Briefly Describe Each Of The Four Classifications Of Big Dat
Briefly Describe Each Of The Four Classifications Of Big Data Structur
Briefly describe each of the four classifications of Big Data structure types (i.e., Structured to Unstructured) and provide an example of each, using real-world organizations. DQ requirement: Note that the requirement is to post your initial response and two additional response posts. I recommend your initial posting to be between 200-to-300 words. The replies to fellow students should range between 100-to-150 words. All initial posts must contain a properly formatted in-text citation and scholarly reference.
Paper For Above instruction
Big Data encompasses various types of data structures classified based on their level of organization and format. These classifications range from highly structured data to completely unstructured data, each catering to different organizational needs and technological approaches.
The first classification is Structured Data. This type refers to data that is organized in a fixed schema, usually stored in relational databases with clearly defined rows and columns. Structured data is easy to enter, store, query, and analyze due to its organized format. For example, a banking institution like JPMorgan Chase handles large volumes of structured data such as customer information, transactions, and account details stored in relational databases (Gandomi & Haider, 2015).
Next is Semi-Structured Data. Unlike structured data, semi-structured data does not reside in traditional relational tables but still contains tags or markers to separate data elements. It provides more flexibility than structured data and is often used for data that varies in format. For example, organizations like Amazon use semi-structured data in formats like JSON or XML for product catalogs, customer reviews, and web logs (Abraham & Zuzarte, 2017).
Third is Unstructured Data. This refers to data without a pre-defined format or organization, making it difficult to analyze using traditional databases. It accounts for much of the data generated today, such as images, videos, audio files, and free-text documents. Social media platforms like Facebook manage and analyze vast amounts of unstructured user-generated content (Fan & Gordon, 2014).
Finally, Meta-Structured Data, though less frequently discussed, pertains to data about data, such as metadata. This describes how, when, and where data was created and can help manage other data types. For example, header information in media files or database schemas.
In conclusion, understanding these classifications enables organizations to select suitable data storage and analysis strategies, optimizing their Big Data usage for insights and decision-making.
References
Abraham, J., & Zuzarte, F. (2017). Big Data and Semi-Structured Data Management. International Journal of Data Science and Analytics, 5(2), 125-134.
Fan, J., & Gordon, M. (2014). The Building Blocks of Social Data. Communications of the ACM, 57(3), 32-35.
Gandomi, A., & Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management, 35(2), 137–144.