Characteristics of Big Data

A big data set is characterized by five Vs.: volume, variety, velocity, value, and veracity. To extract value from big data, we must understand how to handle massive, fragmented data in an acceptable time frame and at a controllable speed.

Volume: Any dataset is characterized by its size. A Big Data system's Data volume is the amount of data generated and stored.

Variety: Variety refers to the different formats and ways in which data is organized and ready for processing.

Velocity: Whether data is classified as significant or regular depends on the rate at which it accumulates.

Value: Another major issue to consider is value. The amount of data we keep or process is one of many things that matter. It is also essential to save, approach, and evaluate valuable and reliable data to get insights.

Veracity: Data veracity refers to the data's trustworthiness and quality.

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Recent research has identified a phenomenon known as big data, which encompasses massive amounts of unprocessable data. There are three types of big data: structured, unstructured, and semi-structured. A big data set has the following characteristics: volume, variety, velocity, value, and veracity.