DATABASE MANAGEMENT SYSTEM

1. LESSON 1: Database
Management Systems

2. Database Systems

3. In today’s fast-paced world, data is generated from every digital computing
device, handheld phone, workstation, server, and so on. This valuable
information generated by millions of computing devices, helps private, public,
and professional enterprises store, monitor, and analyze data for different
purposes. What is Database Systems? These are systems that cater to
such data services.
The majority of market-leading companies are deploying Big Data
technologies with IoT infrastructure to experience Real-Time data
processing and Analytics. Real-Time data processing enables enterprises
to provide valuable and useful customer experience or satisfaction. On the
other side, companies who invest in analytics gain a competitive advantage
in the business marketplace, as predicted by Gartner.
When data is stored in Database Systems, it can be stored in any format.
Data can be presented in either a structured or unstructured format. The
complex combination of structured and unstructured data sets is known
as Big Data. Due to the 3V’s (Volume, Velocity, Variety) of Big Data,
traditional technologies and methods can’t be used to analyze them.
Database Systems have been developed to address the issues of Big Data
also.

4. What is Database Systems or DBMS?
Database Systems or DBMS is software that caters to the collection of electronic and digital
records to extract useful information and store that information is known as Database Systems/
Database Management Systems or DBMS. The purpose of a standard database is to store and
retrieve data. Databases, such as Standard Relational Databases, are specifically designed to
store and process structured data.
Generally, Databases have a table to store data, they use Structured Query Language (SQL) to
access the data from these tables. Databases and Database Systems play a vital role in
processing hard, fast and diverse datasets. Without a Database Management System,
businesses won’t receive valuable insights and deep analytics.
In the Database environment, data is accessed, modified, controlled, and then presented into a
well-organized form, allowing the business corporations to execute multiple data-processing
operations. The data is usually organized in the form of rows and columns to minimize the
workload pressure and achieve accurate results instantly.
Different types of data that can be stored, processed, or retrieved in Database Management
System include numerical, time series, textual and binary data.

5. The figure below highlights what is Database Systems and how they are used to monitor and
collect data from multiple sources to gather valuable business insights from them:

6. 9 Key Characteristics of Database Systems
By now, you are fairly clear on the idea of what is Database Systems. Let’s now have a look at
the many characteristics that make them suitable for handling multiple data sources and also
helping in Data Analytics to gather valuable business insights. The key characteristics of
Database Systems are given below:
•Less Duplication
•Limited Redundancy
•Ease of Use
•Multiple Layouts and Presentations
•Reduces Storage Space
•Data Security
•Data Recovery and Backup Plan
•Maintaining Integrity
•Improvised Efficiency

7. 1) Less Duplication
Database Systems provides a specific identity number for each entry. By having a specific ID
number for all entries, users won’t experience duplication errors and issues.
2) Limited Redundancy
Undoubtedly, there are high chances of data repetition as multiple users use the same version/
software of a Database to store their files. To avoid large chances of redundancy, a DBMS offers
a single data repository and various Data Mapping functionalities.
3) Ease of Use
There’s no need to get noble experience or technical skills to use a DBMS. The reason is all
these tools contain a smooth and easy-to-use interface. Whether you’re familiar with
programming languages or not, you can easily use queries to insert, update, delete or search
records in Database Systems.
4) Multiple Layouts and Presentations
Database Systems has different layouts and presentation formats through which one can easily
select knowledge and language options, according to his/her expertise. Some Databases
contain translating options that allow you to move from one layout to another without making
any change in the integrity of data.

