Data is then added to the data base and SQL DML is used to manage the database and extract data and answer business questions. The results are then reported and analyzed with a description provided....

Running Head: DATABASE DESIGN AND DATA MANAGEMENT TOOLS        1
DATABASE DESIGN AND DATA MANAGEMENT TOOLS                3
DATABASE DESIGN AND DATA MANAGEMENT TOOLS
Table of Contents
Differentiating among Structured, Semi-Structured and Unstructured Data with Business Examples    3
Structured Data    3
Semi-Structured Data    3
Unstructured Data    3
Limitations of Relational Databases and SQL in Dealing with Semi-Structured and Unstructured Data    4
Limitations of Structure:    4
Isolation of Information:    4
Cost:    4
Inflexible Model:    4
Performance:    5
Major Categories of Data Management Technologies using NoSQL Approaches, along with Advantages and Disadvantages Compared to Relational DBMSs    5
Key-Value Store Databases:    5
Column Oriented Databases:    5
Document Store Databases:    5
Graph Databases:    5
Object Oriented Databases:    6
Concept of Big Data, Reasons for its Emergence and its Potential Future Implications for Business Intelligence    6
References    8
Differentiating among Structured, Semi-Structured and Unstructured Data with Business Examples
Structured Data
The data, which can be searched and defined, is called structured data. It resides in a fixed field within a file or record. Structured data is typically stored in a relational database (RDBMS). It is a quantitative data. It can be easily analyzed. It is stored in data warehouse. It exists in predefined formats (Alharthi, Krotov & Bowman, 2017). The programming language used for structured data is SQL (Structured Query Language). Developed by IBM in the 1970s, SQL handles relational databases. Examples are credit card numbers, geolocation and stock information.
Semi-Structured Data
Semi-structured data falls between structured and unstructured data. It does not match the description of structured data entirely but it still employs tagging systems or other markers, separating different elements and enabling search. Sometimes, this is referred to as data with a self-describing structure. Onwujekwe, Osei-Bryson and Ngwum (2020) mentioned that it does not follow strict structural framework. Example is Email used in business on daily basis. Information such as name, email address, recipient, date and time are structured. However, the message it contains in body is unstructured. It does not follow any fixed format, yet conveys message to recipient.
Unstructured Data
Unstructured data is usually exists in its original form and is qualitative in nature. This data needs further processing to understand. It is stored in data lakes. It exists in a variety of formats. The amount of unstructured data available is much larger than that of structured data (Gupta, Tyagi, Panwar, Sachdeva & Saxena, 2017). It is stored in non-relational or NoSQL databases. Examples are social media interactions, customer surveys and interviews.
Limitations of Relational Databases and SQL in Dealing with Semi-Structured and Unstructured Data
Limitations of Structure:
It impose limits on the length of data fields. If more information is being added than it can accommodate, then that...