Fundamentals of information systems chapter 3.ppt

Chapter 3
Database System and Big Data

Introduction
• Database: is an organized collection of data
• It is a well-designed, organized, and carefully managed collection of data
• It can contribute to organizational success by providing managers and decision makers
with timely, accurate, and relevant information built on data.
• It also help companies analyze information to reduce costs, increase profits, add new
customers, track past business activities, and open new market opportunities.
• A database management system (DBMS) is a group of programs that:
• Access, manage and manipulate the database
• Provide an interface between the database and its users and other application programs
• provides a single point of management and control over data resources, which can be
critical to maintaining the integrity and security of the data.
• A database, a DBMS, and the application programs that use the data make up a database
environment
• Databases and database management systems are becoming even more important to
organizations as they deal with rapidly increasing amounts of information.
• Most organizations have many databases; however, without good data management, it is
nearly impossible for anyone to find the right and related information for accurate and
business-critical decision making

Introduction
• Data fundamentals :
• Without data and the ability to process it,
• An organization could not successfully complete most business
activities.
• It cannot pay employees, send out bills, order new inventory, or
produce information to assist managers in decision making.
• Data consists of raw facts such as employee numbers and sales
figures
• Data must be organized in a meaningful way to transform it into
useful information

Hierarchy of Data
• A bit (binary digit i.e.0 or 1) that represents a circuit that is either on
or off
• Bits can be organized into units called bytes
• A byte is made up of eight bits
• Each byte represents a character
• Character: is the basic building block of most information.
• Field: a name, number, or combination of characters that describes an
aspect of a business object or activity
• Record: a collection of related data fields
• File: a collection of related records
• Database: a collection of integrated and related files
• Hierarchy of data: bits, characters, fields, records, files, and
databases

Data Entities, Attributes, and Keys
• Entity: a person, place, or thing for which data is collected, stored,
and maintained. Examples of entities include employees, products,
and customers.
• Most organizations organize and store data as entities.
• Attribute: a characteristic of an entity. For example, employee
number, last name, first name, hire date, and department number
are attributes for an employee.
• Data item: the specific value of an attribute
• Primary key: a field or set of fields that uniquely identifies the
record
• No other record can have the same primary key.
• The primary key is used to distinguish records so that they can be accessed,
organized, and manipulated. Primary keys ensure that each record in a file is
unique
• A data key is a field within a record that is used to identify the
record

Data Entities, Attributes, and Keys

The Database Approach
• Traditional approach to data management
• Each distinct operational system used data files dedicated to that system
• Database approach to data management
• Information systems share a pool of related data
• Offers the ability to share data and information resources
• To use the approach A database management system (DBMS) is required
• Typically, this software acts as a buffer between the application
programs and the database itself.

Data Modeling and Database Characteristics
• A database should be designed to store all data relevant to the
business and to provide quick access and easy modification.
• Moreover, database must reflect the business processes of the
organization
• Considerations when building a database
• Content: what data should be collected? cost?
• Access: what data should be provided to which users and when?
• Logical structure: how should data be arranged so that it makes
sense?
• Physical organization: where should data be physically located?
• Archiving: how long to store?
• Security: how can data be protected?

Data Modeling
• One of the tools database designers use to show the logical
relationships among data is a data model.
• Data model: a diagram of data entities and their relationships
• Data modeling usually involves developing an understanding
of a specific business problem and then analyzing the data and
information needed to deliver a solution
• Enterprise data modeling is a data modeling done at the level
of the entire enterprise.
• Enterprise data modeling is an approach that starts
• by investigating the general data and information needs of the
organization at the strategic level and
• then moves on to examine more specific data and information needs
for the functional areas and departments within the organization.
• It involves analyzing the data and information needs of an entire
organization
• It provides a roadmap for building database and information systems

Data Modeling
•Entity-relationship (ER) diagrams: data models that use basic graphical
symbols to show the organization of and relationships between data
•ER diagrams show data items in tables (entities) and the ways they
are related.
•In most cases, boxes in ER diagrams indicate data items or entities
contained in data tables, and lines show relationships between
entities
•ER diagrams help ensure that the relationships among the data
entities in a database are correctly structured so that any application
programs developed are consistent with business operations and user
needs.
•In addition, ER diagrams can serve as reference documents after a
database is in use.
•If changes are made to the database, ER diagrams help design them

Relational Database Model
• Relational model: a simple but highly useful way to organize data
into collections of two-dimensional tables called relations
• Relation or table represents an Entity
• Each row in the table represents records or instances of an entity
• Each column represents an attribute of that entity
• Domain: range of allowable values for a data attribute

Manipulating Data
•Selecting: eliminating rows according to certain criteria
•Projecting: eliminating columns in a table
•Joining: combining two or more tables
•Linking: combining two or more tables through common
data attributes to form a new table with only the unique
data attributes

Data Cleansing
• It is also called data cleaning or data scrubbing
• The process of detecting and then correcting or deleting
incomplete, incorrect, inaccurate, irrelevant records that reside in a
database
• The cost of performing data cleansing can be quite high
• Data cleaning is different from data validation
• Which involves the identification of “bad data” and its rejection at the
time of data entry.

