DataBase Management System Relationship Posted on January 4, 2024 Introduction The PAW Fondation links animal right and well-being campagnes worldwide. Many Works for its abord branches. PAW pourchasse a massive data base to simplifie Operations. The Project data analyst wants to build and Install a SQL data base for the organisation. Branch, worker, member, suscription, payement, contribution, and other donation data Will Be retained. PAW’s unusual organisationnel structure—one Branch per zip code—uses a well-built data base to engage with other animal protection NGOs. The system Will process monetary donations, other presents, and complex Relationship between Works, managers, subscribers, and other contributors. The data base now includes “Volontiers” and “Events,” Along with the Project scenarios main components, to improve It. Without compassionate chapter volontiers, PAW would Fail. Event inclusion promotes community-building via Schedule events. This Project Will meet PAW’s data management needs and prepare for analytics. Following sections explain the logical data model, Entity-Relationship Diagram (ERD), and SQL data base design. Each data base design process evaluated accuracy, efficience, and Protect Animal Welfare’s worldwide animal welfare activism. Part A: As I Am now working on a series of practice problems for ERD, I was wondering what the best strategy is for modelling Ethier or Relationship. Could You perhaps provider me with some information? At This very moment, I Am working on the collection of questions. At the moment, I Am working on tasks That are considered to Be practice problems. For exemple, You Will Be responsable for maintaining Customer accounts at a Taekwondo school. These accounts Will Be in charge of representing and paying for one or more pupils. Using these accounts to make payements is Something That is going to Be done in the future. The accounts in question are ones That the organisation has the potentiel to acquire in the future. This issue Will Be decided by the conditions That are now in existence; nonetheless, There is a chance That the account is owned by Ethier the student or a parent. Nevertheless, This matter Will Be resolved. Depending on the circumstances, the student of the parent is the one whois is the owner of the account. This is because the student is the owner of the account, which is the reason for this difference. Relationship: Sure, let’s identify the types of Relationship for each pair of tables: 1. One-to-One Relationship: – Suscriptions and Payements: Each suscription has one correspondant payement, and Eich payement is relate to one suscription. 2. One-to-Many Relationship: – Branches to Employees: One Branch Can have man employées, but an employée bélongs to onlay one Branch. – Branches to Volontiers: One Branch Can have man volontiers, but a volontiers bélongs to onlay one Branch. – Branches to Events: One Branch Can organise man events, but an évent is Associate

1. DataBase Management System Relationship
Posted on January 4, 2024
Introduction
The PAW Fondation links animal right and well-being campagnes worldwide. Many
Works for its abord branches. PAW pourchasse a massive data base to simplifie
Operations. The Project data analyst wants to build and Install a SQL data base for
the organisation. Branch, worker, member, suscription, payement, contribution, and
other donation data Will Be retained. PAW’s unusual organisationnel structure—one
Branch per zip code—uses a well-built data base to engage with other animal
protection NGOs. The system Will process monetary donations, other presents, and
complex Relationship between Works, managers, subscribers, and other
contributors.
The data base now includes “Volontiers” and “Events,” Along with the Project
scenarios main components, to improve It. Without compassionate chapter
volontiers, PAW would Fail. Event inclusion promotes community-building via
Schedule events. This Project Will meet PAW’s data management needs and
prepare for analytics.
Following sections explain the logical data model, Entity-Relationship Diagram
(ERD), and SQL data base design. Each data base design process evaluated
accuracy, efficience, and Protect Animal Welfare’s worldwide animal welfare
activism.
Part A:

2. As I Am now working on a series of practice problems for ERD, I was wondering
what the best strategy is for modelling Ethier or Relationship. Could You perhaps
provider me with some information? At This very moment, I Am working on the
collection of questions. At the moment, I Am working on tasks That are considered to
Be practice problems.
For exemple, You Will Be responsable for maintaining Customer accounts at a
Taekwondo school. These accounts Will Be in charge of representing and paying for
one or more pupils. Using these accounts to make payements is Something That is
going to Be done in the future. The accounts in question are ones That the
organisation has the potentiel to acquire in the future. This issue Will Be decided by
the conditions That are now in existence; nonetheless, There is a chance That the
account is owned by Ethier the student or a parent. Nevertheless, This matter Will
Be resolved. Depending on the circumstances, the student of the parent is the one
whois is the owner of the account. This is because the student is the owner of the
account, which is the reason for this difference.
Relationship:

