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DBFN212 Database Fundamentals Assignment Sample


Students are required to write a Reflective Journal in which they reflect on unit content and learning experiences between weeks 1 and 11. In this assignment you should describe an interesting or important aspect of each week’s content/experiences, analyse this aspect of the week critically by incorporating and discussing academic or professional sources, and then discuss your personal learning outcomes.

The document structure is as follows (2500 words):

1. Title page

2. Introduction (~150 words)

a. Introduce the focus of the unit and the importance of the unit to your chosen professional area. Provide a preview of the main experiences and outcomes you discuss in the body of the assignment.

3. Body: Reflective paragraphs for each week from week 1 to week 11 (1 paragraph per week, ~200 words per paragraph). In each reflective paragraph:

a. DESCRIPTION (~50 words): Describe the week

- Generally, what was the focus of this week’s lecture and tutorial?

- What is one specific aspect of the week’s learning content that was interesting for you? (e.g. a theory, a task, a tool, a concept, a principle, a strategy, an experience etc.)? Describe it and explain why you chose to focus on it in this paragraph. (*Note: a lecture slide is not an acceptable choice, but an idea or concept on it is)

b. ANALYSIS (~75 words): Analyse one experience from the week

- Analyse the one specific aspect of the week you identified above.

- How did you feel or react when you experienced it? Explain.

- What do other academic publications or professional resources that you find in your own research say about this? (include at least 1 reliable academic or professional source from your own research). Critically analyse your experience in the context of these sources.

c. OUTCOMES (~75 words): Identify your own personal learning outcomes

- What have you learned about this aspect of the unit?
- What have you learned about yourself?
- What do you still need to learn or get better at?
- Do you have any questions that still need to be answered?
- How can you use this experience in the future when you become a professional?

4. Conclusion (~100 words): Summarise the most important learning outcomes you experienced in this unit and how you will apply them professionally or academically in the future.

5. Reference List

Your report must include:

- At least 10 references, 5 of which must be academic resources, 5 of which can be reliable, high-quality professional resources.
- Use Harvard referencing for any sources you use.
- Refer to the Academic Learning Support student guide on Reflective Writing and how to structure reflective paragraphs.



A database management system is essential because it properly manages data and allows individuals to do a variety of tasks with ease. A database management system (DBMS) is a software application that stores, organizes and manages large amounts of data (Yunus et al. 2017, pp.192-194). The application of this technology enhances the efficiency of business operations while cutting overall costs. In this course, we learned about relational databases, SQL statements to extract information to meet business reporting demands, creating entity relationship diagrams (ERDs) to construct databases, and analysing table designs for unnecessary redundancy (Astrova 2009, pp. 415-424).

Hence, we mainly learn about the main aspects of Database Management Systems and alongside we will also get to know how the Databases are managed, built, their types and how they are integrated with different web services for best assignment help.

Week 1: Databases

The focus of this week was mainly on Database Systems. Out of all the topics discussed this week I mainly learned that a database is a structured data or information collection procedure which is typically stored digitally in a computing device. A database management system (DBMS) mainly consists of a database and its handling in particular. Through this learning process, I have analysed that DBMS are mainly the link between a user and a database as it allows a user to share and receive data, allows the user to view their data in an integrated manner, and provides efficient management of data. Though DBMS provides us with a lot of enhancements, it brings along elevated costs, complex management, and dependency on the vendors and much more. This week, I have learned that Databases hold a large amount of data for users and allow them to View and manage the data. I used to wonder how the data that we enter or see is being managed or stored. It is clear to me that DBMS manages everything related to data. I still need to learn how the data manipulation (Yunus et al. 2017, pp.192-194.) is being done and stored. In future, this would help me in understanding the basic concept of the data that is being managed by the businesses.

Week 2: Data Models

This week we have mainly targeted the Data models. Data models define how well the schematic representation of a database is portrayed. Models are fundamental components of a DBMS for adding abstractions. These models describe ways data is related to each other and how it is managed and kept within the system. The below analysis can be made that to manage data as a resource, data modelling methods and approaches are used to represent information in a standard, stable, and precinct manner. The data modelling (Liyanage 2017, pp. 432-435) mainly deals with the building blocks like Entity, Relationships and Constraints. I have learnt through data models that data modelling may be done during many sorts of projects and at various stages of a project. Data models are iterative; there is no such thing as a definitive data model for all corporations or applications. It follows no free lunch theorem. The data models should preferably be saved in a repository so that they may be accessed, extended, and updated over time.

