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Lesson 2 Database Life Cycle
Objective Describe where we are in the database life cycle.

Database Life Cycle

DBLC consists of five Stages

As mentioned in other areas of this site, the database life cycle (DBLC) 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).

DBLC consisting of 1. Requirements Analysis, 2. Logical Design, 3. Physical Design, 4. Implementation, 5. Modification, Maintenance

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.
The next lesson introduces tables, describing their role in relational theory.
[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.