dimensional modeling business intelligence solutions

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Dimensional Modeling Business Intelligence Solutions

Post on 19-Dec-2015

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Dimensional Modeling

Business Intelligence Solutions

Definition

According to Dr. Kimball, Dimensional Modeling is a design technique for databases intended to support end-user queries in a data warehouse.

Dimensional Design VS Normalized Design

Dimensional Modeling

Normalized Modeling

Reporting and Analysis

Operational Inserts and Updates

Data Warehouse

Transactional Data Source(s)

Transactional System and Normalized Design

Enable consistent and fast writing

Insert or update touch the database in one place

Too complicated for data warehouse queries and performance issue

3NF (third-normal-form)

Normalized Design

Data Warehouse and Dimensional Design

Star schema: when a fact table is joined to a set of dimension tables

Simplicity

Symmetry

Performance benefits

Dimensional Design

Dimension Model Elements

Fact Table

Dimension Tables

Dimension Model Elements: Fact Table

Stores numerical performance measurements

Contains:o Dimension keyso Facts: business measures

Grain: defined by a list of dimensions

3 categories of fact table grains: o Transactiono Periodic snapshoto Accumulative snapshot

Dimensions

Facts

Dimension Model Elements: Fact Table

Looks ‘thin’ in shape

Primary key: subset of the foreign keys

Expresses a many-to-many relationships

Stores textual descriptors

Contains: attributes

Highly denormalized

Represent hierarchical relationships

Looks ‘fat’ in shape

Dimension Model Elements: Dimension Tables

Dimension Model: bring Facts and Dimensions together

Surrogate keys: sequentially assigned integers

Benefits:

o Environment buffer

o Allow data integration

o Support SCD (slow changing dimension)

Handling Changes: SCD

Type 1 SCD: Overwrite the value

Handling Changes: SCD

Type 2 SCD: Add a Dimension Row

Handling Changes: SCD

Type 3 SCD: Add a Dimension Column

Dimensional Modeling Design Process

Choose the Business Process

Declare the Grain

Identify the Dimensions

Identify the Facts

Easy to understand

Optimized for performance

Historical tracking of information

Extensible to accommodate changes

Summary

Questions?