8. 5) Reduces Storage Space
Public and private companies use Database Systems to save a massive amount of data, files,
documents, media, audio and video extensions. Companies need a lot of space to store these
assets, but DBMS provides proper integration, helping users to reduce space as compared to
traditional systems. This functionality permits enterprises to save cost as well.
6) Data Security
Security of data is the foremost and essential need for companies as hacking is common in this
digital world. DBMS is accessible to all users, employees, clients, thus different policies, and rules
must be implemented to restrict multiple windows.
While keeping this in mind, Database Systems are built with tenacious security functions that
allow companies to protect confidential information. Enterprises deploy policies to restrict access
for particular users, letting them minimize security breaches and insiders attacks.
7) Data Recovery and Backup Plan
Nowadays, Database Systems are coming up with data recovery and backup options. Companies
know that intentional and unintentional events can occur at any time. For instance, in some cases
employees remove data accidentally, developers delete or discard manufacturing and production
tables.
Consequently, DBMS are embedded with Data Recovery options and a Backup Plan to avoid
such incidents. They work like a permanent storage plan in which it is impossible to eradicate
data.

9. 8) Maintaining Integrity
Database Systems contain schemas, primary and secondary key options that permit
companies, especially E-Commerce and inventory stores, to maintain integrity, consistency
and concurrency of data.
9) Improvised Efficiency
With functions and tools of DBMS, raw information gets converted into valuable statistics.
Companies use these statistics to make a wise and quick decision in a Real-Time
environment. It advances the Database’s performance and efficiency of the system.

10. Languages Supported by Database Systems
Database Systems comprise of specific languages that are used by operators, programmers
and end-users to interact with Database queries and updates. There are generally 4 types of
Database Languages:
•Data Definition Language (DDL)
•Data Control Language (DCL)
•Data Manipulation Language (DML)
•Transaction Control Language (TCL)

11. 1) Data Definition Language (DDL)
It is also called Data Description Language and is used to describe data structures, create and
modify data. SQL commands and statements like Create, Alter, Drop, Truncate, Rename, and
Comment are used to form the pattern of the Database.
2) Data Control Language (DCL)
DCL commands include Revoke and Grant used to retrieve previously stored and saved data.
The syntax of DCL commands is similar to programming languages. These statements play an
essential role to describe the ‘‘Rights & Permissions’’ across the Database system.
3) Data Manipulation Language (DML)
DML commands include Select, Insert, Update, Delete, Merge and Call. These are used to
access and manipulate data in the Database. These statements are commonly meant for
handling user requests.
4) Transactional Control Language (TCL)
TCL is used to handle all the transactions within Database Systems. TCL commands include
Commit, Rollback and SavePoint.

12. The figure below depicts all the languages in a DBMS
along with their commands:

13. Types of Database Systems
There are 4 mainly types of Database Systems:
•Hierarchical Database System
•Network Database System
•Relational Database System
•Object-Oriented Database System
Hierarchical Database System
The Hierarchical Database System follows a tree-like procedure to present the data. It arranges
data in either Top-Down or Down-Up flow and defines the flow through the parent-child
relationship.
The Hierarchical Database System includes two types of relationships; One-to-One and One-to-
Many relationship. A parent can have only one child in a One-to-One relationship, whereas a
parent can have more than one child in a One-to-Many relationship.
Some of the popular Hierarchical Database Systems include IBM Information Management
Systems (IMS), Windows Registry, RDM Mobile, XML, and XAML.

14. The figure below depicts the Hierarchical Database
System:

15. 2) Network Database System
The Network Database System enables users to build Many-to-Many relationships due to
which it is more complicated and intricated than the other types of DBMS. It is feasible for
users to access data from the Network Database System as data is arranged in a graphical
format and can be acquired through different data routes.
By having a Many-to-Many relationship, a child can have more than one parent and vice
versa. In this way, multiple relationships can be built in a Network Database System,
permitting enterprises to achieve efficiency.
Some of the popular Network Database Systems include Integrated Database Management
System (IDMS), Raima Database Manager, TurboIMAGE, Integrated Data Store (IDS) and
Univac DMS-1100.

16. The given figure below depicts the Network Database
System:

17. 3) Relational Database System
In the Database field, the Relational Database System is one of the most extensive and
complicated ones. It allows developers and programmers to normalize data and organize
information as rationally independent tables.
Connections are made by using ‘‘Select’’ and ‘‘Join’’ options. The concept of referential
integrity is used in Relational Database Systems to preserve the reliability of the
connection between different tables.
Some of the popular Relational Database Systems include DB2 and Informix Dynamic
Server, Microsoft Access & SQL Server, RDB and Oracle.