Relational Database Management Systems (DBMSs)
•Relational DBMSs:
• Creating and implementing the right database system ensures
that the database will support both business activities and goals
• Capabilities and types of database systems vary considerably
•SQL Databases :
•SQL: a special-purpose programming language for
accessing and manipulating data stored in a relational
database
•SQL databases conform to ACID properties:
-Atomicity, consistency, isolation, and durability
•SQL was adopted by ANSI 1986 as the standard query
language for relational databases

Database Activities
•A) Providing a user view of the database
•B) Creating and modifying the database
•C) Storing and retrieving data
•D) Manipulating the data and generating reports
•A) Providing a User View:
•Schema: a description of the entire database
•A schema can be part of the database or a separate
schema file
•The DBMS can reference a schema to find where to
access the requested data in relation to another piece of
data

B) Creating and Modifying the Database:
• Data definition language (DDL)
• A collection of instructions and commands used to define and describe
data and relationships in a specific database
• Allows the database’s creator to describe data and relationships that are
to be contained in the schema
• Data dictionary: a detailed description of all the data used in the database
• It can also include a description of data flows, information about the
way records are organized, and the data-processing requirements

C) Storing and Retrieving Data:
• When an application program needs data, it requests the data
through the DBMS
• Concurrency control deals with the situation in which two or
more users or applications need to access the same record at the
same time

D) Manipulating Data and Generating Reports:
•Query by Example (QBE) is a visual approach to
developing database queries or requests
•Data manipulation language (DML): a specific language,
provided with a DBMS
•Allows users to access and modify the data, to make
queries, and to generate reports
•A DBMS can produce a wide variety of documents,
reports, and other output that can help organizations
achieve their goals

Manipulating Data and Generating Reports

Database Administration
• Database administrators(DBAs): skilled and trained Information
System professionals
• Works with users to define their data needs
• Applies database programming languages to craft a set of
databases to meet those needs
• Tests and evaluates databases
• Implements changes to improve their databases’ performance
• Assures that data is secure from unauthorized access
• Data administrator: a non technical position responsible for
defining and implementing consistent principles for a variety of
data issues
• Including setting data standards and data definitions that apply across all the
databases in an organization
• The data administrator can be a high-level position reporting to
top-level managers

Popular Database Management Systems
• Database as a Service (DaaS)
• The database is stored on a service provider’s servers
• The database is accessed by the client over a network, typically
the Internet
• Database administration is handled by the service provider
• Example of DaaS: Amazon Relational Database Service (Amazon
RDS)

Using Databases with Other Software
•DBMSs can act as front-end or back-end
applications
•Front-end applications interact directly with people
•Back-end applications interact with other programs or
applications
•Example:
•The Library of Congress (LOC) provides a back-end
application that allows Web access to its databases,
which include references to books and digital media in
the LOC collection

Big Data
• Extremely large and complex data collections
• Traditional data management software, hardware, and analysis processes are
incapable of dealing with them
• Four characteristics of big data
• Volume
• Velocity
• Variety
• Veracity
• Sources of Big Data:

Big Data Uses
• Examples:
• Retail organizations monitor social networks to engage brand advocates,
identify brand adversaries
• Advertising and marketing agencies track comments on social media
• Hospitals analyze medical data and patient records
• Consumer product companies monitor social networks to gain insight into
consumer behavior
• Financial service organizations use data to identify customers who are likely
to be attracted to increasingly targeted and sophisticated offers
• Challenges of Big Data:
-How to choose what subset of the data to store
-Where and how to store the data
-How to find the nuggets of data that are relevant to the decision
making at hand
-How to derive value from the relevant data
-How to identify which data needs to be protected from unauthorized
access

Data Management
• Data management
• An integrated set of functions that defines the processes by which data
is obtained, certified fit for use, stored, secured, and processed in such
a way as to ensure that the accessibility, reliability, and timeliness of
the data meet the needs of the data users within an organization
• Data governance
• Defines the roles, responsibilities, and processes for ensuring that data
can be trusted and used by an entire organization

Data Management
•Data management is driven by a variety of factors:
• The need to meet external regulations designed to manage risk
associated with financial misstatement
• The need to avoid the inadvertent release of sensitive data
• The need to ensure that high data quality is available for key
decisions
•Data governance requires business leadership and active
participation
• Use of a cross-functional tea is recommended
• Team should consist of executives, project managers, line-of-
business managers, and data stewards
• A data steward is an individual responsible for management of
critical data elements

Data Management
• Data lifecycle management (DLM)
• A policy-based approach to managing the flow of an enterprise’s data

Data Warehouses, Data Marts, and Data Lakes
• Data warehouse: a large database that collects business information
from many sources in the enterprise in support of management
decision making
• ETL process:
• Extract
• Transform
• Load

Data Warehouses, Data Marts, and Data Lakes
•Data mart: a subset of a data warehouse that is used by
small- and medium-sized businesses and departments
within large companies to support decision making
•A specific area in the data mart might contain greater
detailed data than the data warehouse
•Data lake: takes a “store everything” approach to big
data, saving all the data in its raw and unaltered form
• Also called an enterprise data hub
• Raw data is available when users decide just how they want to
use the data
• Only when the data is accessed for a specific analysis is it
extracted from the data lake

NoSQL Databases
• NoSQL database
• Provides a means to store and retrieve data that is modeled using some means
other than the simple two-dimensional tabular relations used in relational
databases
• Advantages:
• Ability to spread data over multiple servers so that each server contains only a
subset of the total data
• Do not require a predefined schema
• Data structures are more flexible and can provide improved access speed and
redundancy.

Hadoop
•Hadoop
•An open-source software framework that includes
several software modules that provide a means for
storing and processing extremely large data sets
•Has two primary components:
•A data processing component (MapReduce)
•A distributed file system (Hadoop Distributed File
System, HDFS)

In-Memory Databases
• In-memory database (IMDB)
• A database management system that stores the entire database in random
access memory (RAM)
• Provides access to data at rates much faster than storing data on some form of
secondary storage
• Enables the analysis of big data and other challenging data-processing
applications
• Performs best on multiple multicore CPUs

THE END !!!
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Fundamentals of information systems chapter 3.ppt

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