Database Management
System II
Lesson 1: Overview of Database System Concepts
Nurudeen Mohammed, PhD
UPSA IT Department
Outline
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DBMS?
Types of DBMS
Database-System Applications
Benefits and Challenges of DBMS
Data Abstraction
Data Independence
Database Engine
Database Architecture
Database Users and Administrators
History of Database Systems
DBMS
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Data?
Raw facts about an object or entity under consideration which can be recoded.
Format: Text, image, voice, video, numbers and so on
• Database?
A database is a collection of related data which represents some aspect of the real world.
A database is an organized collection of data, generally stored and accessed electronically from a
computer system.
• DBMS ?
A software for storing and retrieving users' data while considering appropriate security measures.
 Collection of interrelated data
 Set of programs to access the data
 An environment that is both convenient and efficient for users
• What kind of Data should DBMS manage?
 Highly valuable
 Relatively large
 Accessed by multiple users and applications, often at the same time.
DBMS Software
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MySQL
Microsoft Access
Oracle
PostgreSQL
dBASE
FoxPro
SQLite
IBM DB2
LibreOffice Base
MariaDB
Microsoft SQL Server
Types of DBMS
Hierarchical DBMS
 Data is organized in a tree-like structure.
 Data is Stored Hierarchically (top down or
bottom up) format.
 Data is represented using a parent-child
relationship.
 Parent may have many children, but
children have only one parent.
Types of DBMS
Network Model
 It allows each child to have multiple
parents.
 It helps allows many-to-many
relationship.
 Entities are organized in a graph and
there can several pathways access
each entity.
Types of DBMS
Relational model
 It is the most widely used DBMS model.
 It is based on normalizing data in the
rows and columns of tables.
 It stored in fixed structures and
manipulated using SQL.
Types of DBMS
Object-Oriented Model
 In Object-oriented Model data stored
in the form of objects.
 It is structured into classes which
display data within it.
 It defines a database as a collection of
objects which stores both data values
and operations.
Schematic Diagram of DBMS
Database-System Applications
• Banking Application
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For customer information
account activities
payments
deposits
loans
Database-System Applications
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Flight Ticket Reservation
Booking a cab (Uber, Yango, Bolt)
Hotel Reservation
Hospital appointments
Consular services
Online restaurants
Other Application of DBMS
Universities
For student information, course registrations, colleges and grades.
Telecommunication
It helps to keep call records, monthly bills, maintaining balances, etc.
Finance
For storing information about stock, sales, and purchases of financial instruments like stocks and bonds.
Sales
Use for storing customer, product & sales information.
Manufacturing
It is used for the management of supply chain and for tracking production of items. Inventories status in
warehouses.
HR Management
For information about employees, salaries, payroll, deduction, generation of paychecks, etc.
Benefits of DBMS
• DBMS was designed to solve the fundamental problems
associated with storing, managing, accessing, securing, and
auditing data in traditional file systems.
• Traditional database applications were developed on top of the
databases, which led to challenges such as:
 Data redundancy.
Isolation.
Integrity constraints.
Difficulty managing data access.
Challenges of DBMS
• Cost of Hardware and Software of a DBMS is quite high
• Most database management systems are often complex systems, so the
training for users to use the DBMS is required.
• In some organizations, all data is integrated into a single database which
can be damaged because of electric failure
• Use of the same program at a time by many users sometimes lead to
the loss of some data.
• DBMS can't perform sophisticated calculations
Data Abstraction
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A major purpose of a database system is to provide users with an abstract view of the data.
 There are 3 level of the data abstraction
External
Schema
Conceptual Schema
Internal Schema
 Data abstraction allows us to achieve data independence
Data Independence
• Data Independence - is the characteristic in which we are able to modify the
schema at one level of database system without altering the schema at the next
higher level .
• There are 2 types of data independence base on 3 layers of data abstraction.
• Physical Data Independence ?
• the ability to modify the physical schema without changing the logical schema
 So when we change the location of a database it should not result in changes in
the structure of our tables.
 Also, when we change data structures, access methods or when we modify
indexes, file organization techniques .These are changes in the physical schema
and it should not affect the logical schema or conceptual schema.
Data Independence
• Logical Data Independence?
 the ability to modify the logical schema without affects the applications or
external schema
 Eg. If you modify attributes or entities in a table it should not result in
modifications in your user applications or external schema.
Also if you changes the relationship between tables,merge or divide records, alter
table structure it should not result in modifications in your user applications or
external schema.