18. The figure below depicts the Relational Database System:

19. 4) Object-Oriented Database System
In an Object-Oriented Database System, diverse programming languages, such as Perl,
Scala, .NET, Java, Python, JavaScript, Delphi, Visual Basic and C++, are used by
programmers to build relationships between variables and establish schemas.
Some of the popular Object-Oriented Database Systems include Cache, ConceptBase.cc,
Db4o (Database 4 objects).

20. The figure below depicts the Object-Oriented Database
System:

21. Advantages of Database Systems
Now that have understood about Database Systems, different languages it supports, and types
of Database Systems. In this section, you will read about the advantages of Database Systems.
A few benefits of Database Systems are listed below:
•Data Safety: As the number of users accessing the Database increases, the threats to data
breaches increase. Database Systems ensure data confidentiality and safety through controlled
user access.
•Improves Efficiency: Using better-streamlined software to access data that can convert data
into valuable information for analysis helps companies make better data-driven business
decisions.
•Data Sharing: Database Systems or DBMS allow users to easily share data, whether it’s
available on On-premise Database or remote users by following the correct authorization
protocols. It provides well-managed data to get faster query responses.
•Data Integration: Data Systems support many integrations and provide users a holistic view
of the data. It also helps users to know how different activities affect other activities and monitor
the progress of the company’s activities.
•Better Decision Making: Database Systems keep the data in a well-managed form, which
helps businesses to have better capacity in making sound decisions.

22. Applications of Database Systems
Let’s go through some of the most common applications of Database Systems or DBMS. A few
applications are listed below:
•Telecommunication: Databases Systems store all the data related to monthly bills, call
archives, user information, retaining balances, subscription packages, and other details.
•Sales and Marketing: Companies store all the user information, Sales details, prospects,
leads, and information on Marketing Campaigns in Database Systems.
•Airlines: All the information on flight bookings, payments, customers, offers, destination, and
venue is stored in Databases.
•Human Resources: Database Systems store and manage all the data related to salary,
employees, departments, finances, deductions, and other confidential information.
•Banks: DBMS stores all the data related to clients and their bank accounts, deposit and
withdrawal, credits, and mortgages.
•Education: Student’s details, records, marks, achievements, courses, and other details are
managed in Database Systems.
•Economics and Finance: Database Systems store all the data on transactions, bonds, fiscal
instruments such as shares.

23. Functions and Components of a
Database Management Systems

24. What is Data?
Before we get into the concept of a database, we should first understand what data is. Put
simply, data are pieces of information or facts related to the object being considered. For
example, examples of data relating to an individual would be the person’s name, age, height,
weight, ethnicity, hair color, and birthdate. Data is not limited to facts themselves, as pictures,
images, and files are also considered data.
There are a few key terms that would be useful to help one understand data more,
particularly the relation between data and databases.
Fields: Within a database, a field contains the most detailed information about events,
people, objects, and transactions.
Record: A record is a collection of related fields.
Table: A table is a collection of related records with a unique table name
Database: A database is a collection of related tables.

25. What is the Role of Databases in an Enterprise?
Enterprises typically make use of both internal databases and external databases. Internal
databases typically include operational databases and data warehouses. The former,
operational databases, refer to databases that are actively used in the operations of the
business, such as accounting, sales, finance, and HR.
Data warehouses contain data collected from several sources, and the data contained
within are generally not used for routine business activities. Instead, data warehouses are
usually used for business intelligence purposes. External databases refer to databases
external to an organization and are generally accessed over the Internet and are owned by
other organizations. An example of an external database is the SEC database.

26. Components of a Database
The five major components of a database are:
1. Hardware
Hardware refers to the physical, electronic devices such as computers and hard disks that
offer the interface between computers and real-world systems.
2. Software
Software is a set of programs used to manage and control the database and includes the
database software, operating system, network software used to share the data with other
users, and the applications used to access the data.