Week 3: Relational Database Model

This week’s main focus is on Relational Database. A relational database is an organized collection of data components connected by established connections. These objects are generally stored as a series of tables. In a database, a column maintains the actual value of an attribute and the rows represent a collection of links. A primary key is allocated to each item in a database, and entries from different tables can be connected via foreign keys. According to my analysis, when working with big, complicated datasets, relational databases are ideal. A relational database is a form of database that stores and makes data points that are connected available. Every row in a relational database is a transaction with a unique identifier known as the key. The table's columns include data attributes, and each record typically contains a value for each feature, making it simple to construct links between data points. This has taught me that a relational database (Astrova 2009, pp. 415-424) is a subtype of a database. It employs a framework that enables us to identify and access data in the database. I've also learned about relational databases, and how data in a relational database is structured into tables. Relational databases, as I have understood, eliminate data redundancy. To me, relational databases have been a completely new and interesting concept. I will incorporate it to enhance my work.

Week 4: ER Modelling

This week’s focus is on ER Modelling. Modelling of ER (or Entity-Relationship Modelling) are sometimes referred to as ERDs or ER Models. The ER model is a traditional and completely normalised relational schema that is utilised in many OLTP systems. Typically, these applications do query tiny units of information at a time, such as a customer record, an order record, or a shipping record. I have mainly focused on and found that Entity Relationship Modelling (ER Modelling) (Weilbach et al. 2021, pp. 228-238) is a graphical method which helps in representing real-world objects. It also resolves the difficulties in developing a database is that designers, developers, and end users all have different perspectives on data and its application. A combination of attributes characterizes an entity and values can be assigned to entity properties. During this process, I discovered that one of the pitfalls while developing an efficient database comes from the fact that designers, developers, and end-users all have different visualisations and necessities of data (Schulz et al. 2020, pp.150-169). Developers will find it simple to use, manage, and maintain. I have learned a new way to generalize database entity structure. I will surely incorporate this technique to make thing more professional in future.

Week 5: Advanced Data Modelling

This week we mainly discussed Advanced Data Modelling. It refers to data patterns that enable users to rapidly discover and effectively evaluate complicated cross-enterprise data-focused business rules and validate complex requirements. Techniques for generalisation and abstraction allow for flexible data structures that can adapt to quickly changing business norms and needs. I have analysed and suggested the five data model dimensions as the most important (Liyanage 2017, pp. 432-435).Clarity implies the ability of the Data Model to be comprehended by those who look at it. Flexibility means a model's capacity to adapt without having a significant influence on our code. Performance describes performance advantages solely based on how we represent the data. Productivity implies a model that is simple to work with without wasting a lot of time. Lastly, Traceability means information that is essential to the system as well as data that is valuable to our consumers. As a result, I've learned that the Data Model of each programme is its heart. In essence, it's all about data: data enters via the user's computer or from an external source, data is processed according to certain business rules, and data is eventually displayed to the user (or external apps) in some convenient manner. In future I would need this Data Model knowledge as every component of DBMS relies on data to make sense of the entire system.

Week 6: Normalisation

This week's focus is on Normalization. The act of structuring database tables in such a way that the outcomes of utilising the database are always clear and as intended is known as database Normalisation. It has the potential to duplicate data inside the database and frequently leads in the development of new tables. According to My Analysis, for many years, database normalisation has been an important element of the database developer's arsenal due to its capacity to minimise or decrease replication while increasing data integrity (Sahatqija et. al. 2018, pp. 0216-0221). The relational approach developed during a time when corporate records were primarily kept on paper. Other reasons have also contributed to the challenge to database normalization's supremacy. Hence, I have mainly learned that Normalization helps in reducing Data redundancy and for my future work, Normalization will play a very important role. Normalization is among the most significant features of Database Management Systems that I have studied about. If data is updated, removed, or entered, it does not affect database tables and helps to enhance the integrity and performance of relational tables. Some believe that normalisation will improve performance. It avoids data abnormalities. Hence I have mainly learned that Normalization helps in reducing Data redundancy and for my future work, Normalization will play a very important role.

Week 7: SQL

This week, we will be learning about SQL. For excellent purposes, database systems and SQL are immensely prevalent in the industry. SQL, as a language, enables you to query these databases effectively. SQL is a declaratory language of programming, which implies that when we write SQL code, we understand what it is doing but not how it works. In Analysis, though SQL is often used by software programmers, it is also popular among data analysts for several reasons (Taipalus 2019, pp.160-166). It's simple to grasp and learn from a semantic standpoint, analysts do not have to copy data into other programs since they can access enormous volumes of data immediately where it is kept and when compared to spreadsheet technologies, SQL data analysis is simple to audit and reproduce. I didn't believe SQL would be beneficial for my day-to-day job as a graduate student researching computational cognitive neuroscience when I initially learned about it. I realised that because SQL is so widely used in the industry, I would also have to study it, but I had no intention of using SQL as a student. After thinking about how SQL could be utilized in my profession a little more, I discovered that attempting to create and handle relational databases may be quite valuable in my job.