In general, the interfaces between the various levels and components should be well
defined so that changes in some parts do not seriously influence others.
Data Independence
Benefits:
• Ability to improved performance as we can make changes to
both physical and logical schemas without any complications
we improve performance.
• Modifications in data structures does not require modification
in application programs
• Implementation details can be hidden from the user. So the
user is only shown the data relevant to him. All other data is
hidden from him for better user interaction
Database Engine
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A database system is partitioned into modules that deal with each of the responsibilities of the overall
system.
The functional components of a database system can be divided into
 The storage manager,
 The query processor component,
 The transaction management component.
Storage Manager
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A program module that provides the interface between the low-level data stored in the database and the
application programs and queries submitted to the system.
The storage manager is responsible to the following tasks:
 Interaction with the OS file manager
 Efficient storing, retrieving and updating of data
The storage manager components include:
 Authorization and integrity manager
 Transaction manager
 File manager
 Buffer manager
Storage Manager (Cont.)
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The storage manager implements several data structures as part of the physical system implementation:
 Data files -- store the database itself
 Data dictionary -- stores metadata about the structure of the database, in particular the schema of the
database.
 Indices -- can provide fast access to data items. A database index provides pointers to those data items
that hold a particular value.
Query Processor
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The query processor components include:
 DDL interpreter -- interprets DDL statements and records the definitions in the data dictionary.
 DML compiler -- translates DML statements in a query language into an evaluation plan consisting of lowlevel instructions that the query evaluation engine understands.
 The DML compiler performs query optimization; that is, it picks the lowest cost evaluation plan from
among the various alternatives.
 Query evaluation engine -- executes low-level instructions generated by the DML compiler.
Query Processing
1. Parsing and translation
2. Optimization
3. Evaluation
Transaction Management
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A transaction is a collection of operations that performs a single logical function in a database application
Transaction-management component ensures that the database remains in a consistent (correct) state
despite system failures (e.g., power failures and operating system crashes) and transaction failures.
Concurrency-control manager controls the interaction among the concurrent transactions, to ensure the
consistency of the database.
Database Architecture
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Centralized databases
 One to a few cores, shared memory
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Client-server,
 One server machine executes work on behalf of multiple client machines.
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Parallel databases
 Many core shared memory
 Shared disk
 Shared nothing
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Distributed databases
 Geographical distribution
 Schema/data heterogeneity
Database Architecture (Centralized/Shared-Memory)
Database Applications
Database applications are usually partitioned into two or three parts
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Two-tier architecture -- the application resides at the client machine, where it invokes database system
functionality at the server machine
Three-tier architecture -- the client machine acts as a front end and does not contain any direct database
calls.
 The client end communicates with an application server, usually through a forms interface.
 The application server in turn communicates with a database system to access data.
Two-tier and three-tier architectures
Database Users
Database Administrator
A person who has central control over the system is called a database administrator (DBA). Functions of a DBA
include:
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Schema definition
Storage structure and access-method definition
Schema and physical-organization modification
Granting of authorization for data access
Routine maintenance
Periodically backing up the database
Ensuring that enough free disk space is available for normal operations, and upgrading disk space as
required
Monitoring jobs running on the database
History of Database Systems
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1950s and early 1960s:
 Data processing using magnetic tapes for storage
 Tapes provided only sequential access
 Punched cards for input
Late 1960s and 1970s:
 Hard disks allowed direct access to data
 Network and hierarchical data models in widespread use
 Ted Codd defines the relational data model
 Would win the ACM Turing Award for this work
 IBM Research begins System R prototype
 UC Berkeley (Michael Stonebraker) begins Ingres prototype
 Oracle releases first commercial relational database
 High-performance (for the era) transaction processing
History of Database Systems (Cont.)
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1980s:
 Research relational prototypes evolve into commercial systems
 SQL becomes industrial standard
 Parallel and distributed database systems
 Wisconsin, IBM, Teradata
 Object-oriented database systems
1990s:
 Large decision support and data-mining applications
 Large multi-terabyte data warehouses
 Emergence of Web commerce
History of Database Systems (Cont.)
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2000s
 Big data storage systems
 Google BigTable, Yahoo PNuts, Amazon,
 “NoSQL” systems.
 Big data analysis: beyond SQL
 Map reduce and friends
2010s
 SQL reloaded
 SQL front end to Map Reduce systems
 Massively parallel database systems
 Multi-core main-memory databases
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