Introduction to DBMS

 Database Management System (DBMS)

• Collection of interrelated data 

• Set of programs to access the data 

• DBMS contains information about a particular enterprise 

• DBMS provides an environment that is both convenient and efficient to use. 

• Database Applications: • Banking: all transactions 

                                             • Airlines: reservations, schedules 

                                            • Universities: registration, grades 

                                            • Sales: customers, products, purchases 

                                          • Manufacturing: production, inventory, orders, supply chain

Difference between File System and DBMS

FILE SYSTEM	DBMS
Used to manage and organise the files stored in the hard disk of the computer	A software to store and retrieve the user’s data
Redundant data is present	No presence of redundant data
Query processing is not so efficient	Query processing is efficient
Data consistency is low	Due to the process of normalisation, the data consistency is high
Less complex, does not support complicated transactions	More complexity in managing the data, easier to implement complicated transactions
Less security	Supports more security mechanisms
Less expensive in comparison to DBMS	Higher cost than the File system
Does not support crash recovery	Crash recovery mechanism is highly supported

Data models - relational, hierarchical, network

1. Hierarchical Data Model: 

Hierarchical data model is the oldest type of the data model. It was developed by IBM in 1968. It organizes data in the tree-like structure. Hierarchical model consists of the following :

• It contains nodes which are connected by branches.
• The topmost node is called the root node.
• If there are multiple nodes appear at the top level, then these can be called as root segments.
• Each node has exactly one parent.
• One parent may have many child.

2. Network Data Model: 

It is the advance version of the hierarchical data model. To organize data it uses directed graphs instead of the tree-structure. In this child can have more than one parent. It uses the concept of the two data structures i.e. Records and Sets.

3. Relational Data Model: 

The relational data model was developed by E.F. Codd in 1970. There are no physical links as they are in the hierarchical data model. Following are the properties of the relational data model :

• Data is represented in the form of table only.
• It deals only with the data not with the physical structure.
• It provides information regarding metadata.
• At the intersection of row and column there will be only one value for the tuple.
• It provides a way to handle the queries with ease.

Difference between Hierarchical, Network and Relational Data Model:

S. No.	Hierarchical Data Model	Network Data Model	Relational Data Model
1.	In this model, to store data hierarchy method is used. It is the oldest method and not in use today.	It organizes records to one another through links or pointers.	It organizes records in the form of table and relationship between tables are set using common fields.
2.	To organize records, it uses tree structure.	It organizes records in the form of directed graphs.	It organizes records in the form of tables.
3.	It implements 1:1 and 1:n relations.	In addition to 1:1 and 1:n it also implements many to many relationships.	In addition to 1:1 and 1:n it also implements many to many relationships.
4.	Pointers are used to establish relationships among records physically.	A linked list is used to establish a relationship among records physically.	The logical representation is used with rows and columns to depict relationship among records.
5.	Insertion anomaly exits in this model i.e. child node cannot be inserted without the parent node.	There is no insertion anomaly.	There is no insertion anomaly.

Schema – the logical structure of the database 

    • e.g., the database consists of information about a set of customers and accounts and the relationship     between them) 

    • Analogous to type information of a variable in a program 

            • Physical schema: database design at the physical level 

            • Logical schema: database design at the logical level 

Instance – the actual content of the database at a particular point in time 

    • Analogous to the value of a variable

 Levels of abstraction: There are mainly 3 levels of data abstraction: 

Physical level – it will contains block of storages (bytes,GB,TB,etc)

Logical level –  it will contain the fields and the attributes of data.

View level – it works with CLI or GUI access of database

Data Independence

A database system normally contains a lot of data in addition to users’ data. For example, it stores data about data, known as metadata, to locate and retrieve data easily. It is rather difficult to modify or update a set of metadata once it is stored in the database.

Logical Data Independence : 

Logical data is data about database, that is, it stores information about how data is managed inside. For example, a table (relation) stored in the database and all its constraints, applied on that relation. Logical data independence is a kind of mechanism, which liberalizes itself from actual data stored on the disk. If we do some changes on table format, it should not change the data residing on the disk. 

Physical Data Independence 

All the schemas are logical, and the actual data is stored in bit format on the disk. Physical data independence is the power to change the physical data without impacting the schema or logical data. For example, in case we want to change or upgrade the storage system itself − suppose we want to replace hard-disks with SSD − it should not have any impact on the logical data or schemas.

Structure of DBMS

Database Users 

• Users are differentiated by the way they expect to interact with the system 

• Application programmers – interact with system through DML calls 

• Sophisticated users – form requests in a database query language 

• Specialized users – write specialized database applications that do not fit into the traditional data processing framework 

• Naïve users – invoke one of the permanent application programs that have been written previously 

• E.g. people accessing database over the web, bank tellers, clerical staff