3. Sure, let’s identify the types of Relationship for each pair of tables:
1. One-to-One Relationship:
– Suscriptions and Payements: Each suscription has one correspondant payement,
and Eich payement is relate to one suscription.
2. One-to-Many Relationship:
– Branches to Employees: One Branch Can have man employées, but an employée
bélongs to onlay one Branch.
– Branches to Volontiers: One Branch Can have man volontiers, but a volontiers
bélongs to onlay one Branch.
– Branches to Events: One Branch Can organise man events, but an évent is
Associates with onlay one Branch.
– Employees to Membres: An employée Can Be Associates with man membres, but
Eich member is Associates with onlay one employée (assument an employée Can
intro duce or Be Associates with multiple membres).
– Membres to Suscriptions: A member Can have multiple suscriptions, but Eich
suscription is Associates with onlay one member.
– Donations to Donation Catégories: A donation Can bélong to multiple catégories,
but Eich catégorie is Associates with multiple donations.
3. Many-to-Many Relationship:

4. – Employees to Membres: An employée Can Be Associates with man membres, and
a member Can Be Associates with man employées. This is resolved usine the
Junction table `Employee Membres` (representing the man-to-man Relationship).
– A present might fall into a number of different categories. The person-to-person
relationship is represented by the Junction table titled “Donation Category Relation,”
which provides a solution to this issue.
In su mary:
– One-to-One: Suscriptions to Payements.
– One-to-Many: Branches to Employees, Branches to Volontiers, Branches to
Events, Employees to Membres, Membres to Suscriptions, Donations to Donation
Catégories.
– Many-to-Many: Employees to Membres (resolved by `EmployeeMembers`),
Donations to Donation Catégories (resolved by `Donation Category Relation`).
2- Database Implémentations and
Scripting:
In the process of building the data base for the Protect Animal Welfare (PAW)
Fondation, we used MySQL/Maria DB as the relationnel data base management
system. This was done in ordre to Stream line the process. This action was takin
with the intention of shooting out the process and main ith more efficient. This phase
was carrier out with the intention of boostant the effective Ness of the trématent and
main ith more plaisant for the individuels whois ère takin part in suc procédures.

5. Additionnelle, the exécution of this was carrier out in à wax That was in compliance
with the Entity-Relationship Diagram (ERD) That was suggestif throughout the
process. This was done in ordre to ensure maximum efficiency.
Database Création and Table
Définitions:
Table Branches:
Table Employees:
The primary key of the table is the integer column BranchID.
You have the option to preserve the branch name in the BranchName string column.
Because it can’t be NULL, this field must have some data.
An additional string column might be used to store the branch location.
The branch manager’s name should be included in this area.
You may modify the column limits and data types to fit your needs. The optimal
column organisation for the “Branches” table is dependent on the data you want to
store there.
Once created, a table may be filled with data using the INSERT INTO command, and
its contents can be queried using the SELECT statement.

6. Table Membres:
We utilise the number “member_id” to uniquely identify each and every one of our
affiliates.
For the purpose of storing the members’ first and last names as string data, two
variables are utilised: first_name and last_name.
Every client is provided with a minimum of one email address.
Here we keep track of the precise birthdates of every member.
Named “date_of_registration” for obvious reasons, it is the first registration date.
present at the moment: This column displays the person’s engagement status as of
the present moment.
How you may change the data types and limits is dependent on the database’s
capabilities and your requirements. You may have to add additional columns or
establish restrictions depending on the information you wish to keep about your
system members.
Table Suscriptions:

7. The subscription_id is a unique identifier for each and every subscription.
There is a record of every name in the “member_name” column.
The value of a membership tier may alter on a monthly, yearly, or even more
frequent basis.
Commencement date marks the beginning of the subscription period.
If you would want your subscription to continue after the current term finishes, just
leave it blank.
membership dues are the necessary cost to join.
Platforms for managing databases such as PostgreSQL, SQL Server, and MySQL
allow you to modify data types and restrictions to suit your specific needs. If you
have any questions or concerns, please don’t hesitate to contact us at your
convenience.
Table Payements:
One common usage for PaymentID is as a primary key, as each payment is unique.

