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UOC Differences Between Structured and Unstructured Data Discussion

UOC Differences Between Structured and Unstructured Data Discussion

UOC Differences Between Structured and Unstructured Data Discussion

Question Description

I’m working on a computer science discussion question and need an explanation and answer to help me learn.

Post 1:- Structured data is where the information has been formatted and transformed into a well-defined data model. In the structured data, the raw data which is collected is mapped into the pre-defined fields which can be extracted and read easily through SQL commands. In general, the SQL relational databases consists of all the tables with both rows and columns, and this is considered to be the perfect example of structured data. When the structured data is utilized, the relational model will use memory as it minimizes the data redundancy (Azad et al., 2020). This makes the structured data to be more inter-dependent and less flexible. Some of the examples of structured data are data such as quantity, barcodes, weblog logistics, and more.

Semi-structured data is where the data is in between structure and unstructured. The data which has some consistent and definite characteristics in the data is called semi-structured data. In general, structured data is where the relational database properties are met, and in unstructured data, it does not confine to a rigid structure that is required by a relational database. Properties such as metadata or semantics are used in the semi-structured data to make it more manageable.

On the other hand, unstructured data is raw data that is collected. Raw data is complex and does not have any sort of formatting (Vest et al., 2017). The data collected from social media, IoT devices, online chats, emails, presentations, and other areas are considered to be unstructured data. Unstructured data requires more complex software to process the data, and the data can be used for business use.

Post 2:- A data set is considered structured if its elements can be addressed for successful analysis. Structured data is kept in a repository with a standard format, usually a database (Giudice et al., 2019). They are equipped with relational keys that provide easy mapping into standard data fields. Such records are now most handled in the advanced and easiest method of information management (Malik et al., 2020). A good example of structured data is Relational data.

Semi-structured data refers to a data that is not stored in a relational database but yet has some semblance of structure that facilitates analysis (Malik et al., 2020). Although it may be challenging to store semi-structured data in a relational database, the appropriate data models do exist to reduce storage requirements (Giudice et al., 2019). Semi-structured data is more flexible than structured data. It is also possible to version semi-structured data over tuples or graph. A good example of semi-structured data is the XML data.

Lastly, unstructured data refers to a data that cannot be stored in a traditional relational database because it lacks standardization and predetermined data format (Malik et al., 2020). Unstructured data is utilized by businesses in a wide range of BI and analytics applications, it is becoming more common in IT systems and necessitating new methods of storage and management (Giudice et al., 2019). Examples of semi-structured data are Word, PDF, text, and media logs. 

–Need response for the above 2 posts separately with at least 200 words each and one reference for each  

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