The Database Life Cycle consists of Five Stages

As mentioned in other areas of this site, the (DBLC) database life cycle consists of five stages:

1. Requirements Analysis
2. Logical Design
3. Physical Design
4. Implementation
5. Monitoring, Modification, and Maintenance

In the first stage of the DBLC, Requirements Analysis, you determine what data your database will need to hold and what tasks it will need to perform to meet the needs of your users. You identify

1. business objects: (things in the business environment that need to be represented in the database) and
2. business rules: (restrictions on how users perceive and use data).

In the first phase of the Logical Design stage you convert the business objects and their characteristics into entities^[1] and attributes [2]. You then create an entity-relationship (ER) diagram^[3] that graphically represents as base tables the entities, their attributes, and the relationships^[4] that exist between them. The first course in this series covered Requirements Analysis and the Logical Design stage through the creation of the ER diagram. The next phase in the Logical Design stage of the DBLC is to normalize^[5] the base tables you created in the ER diagram, so you can store and retrieve information in the database efficiently.

• Uses of the term Database Table:
  The term table takes on a dual meaning when we talk about database design. On the one hand, the elements comprising an ER diagram are referred to as base tables. These are represented as
  1. entities and
  2. attributes, which represent the business objects
  
  to be included in the database, and are the sort of tables during the process of relational database design. They come into play during the Logical Design phase of the (DBLC) database lifye cycle. We also use the term table to refer to the relational constructs comprising the implementation of the database. These tables will be described in greater detail later in this module.

In the context of Entity-Relationship Diagrams (ERDs), base tables are:
📌 Definition
Base tables represent the actual database tables that are created from entities and relationships in an ERD. They are permanent, physical tables stored in the database and contain real, persistent data.
🧱 Role in ER Diagrams
In an ERD:

• Entities (like Customer, Order, Product) are mapped to base tables during database design.
• Each attribute of an entity becomes a column in the corresponding base table.
• Primary keys and foreign keys are enforced as constraints in these base tables.

🗂️ Types of Tables in ER to Relational Mapping

Table Type	Description
Base Table	Stores actual data derived from entities or relationships
View (Derived Table)	A virtual table generated by a query; not stored permanently
Intersection Table	A base table used for resolving many-to-many relationships
Lookup Table	A base table used for validation/reference values (e.g., Country, Status)

🧭 Example: Consider this ERD fragment:

• Entity: Employee
  • Attributes: EmployeeID, Name, HireDate
• Relationship: WorksIn
  • Between Employee and Department

This gets mapped to:

CREATE TABLE Employee (
  EmployeeID INT PRIMARY KEY,
  Name VARCHAR(100),
  HireDate DATE
);

Here, `Employee` becomes a base table. If the relationship `WorksIn` is many-to-many, a base table called `EmployeeDepartment` may also be created.
✅ Summary

• Base tables store data and originate from entities and relationships in an ERD.
• They are the foundation of a relational schema.
• ERDs help visualize base tables before physical database creation.

In the context of relational database design, a database table is the core structural component for storing and organizing data. Conceptually, a table resembles a matrix or spreadsheet, where:

• Each row (also called a record or tuple) represents a unique instance of an entity.
• Each column (or attribute) defines a specific type of data, with a predefined data type and constraints.

🧱 Core Functions

1. Data Storage: A table stores persistent data representing real-world entities like customers, products, or transactions.
2. Data Organization: Tables organize data into structured formats to simplify processing and reduce redundancy.
3. Querying and Manipulation: Tables are designed to support efficient SQL operations like SELECT, INSERT, UPDATE, and DELETE.
4. Relational Integrity: Tables are linked through primary keys and foreign keys, allowing for complex relationships across entities.

🛡️ Constraints and Data Integrity
Database tables are governed by constraints that ensure consistency and accuracy:

• Primary Key: Uniquely identifies each record.
• Foreign Key: Links one table to another, enforcing referential integrity.
• NOT NULL: Ensures that a column cannot have missing values.
• UNIQUE: Prevents duplicate values in a column.
• CHECK: Validates that values meet a specified condition.

These constraints collectively ensure data integrity, validation, and consistency across operations.
🔄 Relational Model Integration
Tables form the foundation of the relational model, where: * Each table represents a relation. * Relationships between tables enable data normalization, reducing duplication and improving scalability. * Set-based operations (e.g., joins) allow combining and filtering data across multiple tables efficiently.
✅ Summary
A database table is more than a container for data—it is a relational construct designed to support:

• Efficient data storage
• Logical data access patterns
• Robust integrity constraints
• Complex entity relationships

This makes database tables the building blocks of any well-structured relational database system.

The database life cycle is a set of stages that a database goes through, from initial planning and design to retirement and replacement. While the stages of the database life cycle are generally the same across different database management systems (DBMS), the specific tools, methods, and processes used in each stage may differ depending on the DBMS. The stages of the database life cycle include:

1. Planning and Requirements Analysis: This stage involves defining the purpose of the database, identifying the requirements of the users and stakeholders, and creating a conceptual data model.
2. Design: In this stage, the conceptual data model is refined into a logical data model, which is used to create the physical schema of the database.
3. Implementation: The physical schema is implemented using the DBMS, including creating tables, indexes, and constraints, and loading data into the database.
4. Testing: The database is tested to ensure that it meets the requirements and performs as expected.
5. Deployment: The database is deployed to production and made available to users.
6. Maintenance and Evolution: The database is maintained over time, including performing backups, tuning performance, and making changes to the schema as needed.
7. Retirement and Replacement: Eventually, the database may reach the end of its useful life and need to be retired and replaced with a new database.

While the stages of the database life cycle are generally the same across different DBMS, the specific tools, methods, and processes used in each stage may differ depending on the DBMS. For example, some DBMS may have different options for creating the physical schema or for tuning performance. Additionally, some DBMS may offer additional tools or features for managing the database over its life cycle, such as automated backup and recovery or schema migration tools.

[1]Entity: A single stand-alone unit or a business object about which data are stored in a database; usually synonymous with a database table.

[2]Attribute: A characteristic of an entity; data that identifies or describes an entity.
Usually represented as a column in a table, attributes store data values.

[3]entity-relationship (ER) diagram: A diagram used during the design phase of database development to illustrate the organization of and relationships between data during database design.

[4]relationship: If the same attribute occurs in more than one table, a relationship exists between those two tables.

[5]normalize: To break up large tables into smaller, more efficient tables without losing any information.