8. client IDs are associated with certain transactions and serve as a unique identifier for
each client. To be sure, the “CustomerID” column is present in a “Customers”
database.
The total is the amount, and it is expressed as a two-digit decimal integer.
One way to keep track of when payments were made is using the PaymentDate date
type.
One key represents cash, one key represents credit card, etc.
The TransactionID is a one-of-a-kind identifier that tracks the progress of a
transaction.
This is just an example; the actual way you should alter the table description is
dependent upon your needs and the features offered by your database management
system. Additional restrictions, such not NULL and unique, might be useful, all
dependent on your requirements.

9. Table Donations:
All contributions are primarily identified and stored by donation IDs.
It is standard practice to use the “donor_id” column as a foreign key when dealing
with the “Donors” database. This allows you to connect certain gifts to specific
donors.
The DECIMAL data type is used in this column to record the gift value to the nearest
tenth and second decimal place.
The contribution date, a DATE data type, records the date of the gift.
The method of contribution could be detailed in this part. You have the option to set
the parameter to “Credit Card,” “Cash,” or “Check.” It is entirely up to you to decide
how long the VARCHAR should be.
The notes section allows you to provide further feedback about the present in the
form of a free-form text.
Table Donation Catégories:
Every kind of gift has its own special number, or CategoryID.
A non-null string describing the kind of gift is the category name.
A more in-depth critical review or critique of the work.

10. The CreatedAt timestamp indicates the initial creation of the category, however the
current date is far more often used.
You can see the last modification time for this category in the timestamp UpdatedAt.
This timestamp will always use the current date and time.
The configuration of the tables you build is dictated by your requirements and the
DBMS you’re using. The three most widely used DBMSs are PostgreSQL, SQLite,
and MySQL. You are free to modify the data types and limitations to meet your
needs.
Table Volontiers:
Every kind of gift has its own special number, or CategoryID.
A non-null string describing the kind of gift is the category name.
Carefully and critically assess the assignment.
The CreatedAt timestamp really gives the initial date of the category’s establishment,
even if the current date is typically utilised.
Datestamp UpdatedAt will show you when this category was last updated. This
timestamp will always use the current date and time.

11. Together with your management system, you construct your database by deciding on
the table configuration. After MySQL and PostgreSQL, SQLite is the most popular
database management system. Data types and limits may be adjusted to meet your
needs.
Table Events:
It is standard practice to provide each event a unique identifier, or “EventID.”
To keep track of all the event names, a string variable called “EventName” is utilised.
This is where you may choose to attend a concert or a seminar.
Could you please let me know when the event is scheduled to take place? Applying
the “EventDate” feature does this.
This is where it’s at.
A conference’s “organiser” is the go-getter who ensures that the event runs
smoothly.

12. It is possible to see the precise decimal value of the event ticket price here. To
satisfy the CHECK restriction, the ticket price can’t be negative.
This framework may be adjusted to meet the requirements of your application.
3. Discussion of Decisions:
3.1 Data Types and Contraints:
Based on the nature of the data, select situable data types (e.g., INT, VARCHAR,
DATE) for each property.
UNIQUE contraints for Post code ère added to the Branches data base to ensure
post code unique Ness.
Forgien key restrictions ère used to construct Relationship between tables and
ensure referential integrity.
3.2 Population Tables:

13. Provider seul exemple data to démonstrateur the database’s capabilités.
Dring data insertion, I made certain That primer and forgien key associations were
préserve.
3.3 Junction Table for Many-to-Many Relationship:
To manage the man-to-man Link between Donations and Donation Catégories, à
Junction table (Donation_Categories_Junction) was introduced.
The data base design is résilient as a résulté of these décisions, and the populace
data offres a solide basis for setting and analyses lithiné the Protect Animal Welfare
basis data base.
Three DML scripts
Scenario 1: Retrieve the total number of donations made by each member.
Decision and Rational:
INNER JOIN was used since the scenario expressly requests members who have
made donations. This guarantees that only members who have made matching
donations are listed.

14. GROUP BY: To retrieve the number of donations for each member, I grouped the
results by Member_ID and Name.
COUNT: The COUNT function was used to get the total number of donations made
by each member.
Scenario 2: Retrieve the names of
employees who have other employees
reporting to them.
Decision and Rational:
Self-Join: Using the Supervisor_ID, I performed a self-join on the Employees table,
connecting E1 as the supervisor and E2 as the subordinate.
DISTINCT: DISTINCT was used to avoid repeating pairings of supervisors and
subordinates.
WHERE Clause: A WHERE clause was used to eliminate circumstances where a
supervisor has no subordinates.
Scenario 3: Retriever the members who
have not made any donations.
Decision and Rational:

15. LEFT JOIN: A LEFT JOIN was used to inclue all membres frome the Membres table,
regardes of Werther the hadj équivalent data in the Donations table.
Clause WHERE: Membres with no machin donations (Donation ID IS NULL) have
been filtre out, indication membres whois have not made an donations.
General Coding Considerations:
Colum Alaises: Provider clean and meaningful alaises for colons, improuvions output
readability.
Joins: Based on the individuel rééquipements of each case, select the appropriâtes
joint type (INNER JOIN, LEFT JOIN).
Distinctes: DISTINCT soul Be used sparingly to achieve accurate and non-repetitive
outcomes in each case.
Null Handling: Effectively handled NULL values in the WHERE clause to
accommodate the scenarios’ particular constraints.
Part B: Data Warehouse Design

16. The Protect Animal Welfare (PAW) Fondations Data Waterhouse design entais using
Kimball four-sep dimensionnel design méthode to produc a schéma That allons for
quick qu’Erin and analyses. In this scenario, we Will show how to croate a star
schéma using the suggestif data base frome Part A.
1. Kimball Four-Step Dimensional Design Process:
1.1 Identify the Business Process:
– PAW Fondations business Operations of interest include analyzing global
membership, monitoring money collecte through contributions and suscriptions, and
maintaining inventory levels for varions donation item categories.
1.2 Choose the Grain:
– The amount of detail required for analysis determines the grain. The grain differs in
this case: – For membership insights, the grain may be at the individual member
level.
– It might be at the transaction level for money collected, documenting each
contribution and subscription payment.
– It may be at the level of individual donated items for inventory amounts.
1.3 Choose the Dimensions:

17. – Dimensions are the business categories used to examine data. Dimensions for the
PAW Fondation might include Time (contribution and suscription dates), Geography
(branch locations), Members, Donors, and Items (for inventory).
1.4 Identify the Facts:
– Facts are quantifiable quantities for analysis. Facts for PAW might include the
number of members, the amount of money raised, and the quantity of donated
things.
2. Star Schema Design:
2.1 Central Fact Table:
– The core fact table might be called “Foundation_Facts” and contain primary keys
from multiple dimension tables, as well as the corresponding measurements.
Dimension Tables:
Sample Data Cube Showing Hierarchies
2.3 Fact Table:
Analyse products, locations, and times with the help of the data cube.
Every square in the cube represents one measure, like sales.

18. Using hierarchies within dimensions, we may potentially achieve various depth
levels. Sales may be considered on an annual, monthly, or even daily basis with the
help of the Time dimension.
3. Data Cube for Membership Insights:
A Data Cube may be created to convey information about membership. This cube’s
dimensions might contain Time (Year, Month), Geography (Branch Location), and
Members. The count of members might be one of the metrics.
Discussion:
Grain: The star schema’s granularity enables for investigation at various degrees of
detail, allowing a wide range of queries.

