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1. Introduction to SQL
Server Dimensional
Modeling
SQL Server Dimensional Modeling is a data warehousing technique used
to organize and analyze complex business data. It focuses on creating a
logical data structure that enables efficient reporting and decision-making.

2. Fact Tables and Dimension Tables
Fact Tables
Fact tables contain the core
business metrics or
measures, such as sales,
revenue, or production data.
Dimension Tables
Dimension tables provide
contextual information about
the facts, such as product
details, customer information,
or time periods.
Relationship
Fact tables link to dimension
tables through foreign key
relationships, forming a
dimensional data model.

3. Star Schema and Snowflake Schema
1
Star Schema
A simple dimensional model with a
central fact table surrounded by
dimension tables. 2 Snowflake Schema
A more complex model where
dimension tables are further
normalized into additional tables.
3
Tradeoffs
Star schema is easier to understand
and query, while snowflake schema
can be more efficient for certain use
cases.

4. Slowly Changing Dimensions
1 Type 1
Overwrite existing data, no history kept.
2 Type 2
Add new rows to track historical changes.
3 Type 3
Add additional columns to store previous
values.
4 Type 4
Maintain a mini-dimension to store
historical changes.

5. Degenerate Dimensions
What are they?
Degenerate dimensions are dimensions that
exist within the fact table itself, rather than as
a separate dimension table.
Why use them?
Degenerate dimensions can simplify the data
model and improve query performance by
reducing the number of joins.
Examples
Common examples include invoice numbers,
order numbers, and batch IDs.
Considerations
Carefully evaluate when to use degenerate
dimensions to maintain a balance between
simplicity and flexibility.

6. Junk Dimensions
Miscellaneous
Junk dimensions
store low-cardinality
attributes that don't fit
well into other
dimensions.
Efficient
Junk dimensions can
improve query
performance by
consolidating these
small attributes.
Flexible
Junk dimensions
allow the data model
to evolve without
significantly changing
the fact table
structure.
Simplify
Junk dimensions help
keep the data model
clean and organized.

7. Aggregate Fact Tables
Raw Data
Fact tables contain detailed, granular business data.
Aggregation
Aggregate fact tables pre-calculate and store summarized data.
Performance
Aggregate fact tables enable faster reporting and analytics on large datasets.

8. Best Practices for Dimensional Modeling
1 Start with the Business
Understand the business requirements and
user needs before designing the data
model.
2 Keep it Simple
Strive for a clean, intuitive data structure
that is easy to understand and use.
3 Be Flexible
Design the model to accommodate future
growth and changes in the business.
4 Focus on Performance
Optimize the model for efficient querying
and analysis, using techniques like
aggregation.
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