27. 3. Data
Data are raw facts and information that need to be organized and processed to make it more
meaningful. Database dictionaries are used to centralize, document, control, and coordinate
the use of data within an organization. A database is a repository of information about a
database (also called metadata).
4. Procedures
Procedures refer to the instructions used in a database management system and encompass
everything from instructions to setup and install, login and logout, manage the day-to-day
operations, take backups of data, and generate reports.
5. Database Access Language
Database Access Language is a language used to write commands to access, update, and
delete data stored in a database. Users can write commands using Database Access
Language before submitting them to the database for execution. Through utilizing the
language, users can create new databases, tables, insert data, and delete data.

29. Database Normalization

30. Database normalization is the process of organizing data into tables in such a way that the
results of using the database are always unambiguous and as intended. Such normalization
is intrinsic to relational database theory. It may have the effect of duplicating data within the
database and often results in the creation of additional tables.
The concept of database normalization is generally traced back to E.F. Codd, an IBM
researcher who, in 1970, published a paper describing the relational database model. What
Codd described as "a normal form for database relations" was an essential element of the
relational technique. Such data normalization found a ready audience in the 1970s and
1980s -- a time when disk drives were quite expensive and a highly efficient means for data
storage was very necessary. Since that time, other techniques, including denormalization,
have also found favor.

31. Data normalization rules
While data normalization rules tend to increase the duplication of data, it does not introduce
data redundancy, which is unnecessary duplication. Database normalization is typically a
refinement process after the initial exercise of identifying the data objects that should be in
the relational database, identifying their relationships and defining the tables required and
the columns within each table.
If this table is used for the purpose of keeping track of the price of items and the user want
to delete one of the customers, he or she will also delete the price. Normalizing the data
would mean understanding this and solving the problem by dividing this table into two
tables, one with information about each customer and the product they bought and the
second with each product and its price. Making additions or deletions to either table would
not affect the other.

32. Normalization degrees of relational database tables have been defined and include:
First normal form (1NF). This is the "basic" level of database normalization, and it
generally corresponds to the definition of any database, namely:
•It contains two-dimensional tables with rows and columns.
•Each column corresponds to a subobject or an attribute of the object represented by the
entire table.
•Each row represents a unique instance of that subobject or attribute and must be different
in some way from any other row (that is, no duplicate rows are possible).
•All entries in any column must be of the same kind. For example, in the column labeled
"Customer," only customer names or numbers are permitted.

33. Second normal form (2NF). At this level of normalization, each column in a table that is
not a determiner of the contents of another column must itself be a function of the other
columns in the table. For example, in a table with three columns containing the customer
ID, the product sold and the price of the product when sold, the price would be a function
of the customer ID (entitled to a discount) and the specific product. In this instance the data
in the third column is said to be dependent upon the data in the first and second columns.
This dependency does not occur in the 1NF case.
The column labeled customer ID is considered a primary key because it is a column that
uniquely identifies the rows in that table, and it meets the other accepted requirements in
standard database management schema: It does not have NULL values and its values
won't change over time.
In the example above, the other column headers are considered candidate keys. The
attributes of those candidate keys that make them unique are called prime attributes.

34. Third normal form (3NF). At the second normal form, modifications are still possible
because a change to one row in a table may affect data that refers to this information from
another table. For example, using the customer table just cited, removing a row describing
a customer purchase (because of a return, perhaps) will also remove the fact that the
product has a certain price. In the third normal form, these tables would be divided into two
tables so that product pricing would be tracked separately.
Extensions of basic normal forms include the domain/key normalized form, in which
a key uniquely identifies each row in a table, and the Boyce-Codd normal form (BCNF),
which refines and enhances the techniques used in the 3NF to handle some types of
anomalies.