19. The star structure improves query
performance by reducing the number of
joins necessary.
The schema is very adaptable as it can be
easily modified to meet the needs of
businesses by adding or removing
dimensions and metrics.
The Membership Data Cube provides the
PAW Foundation with a wealth of
information on membership trends
throughout different time periods and
branches. Locate patterns of contact,
determine the peak membership time, and
determine whether there are geographical
differences in participation.

20. Integrating data at many levels allows for
thorough reporting and analysis, including
year, month, and area.
The star schema design, which has its
origins in Kimball’s dimensional design
process, is something to consider while
constructing the PAW data warehouse.
Thanks to this setup, global membership,
contribution, and inventory counts may be
reported and analyzed quickly. The
Membership Data Cube offers a
multi-dimensional view of
membership-related data, which
substantially expands analytical
possibilities.
Part C: Business Intelligence Analysis
for Leading Supermarket

21. With the help of Tableau, I was able to do an analysis on a sales dataset when I was
working as a data analyst for a well-known grocery chain in the United States. Within
the scope of this article, the two most important topics that are covered are the
construction of the necessary calculated fields and the processing of the data.
Additionally, I will give sales data visualizations that are helpful by responding to five
particular requests that have been made. These requests have been made.
Data Preparation Steps:
1. Data Under standing:
– It is critical to begin the analysis by properly grasping the dataset. This entails a
thorough assessment of its structure, factors, and potential difficulties.
Understanding columns, data types, and recognizing missing or inconsistent values
is critical for future analysis.
2. Handling Missing Data:
– Addressing any missing values in the dataset is an essential step. Data is imputed
or eliminated to provide a full and correct dataset for analysis.
3. Ensuring Data Quality:
– The data’s correctness and consistency are critical. This stage entails verifying the
dataset in order to discover and correct any outliers, duplication, or abnormalities
that may jeopardize the integrity of the ensuing study.
4. Addressing Data Types:
– It is critical to ensure that each variable is allocated the right data type. For
example, ensuring that dates in Tableau are recognized as date types is critical for
proper time-based analysis.
5. Creating Data Hierarchies:

22. This stage improves the capacity to dig down into daily, monthly, or annual trends,
allowing for a more detailed understanding of data temporal patterns.
6. Exploring Seasonal Trends:
– A vital component of preparation is delving into the statistics to find and analyze
any seasonal patterns or trends within the sales data. Visualizations may be created
to depict seasonal fluctuations in sales, influencing inventory and marketing strategy.
Calculated Fields:
Extracting City and Post code:
– I created a calculated Field for City using the following formula:
TRIM(SPLIT([Address], ‘,’,2))
– Another calculated Field was created for Post code:
RIGHT([Address], 5)
– These fields enable bretter geographical analysis.
Manufacturer Warranty Field:
– A Manufacturer Warranty Field was created using the formula:
DATEADD(‘month’, 6, [Order Date])
– This represents the date six months after the Order Date.
Order Total and Order Profit Fields:

23. – Order Total, calculated as Quantity multiplie by Price:
[Quantity] [Price]
– Order Profit, calculated as 25% of Order Total and ronde to 2 decimal places:
ROUND([Order Total] 0.25, 2)
Answering Specific Questions:
1. Total Sales and Total Profit for Each Month of
2019:
In order to facilitate the proper administration of performance metrics for 2019, a
thorough dual-axis chart was meticulously constructed. By combining Total
Customers and Total Profit, this comprehensive image aims to provide a complete
view of the economy at now. For a painless monthly data aggregate, we utilized
Tableau’s in-built features to build a detailed date hierarchy. Simplifying the
procedure was the main objective. This data visualization is very helpful because it
shows how sales and profits have changed month-to-month. It also makes it easier
to understand the financial operations that are taking place in 2019. With this visual
depiction, they have a potent tool at their disposal that allows them to see trends,
pinpoint periods of maximum performance, and, ultimately, make informed decisions
to maximize the efficacy of future plans. These stakeholders have access to this tool.

24. 2. Top 5 Cities by Quantity Ordered:
The y-axis of the graph displays both the order numbers and the cities involved in
the transaction. The cities are shown along the x-axis. The development of a
complete bar chart was accomplished via the use of visual analytics. It is possible
that we will be able to see the patterns of ordering in a number of cities by using this
basic strategy.
In order to stress the significance of the results, we began by arranging them in
descending order of importance, from the most significant to the least important.
Quantity has arranged the five cities in a way that is both smart and clever. The
purpose of its development was to make research easier and to enable rapid access
to information that is vital. There is a probability that those who are interested in
marketing, distribution, and inventory management would find this presentation to be
visually appealing and useful in gaining a knowledge of how different factors impact
the overall order volume.

25. 3. Bottom 5 Cities by Number of Orders:
A bar chart was produced after much data analysis using a method similar to the
inquiry described earlier in this paragraph. This well-structured visual representation
provides an in-depth look at each city, drawing attention to the ones with the fewest
orders. Here is the paper for your reference. Pay special attention to the cities
ranked lowest in terms of order volume—the graphic displays this information in
descending order. The goal here is to make sure the chart is easy to understand and
looks good. Doing so highlights the need of being precise. Not only does this
captivating image summarize the numerical component, but it also serves as a
jumping off point for strategic concepts. It highlights potential areas where restoring
order might have a positive effect and provides recommendations for improving
those areas.

26. 4. 2019 Municipal Sales Amounts Exceeding $2,500,000:
– It was decided that a detailed bar chart would be the best instrument to use for an
in-depth analysis of the economic performance of several different areas. The overall
sales figures and the municipalities that were considered show a significant
discrepancy, which has to be considered.
I got an excellent grade. Specifically, cities with sales of more Than $2,500,000 were
chosen after a thorough assessment of the data. The goal in doing this was to
streaming the selection process. The filter was painstakingly created utilizing the
filter, and it allowed us to do a focused inquiry into cities that are of Great economic
significance. And thus it came to pass that we could illuminate the primary means by
which the grocery chain generates revenue. I got an excellent grade. Moreover, the
created graphic made it simple to contrast and compare the sales landscape
according to the city. There was a major bene fit to this. Moreover, with this Remak,
you will get a breakdown of the top-performing websites. Using this analytical
visualization has made it much easier to make strategic decisions about the
allocation of resources and the targeting of marketing activistes. In this case, the
stakeholders were able to identify and choose locations with very high economic
impact potential. This allowed them to identify and focus their inquiry on areas with
the potential to impact the economy.

27. 5. Average Price of Product with Price Each above
$200:
An outstanding bar chart is front and center when examining the amount of change
in the price of luxury items. The x-axis displays all the commodities, while the y-axis
shows the average price. To help you understand the magnitude of the pricing issue,
this graphic displays all items with price tags over $200.
A well-organized chart is then led in diminishing order to interested the audience. We
have sorted the product in the store according to their average price so that buyers
can quickly Locate the greatest bargains. This more precise depiction bolsters
pricing and marketing strategies by assisting decision-makers in selecting
high-performing commodities in the higher price range.
Dashboard:

28. Conclusion:
Finally, the data preparation methods and computed fields were critical in gaining
useful insights from the supermarket’s sales statistics. I solved particular business
challenges with Tableau visualizations, giving decision-makers with actionable
insight. This BI study supports the understanding of sales trends, the identification of
top-performing cities, and the optimization of product pricing strategies. These
insights may be used by the store to improve operational efficiency and optimize
profitability.
Part D: Critical Reflection on Database Selection for PAW Foundation’s Social
Network Platform
Introduction:
The choice of an appropriate database is an important decision that has the ability to
signifiant affect Social Performance Network Platform that is provided by the PAW
Foundation. This option is very important since it has the capacity to have a
significant effect. It is also possible that this choice will have an effect on the
performance of the platform as well as its capacity for expansion.
1. Platform Requirements:
1.1 Data Structure:
Since of this, and since the social media platform is
responsible for the storage of a variety of data types, it is

