DBAS Week 1 (Summer 2025)

What is a database?

Structured collection of logically related data.

Data v.s. Metadata v.s. Information

Data = raw facts

Metadata = data about data (describes context & properties)

Information = data that has been processed to have meaning; used for decision-making

DBMS

• Database Management System

• Software that manages databases and provides controlled access

• Examples: mySQL, Azure Data Studio

Limitations of Traditional File-Based Systems

1. Program-Data Dependence

2. Data Duplication & Redundancy

3. Limited Data Sharing

4. Lengthy Development and Maintenance Time

Name (*do not describe) 5 Advantages of the Database Approach

1. Program-Data Independence

2. Planned Redundancy

3. Data Consistency & Integrity

4. Improved Sharing & Accessibility

5. Higher Productivity for App Development

6. Enforced Standards (naming conventions, security)

7. Better Data Quality

8. Reduced Maintenance

9. Enhanced Decision Support

Name & briefly describe the Components of a Database Environment

(Hint: Don’t Dance Rigidly At Every Awkward Underground Disco—Sing Everything!)

DBMS

• Software that manages database

  • Manages storage, retrieval, and access of data

Repository

• Centralized storage of metadata (think of it like a “blueprint”)

Database

• Storehouse of data

Application Programs

• Software that uses the data

End Users

• Interact w/ the applications

Administrators & Developers

• Controls which personnel has access to database & type of access they have

User Interface

• Text, graphical displays, menus, etc.

Design Tools

• Automated tools used to design databases and application programs

System Developers

• Responsible for designing and maintaining databases and software.

End Users

• People who use the applications and databases.

Database Development Life Cycle (SDLC)

• Name and describe the 6 phases.

• What is the deliverable(s) at end of each phase?

PHASE	FOCUS	DELIVERABLE(S)
Planning	Define scope & goals	Request for study / Project proposal
Analysis	Gather database requirements	Functional specifications, Conceptual data model
Logical Design	Define data structures & business rules	Logical schema, Detailed data design
Physical Design	Choose hardware, storage, indexes setup	DBMS implementation plan, Physical schema
Implementation	Build & deploy database	Code, Training materials, System installation
Maintenance	Monitor, fix, and improve	Audits, Updates, Performance reports

Two Approaches:

• S D L C

  • System Development Life Cycle

  • Detailed, well-planned development process

  • Time-consuming, but comprehensive

  • Long development cycle

• Prototyping

  • Rapid application development (R A D)

  • Cursory attempt at conceptual data modeling

  • Define database during development of initial prototype

  • Repeat implementation and maintenance activities with new prototype versions

SDLC = System Development Life Cycle

• Detailed, well-planned development process

• Time-consuming, but comprehensive

• Long development cycle

Prototyping

• Rapid application development (R A D)

• Define database during development of initial prototype

• Adjust prototype and repeat implementation in resposne to feedback

In a relational model _____ is stored in tables and relations are built through _________________.

In a relational model data is stored in tables and relations are built through primary/foreign keys.

Describe the Three-Schema Architecture

1. External Schema: User view

2. Conceptual Schema: High-level business view (e.g. ER diagrams)

3. Internal Schema: Physical storage details

Property Metadata v.s. Contextual Metadata

Property Metadata (Think “Technical Specs” or “Business Rules”)

• Inherent characteristics about data

• Examples:

  • “Employee name is a 30-character string”

  • “Birth date is in YYYY-MM-DD format”

  • “Phone number must be 10 digits”

Contextual Metadata (Think “Backstory”)

• Tells you the meaning or rules behind the data

• Helps you understand why the data is used or what rules apply

• Examples:

  • “Birth date must be at least 16 years before today”

  • “This ID number is used to uniquely identify each employee”

  • “This status field indicates whether the employee is active or not”

Is the following a property metadata or contextual metadata?

• 30 character field limit for a name

Property Metadata

Business Analyst

Analyze business situation and establish requirements

• – like business analysts, but also have technical expertise for overall information systems

• Database analysts and data modelers – analysts who focus on database

• Users – the “customers” communicate their needs to analysts

• Programmers – coders of the programs that interact with the database

Systems analysts

• like business analysts, but also have technical expertise for overall information systems

Database analysts and Data Modellers

– analysts who focus on database

Users

– the “customers” communicate their needs to analysts

Programmers

– coders of the programs that interact with the database

Database architects

– establish standards for data in business units

Data administrators

• – responsible for existing databases, ensuring data integrity and consistency

Project managers

• – oversee the projects, manage the personnel

Describe the 3 most common types of databases.

1. Personal DBs: Single user, MB-sized

2. Departmental: Small groups, GB-sized

3. Enterprise DBs: Full-scale systems, TB/PB-sized

Provide a brief evolution of databases.

• 1960s–70s: File systems ➡ Relational theory by E.F. Codd

• 1980s: SQL & relational DBs boom

• 1990s: Object-Oriented models emerge

• 2000s+: NoSQL for Big Data (e.g., MongoDB)

File-Based Systems v.s. Database Systems

Aspect	File-Based Systems	Database Systems
1. Data Duplication	✖ Redundant data – multiple programs store the same data in different files (e.g., customer info in billing, shipping, and orders files)	✔ Centralized storage – all data is stored in one place and shared across systems, minimizing redundancy
2. Data Sharing	✖ Difficult to share data – each app controls its own files; no central control	✔ Shared access – authorized users and programs can access a common database with rules for permissions
3. Maintenance (Tight Coupling)	✖ Program-data dependence – if file format changes, every program using it must be updated too	✔ Program-data independence – data structure changes don’t require changing the application code (thanks to DBMS)
4. Updates and Flexibility	✖ Difficult to update or scale – changes require modifying multiple programs and file formats	✔ Easier to update and maintain – changes made centrally and supported by tools in the DBMS
5. Data Integrity & Consistency	✖ Low integrity – duplicate data may become inconsistent if not updated everywhere	✔ High integrity – controlled redundancy and validation rules ensure consistency
6. Security	✖ Weak security – file-level permissions only, hard to enforce complex access rules	✔ Strong security – DBMS allows fine-grained access control (user views, roles, encryption)
7. Standards & Rules	✖ No enforcement – different programs may use different naming conventions and rules	✔ Standardized – consistent data definitions, naming, and rules enforced via DBMS
8. Development Time	✖ Slower development – programmers must manually manage file structures, updates, and formats	✔ Faster development – databases use tools, models (ERD), and automation for development
9. Scalability	✖ Not scalable – limited to small applications; becomes messy as systems grow	✔ Highly scalable – designed for growth in size, users, and complexity
10. Support for Complex Applications	✖ Poor support – lacks features for relationships, transactions, or analytics	✔ Full support – DBMS enables transactions, indexing, querying (e.g., SQL), reporting, and analytics