6. Differences
between Star
schema and
Snowflake
Schema
models
Star Schema Snowflake Schema
Simplified design and easy to
understand
Complex design and a little difficult to
understand
Top-Down model Bottom-Up model
Required more space Less Space
The fact table is surrounded by
Dimension tables
The fact table is connected with
dimension tables and dimension tables
are connected with sub-dimension tables
in normalized
Low query complexity Complex query complexity
Not normalized, so there is a lesser
number of relationships and foreign
keys.
Normalized, so required number of
foreign keys and the well-defined
relationship between tables
Since not normalized, a High volume of
data redundancy
Since normalized, Low volume data
redundancy.
Fast query execution time
Low query execution time due to more
joins
One Dimensional Multidimensional
7. Fact Constellation is a schema for
representing multidimensional model. It is
a collection of multiple fact tables having
some common dimension tables
9. Kimball’s
Data Model
Techinique
Everything is fine with the star schema, as we understood that this is Flexible,
Extensible, and many more. But not answered business process and questions from DWH
The business process to a model –
Keeping customer model, product
model
ATOMIC model – Depth of data level
stored in the fact table in the concrete
ATOMIC model so, we can’t split further
for any analysis and not required too
Building fact tables – designing the
fact tables with a strong set of
dimensions with all possible categories.
Numeric facts – Identifying the most
important numeric measures use to
store at the fact table layer
10. Top Data Modeling Tools in Industry
Erwin
DBSchema
ER Studio
IBM Infosphere etc. …
Demo Logical Model Design …..
