Which of the Following Is Not a Data Type of Big Data?
When it comes to big data, there are various types of data that can be analyzed to gain insights and make informed decisions. These data types range from structured to unstructured, and from internal to external sources.
However, not all types of data fall under the umbrella of big data. Let’s take a closer look at the different data types associated with big data and identify which one is not considered a part of it.
Structured Data:
Structured data refers to information that is organized in a specific format or schema, making it easy to search, analyze, and process. It is highly organized and usually fits neatly into rows and columns, similar to traditional databases. Examples of structured data include financial records, customer information, sales figures, and transactional data.
Unstructured Data:
In contrast to structured data, unstructured data does not have a predefined format or organization. It includes text documents, images, videos, social media posts, emails, web pages, sensor logs, and more. Unstructured data is challenging to process using traditional methods due to its sheer volume and complexity.
Semi-Structured Data:
Semi-structured data lies somewhere between structured and unstructured data. It possesses some organizational properties but does not adhere strictly to predefined schemas. Examples include XML files or JSON documents that have some structure but also allow for flexibility in terms of adding or modifying fields.
Meta Data:
Metadata provides information about other types of data but is not considered raw or primary information itself. It describes the characteristics or properties of other datasets such as their source, format, size, creation date, authorship details, etc. Metadata plays a vital role in data management and is often used to improve data discovery and accessibility.
Which of the Following Is Not a Data Type of Big Data?
From the mentioned data types, metadata is not typically considered a primary type of big data. While it is an essential component for managing and organizing big data, it does not represent the raw information that is analyzed to extract insights or patterns. Instead, metadata provides context and additional information about the datasets themselves.
In conclusion, structured, unstructured, and semi-structured data are all key components of big data. They represent the diverse range of information that can be analyzed to derive valuable insights. On the other hand, metadata serves as supporting information that enhances the understanding and accessibility of big data but is not directly included in its primary analysis.