Week 8: More SQL

SQL is a strong language because it operates entirely behind the scenes, allowing it to query databases with extraordinary performance. Because it is a sequence of instructions, if one is acquainted with imperative programming languages (for example, Python), he/ she will consider it quite straightforward to learn. Again, with SQL, organisations of people committed to understanding how and whento effectively search databases have gone through the process for us and developed our techniques to query databases. With SQL, we simply tell the machine whatever we want to be accomplished (Astrova 2009, pp. 415-424). Analyzing this week’s learnings, SQL mainly focuses on three areas. Data Definition Language(DDL) encompasses starting a Database Model, Creating a Table, Identifying Database Schema and Defining data Types.Data Manipulation Language (DML) involves tasks to insert Rows to a table, Delete Rows From table, Update data in table, Select(View) data from table, Rollback changes on a table, Commit(save) data from a table (Taipalus 2019, pp.160-166).Procedural language extensions to SQL(PL/SQL) create Procedures and batch processes to handle bulk query statements in the form of Code. Hence, this week I learned about SQL and how it is implemented. I have also learned about DDL, DML and PL/SQL and their importance in SQL and DBMS.

Week 9: Database Design

Focus of this week has been Database Design. Database design is vital for developing scalable, elevated applications. Everything else is simply a minor detail. If a database is well-designed, pieces of relevant material are immediately filed and details may be extracted as needed. There is endless diversity under that simple notion. Small actions made early on have a large cumulative influence. After Analysis, there are few factors to consider, and some of the most important (at least in the beginning) for me is to question what is the 'appearance' of the data that can be retained? How is it divided into logical entities, and how many of those entities are expected to exist throughout time? How will the information be used? Is it primarily transaction (OLTP) or primarily used to generate analytical views (OLAP)?Is there any redundancy that may (fairly) be avoided, or anything that should be considered to avoid this in the future? Are there any potentially huge or complicated links (Santosa et al. 2020, p. 012179) that may need particular consideration? I've discovered that the generic aim of database design is to create logical and physical modelling techniques for the suggested database system. To explain, the logical model is largely focused on data needs, and judgments whereas the physical database modelling approach involves a conversion of the database's logical design model by maintaining control over physical media through the use of hardware resources and software systems such as DBMS. I would really be using database design as a key learning for my future.

Week 10: Transaction and Concurrency

This week’s focus is on transactions and concurrency, Database concepts such as transactions and concurrency management techniques are frequently encountered in real-world scenarios (Yu et al. 2018, pp.192-194). Concurrency control is the management of many transactions that are running at the same time. A transaction is a set of activities, generally read and write operations, which is carried out from beginning to end to access and update multiple data elements. In analysis, concurrency control isolates each transaction while it is completed, allowing data to stay consistent long after the transaction has ended, which is very important in multi-user systems. Concurrency control is essential for preventing this. A good transaction has the ACID (Atomicity, Consistency, Isolation, Durability) properties. After being exposed to SQL databases, I got curious about the fundamental ideas that describe how databases work, which prompted me to research transactions and concurrency control. Before starting new transactions on the same object, one transaction should finish its cycle. However, there are drawbacks to this method, such as poor resource use and general inefficiency. This is going to help me while implementing transactions and concurrency in my future work.

Week 11: Database and Web

Today’s topic is on Databases and Web. An intermediate application server or gateway in between Web application and the DBMS is necessary for a Web server to obtain information from a database (DBMS). CGI is the abbreviation for the most widely used Web server interface. A Web server gets a URL, corresponds to a CGI resource, launches a CGI programme, connects to the DBMS, searches the database, and produces the result to the Web server. I have analysed today's learning and found out that web database apps can be free or cost money, generally in the form of monthly subscriptions. Almost any device may access the information. Web database programmes are typically accompanied by their technical support team (Zhao 2022, pp. 1053-1057). They enable users to update information, so all we need to do is design basic web forms. Databases are a commonly utilised technology in the corporate sector, and their importance, attractiveness, and profitability have already expanded. I feel that connecting middleware or the user interface to the application's back-end database still needs a lot of research. There are certain technologies available that connect the user interface to the database at the backend. Some systems consist primarily of a front-end integrated with several levels of middleware and database back-ends.


In Conclusion, I have learned that DBMS mainly manages the Data to/From a user. Data management becomes increasingly complicated as applications become more sophisticated. File-based solutions are inextricably linked to the initial implementation specifications and are exceedingly difficult to redefine and update. It is useful for any coder since practically every application will have to persist its data to a database at some time. With the help of other features like Normalization, Data Modelling ER Modelling etc. we can easily enhance the performance of DBMS by reducing Data redundancy, Scalability and flexibility of the managed data making it more reliable and efficient to use. In this entire learning process, I have learned how data management is done and how we can store and manipulate data without any complexity.


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