Database System Applications

• There are different fields where a database management system is utilized. Following are a few
applications which utilize the information base administration framework –
• Railway Reservation System –
In the rail route reservation framework, the information base is needed to store the record or
information of ticket appointments, status about train’s appearance, and flight. Additionally, if
trains get late, individuals become acquainted with it through the information base update.

• Library Management System –

There are lots of books in the library so; it is difficult to store the record of the relative multitude
of books in a register or duplicate. Along these lines, the data set administration framework
(DBMS) is utilized to keep up all the data identified with the name of the book, issue date,
accessibility of the book, and its writer.

• Banking –
Database the executive’s framework is utilized to store the exchange data of the client in the
information base.
• Education Sector –
Presently, assessments are led online by numerous schools and colleges. They deal with all
assessment information through the data set administration framework (DBMS). In spite of that
understudy’s enlistments subtleties, grades, courses, expense, participation, results, and so forth
all the data is put away in the information base.

• Credit card exchanges –

The database Management framework is utilized for buying on charge cards and age of month to
month proclamations.

• Social Media Sites –

We all utilization of online media sites to associate with companions and to impart our
perspectives to the world. Every day, many people group pursue these online media accounts like
Pinterest, Facebook, Twitter, and Google in addition to. By the utilization of the data set
administration framework, all the data of clients are put away in the information base and, we
become ready to interface with others.

• Broadcast communications –
Without DBMS any media transmission organization can’t think. The Database the executive’s
framework is fundamental for these organizations to store the call subtleties and month to
month postpaid bills in the information base.
The purpose of a database system
• The purpose of a database system is to store and retrieve data in a
convenient and efficient way. Database systems use predefined schemas
and data models to organize data in a structured way. This allows for
efficient storage and retrieval.
• The ultimate purpose of a database management system (DBMS) is to store
and transform data into information to support decision making. DBMSs
have functions such as: Concurrency, Security, Backup and recovery,
Integrity, Data descriptions.
• DBMSs can also help with data manipulation and processing. They have
ACID features, which ensure that data is safe even if the system fails.
•
DBMS in File Process
The following are the main disadvantages of DBMS in File Processing:
• Data redundancy and inconsistency.
• Difficult in accessing data.
• Data isolation.
• Data integrity.
• Concurrency is not possible.
• Security Problems.
 Purpose of Database System
• In DBMS, database systems provide a safe and effective platform to manage vast amounts of data. Their role is to
provide services like data organization, storage, and manipulation, as well as to guarantee data integrity. A database
system’s primary goal is to facilitate data retrieval and provide a dependable storage platform for essential data.

• Efficient storage and retrieval are allowed by structured organization of data through database systems utilizing
predefined schemas and data models.
• DBMS maintains the reliability and accuracy of the information and returns it through enforced constraints and rules
defined in the database schema that eliminates data redundancy and anomalies, respectively.
• Protecting confidential data is crucial and database systems successfully achieve this with their safeguards against
unauthorized access.
• Database systems prioritize the security of sensitive data with their solid mechanisms in place to preserve data
confidentiality.
• The inclusion of strong security measures in database systems ensures the protection of sensitive data and upholds its
confidentiality. Confidentiality and privacy of data are maintained by utilizing resilient security measures within
database systems.
• Collaboration made easy with DBMS. With the provision of a platform to access and manipulate data, multiple users
can now work together and ensure data consistency across various applications. Data sharing and collaboration are
now synonymous with the help of DBMS.
• Data backups and transaction management are mechanisms provided by database systems to ensure data durability.
Safeguarding data against system crashes and failures is their main priority.
 Levels of Data Abstractions in DBMS
In DBMS, there are three levels of data abstraction, which are as follows:
Physical or Internal Level:
• The physical or internal layer is the lowest level of data abstraction in the
database management system. It is the layer that defines how data is actually
stored in the database. It defines methods to access the data in the database.
It defines complex data structures in detail, so it is very complex to understand,
which is why it is kept hidden from the end user.
• Data Administrators (DBA) decide how to arrange data and where to store
data. The Data Administrator (DBA) is the person whose role is to manage the
data in the database at the physical or internal level. There is a data center that
securely stores the raw data in detail on hard drives at this level.
Logical or Conceptual Level:
• The logical or conceptual level is the intermediate or next level of data
abstraction. It explains what data is going to be stored in the database and
what the relationship is between them.
• It describes the structure of the entire data in the form of tables. The logical
level or conceptual level is less complex than the physical level. With the help
of the logical level, Data Administrators (DBA) abstract data from raw data
present at the physical level.
View or External Level:
• View or External Level is the highest level of data abstraction. There are
different views at this level that define the parts of the overall data of the
database. This level is for the end-user interaction; at this level, end users can
access the data based on their queries.
1) Relational Data Model: This type of model designs the data in the form of rows and columns within a table. Thus, a relational model uses tables for
representing data and in-between relationships. Tables are also called relations. This model was initially described by Edgar F. Codd, in 1969. The
relational data model is the widely used model which is primarily used by commercial data processing applications.

2) Entity-Relationship Data Model: An ER model is the logical representation of data as objects and relationships among them. These objects are known
as entities, and relationship is an association among these entities. This model was designed by Peter Chen and published in 1976 papers. It was widely
used in database designing. A set of attributes describe the entities. For example, student_name, student_id describes the 'student' entity. A set of the
same type of entities is known as an 'Entity set', and the set of the same type of relationships is known as 'relationship set'.