35. Database normalization's ability to avoid or reduce data anomalies, data redundancies and
data duplications, while improving data integrity, has made it an important part of the data
developer's toolkit for many years. It has been one of the hallmarks of the relational data
model.
The relational model arose in an era when business records were, first and foremost, on
paper. Its use of tables was, in some part, an effort to mirror the type of tables used on paper
that acted as the original representation of the (mostly accounting) data. The need to support
that type of representation has waned as digital-first representations of data have replaced
paper-first records.
But other factors have also contributed to challenging the dominance of database
normalization.
Over time, continued reductions in the cost of disk storage, as well as new analytical
architectures, have cut into normalization's supremacy. The rise of denormalization as an
alternative began in earnest with the advent of data warehouses, beginning in the 1990s.
More recently, document-oriented NoSQL databases have arisen; these and other
nonrelational systems often tap into nondisk-oriented storage types. Now, more than in the
past, data architects and developers balance data normalization and denormalization as they
design their systems.

36. Database normalization tools
Data modeling software can incorporate features that help automate preparing incoming data
for analysis. IT managers still need to develop a plan to address common problems, including
data normalization. Vendors in data normalization include 360Science, ApexSQL and many
other smaller niche developers.

37. Entity Relationship Diagram
and Relational Modeling

38. Difference between ER Model and Relational Model
The E-R Model and Relational Model are two aspects of the Data Model in DBMS that
are used to construct databases at the physical, logical, and view levels. This article
explains the complete overview of the E-R Model and Relational Model. The difference
between these models is the most common part of an interview question. The key
distinction is that the E-R Model is entity-specific, while the Relational Model is
table-specific. Before making the comparison, we will first know these Data Models.

39. What is ER Model?
An ER model stands for the Entity-Relationship model that Peter Chen developed in
1976. This model consists of a collection of entities (Real word objects) and their
relationships. It describes the database's conceptual view. We must ensure that no two
entities are identical in this context.
ER Model describes the system's logical view from a data perspective formed by the entity
set, relationship set, and attributes. In this model, all entities come under the entity set, all
relations between the entities come under the relationship set, and attributes describe the
properties of entities.
Let us understand ER model with an example. Suppose we have two real-world
entities named Student and Branch that will further form an Entity set. Now we can easily
form a relation between them as the Student belongs to a Branch. It shows how we can
get a relationship set from ER Model. Noted that the ER Model content must conform to
constraints like mapping cardinality. Finally, the attributes of these entities would be:
For Student: stud_id, stud_name, address, mobile, mail-id.
For Branch: branch_id, branch_name, num_of_stud.

40. What is the Relational Model?
In 1970, E.F. Codd developed the relational model. He proposed this model as well as a
non-procedural approach for modeling data in the form of relations or tables. In the
Relational Model, tables are usually interpreted as relations. If we model the database
using ER diagrams, we must convert them into the relational model, which can be
implemented by one of the RDBMS languages such as SQL and MySQL.
In the relational model, each table contains rows and columns where we can have any
number of rows, but the number of columns must be definite. The table rows are called
tuples that include the complete information about specific entities. Records are a set of
tuples, so the Relational model is also known as the Record-based Model. Table columns
are called attributes because they characterize a table's properties. To store values, each
attribute must have a type.

41. Key Differences between ER Model and Relational Model
The following points explain the main differences between ER Model and Relational Model:
•The main distinction between the ER model and the Relational Model is that the ER model
describes the relationship between entities and their attributes. On the other hand, the
Relational Model referred to the implementation of our model.
•The Relational Model is the implementation or representational model, while the ER Model is
the high-level or conceptual model.
•The data in components such as entity sets, relationship sets, and attributes are represented
by an ER model. The Relational model, on the other hand, defines data in components such
as tuples, attributes, and attribute domains.
•As compared to a Relational Model, an ER model makes it easier to understand the
relationships between entities.
•Mapping Cardinality is always a constraint in the ER model, while the cardinality constraint
cannot be defined in the Relational Model.

42. ER Model vs. Relational Model Comparison Chart
The following comparison chart explains their main differences in a quick manner: