Database Systems: Concepts and Metadata

• Covers the fundamentals of database systems, including basic concepts, metadata, file processing disadvantages, data dependency/redundancy issues, and database approach advantages/disadvantages.
• Learning outcomes include understanding database concepts, metadata, constraints, file processing problems, database approaches, and their elements, advantages, along with costs and risks.

Basic Concepts of Database

• A database is an organized collection of logically related data.
• Data are stored representations of meaningful objects and events, which can be structured (numbers, text, dates) or unstructured (images, video, documents).
• Information is data processed to enhance the user's knowledge.
• Metadata describes the properties and context of user data.
• Context helps users interpret data.

Metadata and Constraints

• Metadata describes the properties or characteristics of data, including data types, field sizes, allowable values, and data context.
• Metadata includes properties that are typically described, such as data names, definitions, and length, as well as allowable values.
• Metadata describes the data context, including the source, storage location, ownership, and usage of the data.

Disadvantages of File Processing

• Program-data dependence means all programs maintain metadata for each file they use.
• Duplication of data occurs when different systems/programs have separate copies of the same data.
• Limited data sharing is due to no centralized control of data.
• Lengthy development times occur because programmers must design their own file formats.
• Excessive program maintenance can take up 80% of the information systems budget.

Problems with Data Dependency and Data Redundancy

• With data dependency, each application programmer has to maintain its own data and each application program needs to include code for the metadata as well as processing routines.
• Lack of coordination and central control and use of non-standard file formats are issues with data dependency.
• Data redundancy leads to wasted space and increased maintenance.
• Data inconsistency leads to integrity compromises.

The Database Approach

• Shared data has a central repository.
• Data is managed by a controlling agent
• Data is stored in a standardized, convenient form.

Database Management System

• A software system used to create, maintain, and provide controlled access to user databases.
• DBMS is a set of software tools that controls access, organize, store, manage, retrieve, and maintain data in a database.

Advantages of Database Approach

• It includes program-data independence, planned data redundancy, and improved data consistency.
• It has improved data sharing, increased application development productivity, and enforcement of standards.
• Improved data quality, improved data accessibility/responsiveness, reduced program maintenance, and improved decision support are also included.

Costs and Risks of Database Approach

• New, specialized personnel are required along with installation and management costs and complexity.
• There are conversion costs along with a need for explicit backup and recovery.
• There can be organizational conflict on data definitions, formats, coding, and rights to update.

Elements of Database Approach

• Data models are graphical systems capturing the nature and relationship of data.
• Entities are data models.
• There are relationships and relational databases.

Enterprise Data Model

• High-level entities and relationships for the organization.

Project Data Model

• Detailed view, matching data structure in database or data warehouse.
• Examples of entities with attributes included customer, order, order line, and products.
• Noun form describing a person, place, object, event, or concept.
• An attribute is a characteristic that describes that entity.
• There are one-to-one, one-to-many, many-to-one and many-to-many relationships.

Relational Databases

• Involve database technology.
• It has tables (relations) representing entities.
• Primary/foreign keys representing relationships.

Components of Database Environment

• Data modeling and design tools are automated tools used to design databases and application programs.
• A repository - centralized storehouse of metadata.
• The (DBMS) Database Management System software for managing the database.
• A database is a storehouse of the data.
• There are Application Programs-software using the data.
• Included text, graphical displays, menus, etc. for user.
• Data/Database Administrators-personnel are responsible for maintaining the database.
• System Developers-personnel responsible for designing databases and software.
• End Users people who use the applications and databases.

Enterprise Data Model details

• the first step in the database development process.
• Specifying scope and general content.
• Overall picture of organizational data at high level of abstraction:
  • Entity relationship diagram (ERD).

System Development Life Cycle

• Has a Waterfall approach. Each output from one phase will flow to the next phase.
• Composed of Planning, Analysis, Logical Design, Physical Design, Implementation and Maintenance.

Prototyping Database Methodology

• A classical Rapid Application Development (RAD) approach.
• One of the most popular RAD methods is prototyping, which is an iterative process of systems development in which requirements are converted to a working system.

Database Schema

• External schema differ based on the view and User.
• The conceptual schema is the view of the data architect and data administrator.
• The internal schema is the underlying design and implementation.

Managing Projects: People Involved

• People involved include Business analysts, Systems analysts, Database analysts and data modelers, Users, Programmers, Database architects, Data administrators, Project managers and Other technical experts.

Range of Database Applications

• Applications can be Personal databases, Two-tier and N-tier Client/Server databases or Enterprise applications,
• Enterprise applications can implement enterprise resource planning (ERP) systems or data warehousing.