3) Object-based Data Model: An extension of the ER model with notions of functions, encapsulation, and object identity, as well. This model supports a
rich type system that includes structured and collection types. Thus, in 1980s, various database systems following the object-oriented approach were
developed. Here, the objects are nothing but the data carrying its properties.

4) Semi structured Data Model: This type of data model is different from the other three data models (explained above). The semistructured data model
allows the data specifications at places where the individual data items of the same type may have different attributes sets. The Extensible Markup
Language, also known as XML, is widely used for representing the semistructured data. Although XML was initially designed for including the markup
information to the text document, it gains importance because of its application in the exchange of data.
 Database Users
• Database users are categorized based up on their interaction with the database. These are seven types of database users in DBMS.
• Database Administrator (DBA) : Database Administrator (DBA) is a person/team who defines the schema and also controls the 3 levels of database. The DBA will then create a new
account id and password for the user if he/she need to access the database. DBA is also responsible for providing security to the database and he allows only the authorized users
to access/modify the data base. DBA is responsible for the problems such as security breaches and poor system response time.
• DBA also monitors the recovery and backup and provide technical support.
• The DBA has a DBA account in the DBMS which called a system or superuser account.
• DBA repairs damage caused due to hardware and/or software failures.
• DBA is the one having privileges to perform DCL (Data Control Language) operations such as GRANT and REVOKE, to allow/restrict a particular user from accessing the database.

• Naive / Parametric End Users : Parametric End Users are the unsophisticated who don’t have any DBMS knowledge but they frequently use the database applications in their daily
life to get the desired results. For examples, Railway’s ticket booking users are naive users. Clerks in any bank is a naive user because they don’t have any DBMS knowledge but they
still use the database and perform their given task.
• System Analyst :
System Analyst is a user who analyzes the requirements of parametric end users. They check whether all the requirements of end users are satisfied.
• Sophisticated Users : Sophisticated users can be engineers, scientists, business analyst, who are familiar with the database. They can develop their own database applications
according to their requirement. They don’t write the program code but they interact the database by writing SQL queries directly through the query processor.
• Database Designers : Data Base Designers are the users who design the structure of database which includes tables, indexes, views, triggers, stored procedures and constraints
which are usually enforced before the database is created or populated with data. He/she controls what data must be stored and how the data items to be related. It is
responsibility of Database Designers to understand the requirements of different user groups and then create a design which satisfies the need of all the user groups.
• Application Programmers : Application Programmers also referred as System Analysts or simply Software Engineers, are the back-end programmers who writes the code for the
application programs. They are the computer professionals. These programs could be written in Programming languages such as Visual Basic, Developer, C, FORTRAN, COBOL etc.
Application programmers design, debug, test, and maintain set of programs called “canned transactions” for the Naive (parametric) users in order to interact with database.
• Casual Users / Temporary Users : Casual Users are the users who occasionally use/access the database but each time when they access the database they require the new
information, for example, Middle or higher level manager.
• Specialized users : Specialized users are sophisticated users who write specialized database application that does not fit into the traditional data-
processing framework. Among these applications are computer aided-design systems, knowledge-base and expert systems etc.
JSON (JavaScript Object Notation)
Data mining
• Data mining is the process of analyzing large sets of data to identify
patterns and relationships. It uses statistical and computational
techniques to extract useful information from the data. Data mining
can help organizations solve problems, predict trends, and make
more informed business decisions.
• Data mining can be used with structured, semi-structured, or
unstructured data. The data can be stored in databases, data
warehouses, or data lakes
Advantages of Data Mining

• The Data Mining technique enables organizations to obtain knowledge-

based data.
• Data mining enables organizations to make lucrative modifications in
operation and production.
• Compared with other statistical data applications, data mining is a cost-
efficient.
• Data Mining helps the decision-making process of an organization.
• It Facilitates the automated discovery of hidden patterns as well as the
prediction of trends and behaviors.
• It can be induced in the new system as well as the existing platforms.
• It is a quick process that makes it easy for new users to analyze enormous
amounts of data in a short time.
 A data warehouse
• A data warehouse is a centralized repository for storing and managing large
amounts of data from various sources. Data warehouses are used for business
intelligence (BI), reporting, and data analysis. They are designed for data
analytics, which involves reading large amounts of data to understand
relationships and trends across the data.
• Data warehouses are optimized for fast querying and analysis. They provide a
single source of truth for data, enabling organizations to make informed
decisions.
• Data warehouses are suited for ad hoc analysis as well custom reporting. They
can integrate data from different sources into a single, unified view. This can help
in eliminating data silos and reducing data inconsistencies.
The two main approaches used to build a data warehouse system are:
• Extract, transform, load (ETL)
• Extract, load, transform (ELT)
A data lake
• A data lake stores raw, unstructured data in a repository. A data
warehouse stores structured, processed data that has been cleaned
and transformed for a specific purpose.
• Data lakes can store large amounts of data indefinitely, including
multimedia files, log files, and other large files. Data warehouses
store data from multiple sources, such as relational databases. Data
warehouses use online analytical processing (OLAP) to analyze data.
• Data lakes and data warehouses are becoming the preferred solution
for businesses as they grapple with growing data volumes. Cloud
environments offer the security, reliability, and low maintenance
needed to handle this data explosion.