DATA MODELING SCHEMAS
Schema is a logical description of the entire database. It includes
the name and description of records of all record types including
all associated data-items and aggregates. Much like a database,
a data warehouse also requires to maintain a schema. A
database uses relational model, while a data warehouse uses
Star, Snowflake, and Fact Constellation schema. In this chapter,
we will discuss the schemas used in a data warehouse.
Star Schema
Each dimension in a star schema is represented with only
one-dimension table.
This dimension table contains the set of attributes.
The following diagram shows the sales data of a company
with respect to the four dimensions, namely time, item,
branch, and location.
There is a fact table at the center. It contains the keys to
each of four dimensions.
The fact table also contains the attributes, namely dollars
sold and units sold.
Note − Each dimension has only one-dimension table and each
table holds a set of attributes. For example, the location
dimension table contains the attribute set {location_key, street,
city, province_or_state,country}. This constraint may cause
data redundancy. For example, "Vancouver" and "Victoria" both
the cities are in the Canadian province of British Columbia. The
entries for such cities may cause data redundancy along the
attributes province_or_state and country.
Snowflake Schema
Some dimension tables in the Snowflake schema are
normalized.
The normalization splits up the data into additional tables.
Unlike Star schema, the dimensions table in a snowflake
schema are normalized. For example, the item dimension
table in star schema is normalized and split into two
dimension tables, namely item and supplier table.
Now the item dimension table contains the attributes
item_key, item_name, type, brand, and supplier-key.
The supplier key is linked to the supplier dimension table.
The supplier dimension table contains the attributes
supplier_key and supplier_type.
Note − Due to normalization in the Snowflake schema, the
redundancy is reduced and therefore, it becomes easy to
maintain and the save storage space.
Fact Constellation Schema
A fact constellation has multiple fact tables. It is also known
as galaxy schema.
The following diagram shows two fact tables, namely sales
and shipping.
The sales fact table is same as that in the star schema.
The shipping fact table has the five dimensions, namely
item_key, time_key, shipper_key, from_location,
to_location.
The shipping fact table also contains two measures, namely
dollars sold and units sold.
It is also possible to share dimension tables between fact
tables. For example, time, item, and location dimension
tables are shared between the sales and shipping fact table.
Schema Definition
Multidimensional schema is defined using Data Mining Query
Language (DMQL). The two primitives, cube definition and
dimension definition, can be used for defining the data
warehouses and data marts.
Syntax for Cube Definition
define cube < cube_name > [ < dimension-list > }: < measure_list >
Syntax for Dimension Definition
define dimension < dimension_name > as ( < attribute_or_dimension_list > )
Star Schema Definition
The star schema that we have discussed can be defined using
Data Mining Query Language (DMQL) as follows −
define cube sales star [time, item, branch, location]:
dollars sold = sum(sales in dollars), units sold = count(*)
define dimension time as (time key, day, day of week, month, quarter, year)
define dimension item as (item key, item name, brand, type, supplier type)
define dimension branch as (branch key, branch name, branch type)
define dimension location as (location key, street, city, province or state, country)
Snowflake Schema Definition
Snowflake schema can be defined using DMQL as follows −
define cube sales snowflake [time, item, branch, location]:
dollars sold = sum(sales in dollars), units sold = count(*)
define dimension time as (time key, day, day of week, month, quarter, year)
define dimension item as (item key, item name, brand, type, supplier (supplier
key, supplier type))
define dimension branch as (branch key, branch name, branch type)
define dimension location as (location key, street, city (city key, city, province or
state, country))
Fact Constellation Schema Definition
Fact constellation schema can be defined using DMQL as follows
−
define cube sales [time, item, branch, location]:
dollars sold = sum(sales in dollars), units sold = count(*)
define dimension time as (time key, day, day of week, month, quarter, year)
define dimension item as (item key, item name, brand, type, supplier type)
define dimension branch as (branch key, branch name, branch type)
define dimension location as (location key, street, city, province or state,country)
define cube shipping [time, item, shipper, from location, to location]:
dollars cost = sum(cost in dollars), units shipped = count(*)
define dimension time as time in cube sales
define dimension item as item in cube sales
define dimension shipper as (shipper key, shipper name, location as location in
cube sales, shipper type)
define dimension from location as location in cube sales
define dimension to location as location in cube sales