29. very necessary for us to make certain that each and
every piece of data is taken into consideration. This
collection contains all of the user profiles, posts,
comments, and media assets that have been produced
by the users. Ensure that the database you have selecte
is able to store and retrieve all of the different types of
data that you will need before you commit to using it.
1.2 Complex Queries:
Due to the complexity of the queries needed for features
like tailored content suggestions, friend
recommendations, and search operations, some have
argued that more research is necessary. We need to
finish this job right now. A perfect research approach
would be for everyone to work collaboratively on this
issue. In order to gauge the complexity of the questions,
it is essential to think about these features. A number of
features need intricate database queries, which is
causing the problem. You may be certain that these
qualities will benefit you and help you achieve your
goals. This demands its completion without delay. When
you see it as a responsibility, it requires your whole
focus. But if it’s really required, this may still happen. No
sane individual could have seen through this promise

30. and not followed it. This cannot be debated or
contested. Our capacity to attain our goals will be tested
by how well we can follow through on each stage.
Performance:
Business methods Assess platform reads and writes. Certain databases thrive at
reading, others at writing. The platform’s target users’ behaviour should dictate
database selection. Consider operation response times while computing latency.
Many social networks need low-latency interactions. Check that the database can
handle real-time changes and notifications.
3.1 Data Structure:
An RDBMS may be more suited if the data for the social network is primarily
structured and relational (user profiles, postings, comments).
– If the data is semi-structured or frequently changing, a NoSQL database,
particularly one that is document-oriented, offers greater flexibility.
3.2 Security and Compliance:
An important part of the authorization and authentication process is making sure the
database can manage user permissions and authentication. Implementing rigorous
security measures is essential for protecting sensitive user data. The rules governing
data storage and privacy are complex, so you have to study them well before taking
any action..
3.3 Cost Considerations:
In order to determine the total cost of ownership, it is necessary to take into
consideration all of the relevant factors, including the fees for licencing and
maintenance, as well as the potential expenditures that are related with the

31. extension of the database structure. Informing one self on the open-source
alternatives that are now accessible is one method that may be used to cut down on
the expenses associated with licensing.
When it comes to real estate, there are also taxes and fees to consider. It is essential to do a
thorough examination of the databases specifications in order to ascertain the necessary
hardware. Among these requirements, consideration must be given to the availability of
storage space, networking capabilities, and hardware resources. It is necessary to have a
strategy that has been well développes in order to make the most efficient use of the
resources that are available while minimize the costs that are incurred.
4. Recommandation:
Due to its very dynamic nature, the PAW Foundation’s social networking platform is
strongly recommended to have a NoSQL database built. This is due to the fact that
the PAW Foundation is constantly developing and expanding. The platform that is
being evaluated is likely to go thorough some changes. This is due to the fact that
development and maintenance are always adding new features to the platform. The
document-oriented NoSQL database MongoDB is a great choice to consider. A
NoSQL database is best shown by this. Everyone involved must pay close attention
to this decision. As an added convenience, here are just a few of the many other
considerations that went into drafting this statement:
Flexibility: The flexible schema of MongoDB enables for quick adaption to new
requirements without the need for expensive schema migrations.
Scalability: MongoDB’s horizontal scalability features enable it to handle a rising
user base and a high volume of user-generated material.

