Data Model

 
 

Data Model


Relational model

In Relational model data is normalized to 1st, 2nd or 3rd Normal form.

1s Normal form

1NF, each column must contain only a single value and each row must contain the same columns.

2nd Normal form

A relation must first fulfill the requirements to be in First Normal Form. Additionally, each nonkey attribute in the relation must be functionally dependent upon the primary key.

2NF attempts to reduce the amount of redundant data in a table by extracting it, placing it in new table(s) and creating relationships between those tables.

3rd Normal form

A relation must first fulfill the requirements to be in Second Normal Form. Additionally, all attributes that are not dependent upon the primary key must be eliminated

An example of a 2NF table that fails to meet the requirements of 3NF is:

Tournament Winners
Tournament Year Winner Winner Date of Birth
Indiana Invitational 1998 Al Fredrickson 21 July 1975
Cleveland Open 1999 Bob Albertson 28 September 1968
Des Moines Masters 1999 Al Fredrickson 21 July 1975
Indiana Invitational 1999 Chip Masterson 14 March 1977

In order to express the same facts without violating 3NF, it is necessary to split the table into two:


Tournament Winners
Tournament Year Winner
Indiana Invitational 1998 Al Fredrickson
Cleveland Open 1999 Bob Albertson
Des Moines Masters 1999 Al Fredrickson
Indiana Invitational 1999 Chip Masterson


Player Dates of Birth
Player Date of Birth
Chip Masterson 14 March 1977
Al Fredrickson 21 July 1975
Bob Albertson 28 September 1968

Dimensional model

In dimensional model data is denormalized. The typical design is that of a Star where their is a central fact table containing additive, measurable facts and this central fact table is in relationship with dimensional tables which generally contain text filters that normally occurs in the WHERE clause of a query.

The dimensional model is often implemented on top of the relational model using a star schema, consisting of one table containing the facts and surrounding tables containing the dimensions.

The Star schema consists of a few "fact tables" (possibly only one, justifying the name) referencing any number of "dimension tables".the star schema's dimensions are denormalized with each dimension being represented by a single table

The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions. In the snowflake schema, however, dimensions are normalized into multiple related tables When the dimensions of a snowflake schema are elaborate, having multiple levels of relationships, and where child tables have multiple parent tables.

what are the advantages and disadvantages of a star schema and snowflake schema.

In Star schema

In Star schema fact table is in normalized format and dimention table is in denormalized format

keeping queries simple and providing fast response time

In snowflake schema

In snowsflake both fact and dimention tables are in normalized format

will reduse the query performance because more joins is there

savings small storage spaces

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