Compare: What is similar? Contrast: What is different? Watch: Database Management System (EG: Oracle, MySQL, Mongodb) versus Database Type (EG: Relational, NoSQL) – Text, Chapter 5 – Codd : Relational Data Model – SQL Versus NoSQL Movement with Big Data Analytics, Venkatraman et al Wiki: – – ———————— 1. Format – Max 10 Points – Points will be deducted if Required format for essays (see Main Moodle Page for our class) is not followed 2. References – Max 10 Points – Points will be deducted if references are missing 3. Content – Max 80 Points 3.a (20 points) Word Count: Must be between 700 and 800 words. Points deducted for too few or too many words, 3.b (20 points) Spelling/Grammar: Points deducted for excessive mistakes. 3.c (20 points) Quoted Sources: Points will be deducted for excessive quoting. 3.d (20 points) Your comments and ideas: Points will be deducted if your essay does not include original comments, in your own words.

Database Management Systems (DBMS) and database types are both essential components of managing data effectively. However, they differ in certain aspects, including their format, purpose, and functionality. In this essay, we will compare and contrast DBMS, specifically Oracle, MySQL, and MongoDB, with database types, namely relational and NoSQL databases. The focus will be on Codd’s Relational Data Model and the SQL versus NoSQL movement in the context of big data analytics.

Firstly, let us consider the format of these two entities. DBMS, such as Oracle, MySQL, and MongoDB, are software systems that allow users to create, manipulate, and manage databases. These systems provide a structured approach to store, retrieve, and update data efficiently. On the other hand, database types refer to the organization and structure of data within a database. Relational databases follow a tabular format, where data is stored in tables with predefined relationships between entities. NoSQL databases, on the other hand, provide a more flexible structure, allowing for the storage of unstructured and semi-structured data.

Secondly, the purpose of DBMS and database types also differs. DBMS primarily aim to facilitate the management, storage, and retrieval of structured data. They provide functionalities such as data integrity, concurrency control, and query optimization. Oracle, MySQL, and MongoDB are examples of DBMS that excel in these areas. On the other hand, database types are designed to address specific requirements and use cases. Relational databases, based on Codd’s Relational Data Model, are suitable for applications that require complex relationships between entities and strict data integrity. NoSQL databases, on the other hand, are better suited for handling large volumes of unstructured data and accommodating flexible data models.

Lastly, let us consider the functionality provided by these two entities. DBMS offer a wide range of features, including data modeling, data querying, indexing, and transaction management. Oracle, MySQL, and MongoDB provide robust query languages, namely SQL for relational databases and a proprietary query language for MongoDB. These DBMS also support transactions to ensure data consistency and concurrency control. Conversely, database types handle data differently. Relational databases excel in providing strong consistency and ACID (Atomicity, Consistency, Isolation, Durability) properties. NoSQL databases, however, prioritize scalability, high availability, and eventual consistency. They relax some of the ACID properties in favor of distributed data processing and horizontal scaling.

In conclusion, while DBMS and database types are related, they differ in their format, purpose, and functionality. DBMS, exemplified by Oracle, MySQL, and MongoDB, are software systems that facilitate the management of structured data. On the other hand, database types, such as relational and NoSQL databases, refer to the organization and structure of data. Relational databases follow a tabular format with predefined relationships, while NoSQL databases offer a flexible structure to handle unstructured data. Furthermore, DBMS focus on providing features for data management and manipulation, while database types prioritize specific requirements and use cases. Understanding the similarities and differences between these entities is crucial for effective data management in various applications.