32. Development Speed: By removing the need for formal schema definitions, NoSQL
databases, particularly document stores, allow for a shorter development cycle.
Due to the fact that it is able to manage unstructured data and functions with a high
degree of read-intensiveness, MongoDB is an excellent choice for a database that
should be used by a social networking platform that is designed to facilitate efficient
communication.
NoSQL Database:
Advantage :
Cassandra and MongoDB are two examples of NoSQL databases that are ideal for
handling massive volumes of unstructured and disorganized data. These databases
are among the most popular alternatives. This is far more true given that NoSQL
databases are capable of handling both forms of data. The great data storage and
retrieval capabilities of current databases are the main reason behind this. The
improved horizontal scalability provided by these databases was a contributing factor
in achieving these results. This is the main reason behind everything. These
databases are essential for many reasons, but one of the most significant is that they
can expand along with your company. This only serves as an illustration of their
paramount importance. The data stored in these databases is vital for several
reasons, not the least of which is the abundance of benefits already mentioned.
The schema flexibility of NoSQL databases makes them highly suitable for a wide
range of applications. You can’t access these databases without this key. This is the
main reason Bhind everything.
Because of this, it is possible for the databases to accept data models that are
constantly evolving. As a result of this flexibility, it is able to respond more quickly to
shifting data models, which removes the need for extensive schema modifications.

33. The event was a performance. There are specific use situations in which NoSQL
databases have the potential to deliver improved performance in comparison to
standard relational databases. One example of this is operations that need a
significant amount of space for reading and writing data.
Disadvantages:
Either a pattern of behaviour that is constant throughout time or one that is
undermined by the following categories: When it comes to accomplishing the desired
outcome, it is possible for NoSQL databases to make updates to their rigorous
consistency on a regular basis. This is something that can be done. Taking this step
is done with the intention of improving the overall performance of the database. In
spite of the fact that this is not capable of being used for any other reasons, there is
a possibility that it may be advantageous for some applications.
There is a possibility that the process of deploying a NoSQL solution would include a
learning curve. This is something that should be anticipated. Regarding this
particular matter, it is important to take it into mind. To be more specific, this is the
situation for development teams who are used to dealing with conventional relational
databases.
5. Conclusion:
Finally, but certainly not least, the decision between a Relational Database and a
NoSQL Database for the social network platform that the PAW Foundation is in the
process of constructing is influenced by a variety of different factors. These factors
include the data format, scalability, performance, and development speed. Because
of the dynamic nature of a social network and the need for flexibility and scalability, it
is recommended that a NoSQL database, such as MongoDB, be used. Both of these
characteristics are essential. In spite of this, it is of the utmost importance to conduct
a thorough investigation into the particular requirements of the project and to

34. collaborate with the development team in order to guarantee that the database that
is chosen is compatible with the goals and limitations of the social network platform
that is utilised by the PAW Foundation.
Références:
1. Connolly, T. M., & Begg, C. E. (2014). Database Systems: A Practical
Approach to Design, Implementation, and Management (6th ed.).
Pearson.
2. Date, C. J. (2004). An Introduction to Database Systems (8th ed.).
Addison-Wesley.
3. Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The
Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.
4. MongoDB Documentation. (n.d.). Retrieved from
https://docs.mongodb.com/
5. Oracle MySQL Documentation. (n.d.). Retrieved from
https://dev.mysql.com/doc/
6. Tableau Documentation. (n.d.). Retrieved from https://help.tableau.com/
7. Image source: Shutterstock (for graphics used in Tableau visualizations).
8. Michael Aram and Gustav Neumann (July 1, 2015). “Multilayered analysis
of co-development of business information systems” .
9. Cook, James M.; McPherson, Miller; Smith-Lovin, Lynn (2001). 27 (1):
415–444. S2CID 2341021; ISSN 0360-0572; “
10.Brett Laursen and René Veenstra (2021). “Towards understanding the
functions of peer influence:
11. Steglich, Christian E. G.; Snijders, Tom A. B.; Van de Bunt, Gerhard G.
(2010)..
12.René Veenstra and Lydia Laninga-Wijnen (2023). osf.io. “The Prominence
of Peer Interactions, Relationships, and Networks in Adolescence and
Early Adulthood
13.Spencer Ackerman (17 July 2013). “NSA warned to rein in surveillance as
agency reveals even greater scope” . The Guardian. The date of recovery
was July 19, 2013.
14.“How The NSA Uses Social Network Analysis To Map Terrorist Networks” .
June 12, 2013. The date of recovery was July 19, 2013.
15.“NSA Using Social Network Analysis” . Wired, May 12, 2006. The date of
recovery was July 19, 2013.