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DIMENSIONAL
MODELING
Structuring Data for Better
Reporting and Analysis
Sajjad Zaheer
21 Aug 2014, Folio3
@folio_3 www.folio3.com Copyright 2015
1. Getting into the Context
@folio_3 www.folio3.com Copyright 2015
Online Transaction Processing
• Core database
• Usually ER model
• For transactions and routine tasks
@folio_3 www.folio3.com Copyright 2015
Data about data, i.e information about data tables
in OLTP System.
@folio_3 www.folio3.com Copyright 2015
Extract from source (OLTP)
Transform, according to requirement
Load into Data Warehouse
@folio_3 www.folio3.com Copyright 2015
• For effective querying, analysis and decision-
making
• OLAP (Online Analytical Processing) Design
• Subject-oriented, Integrated, Time-varying, non-
volatile collection of data
@folio_3 www.folio3.com Copyright 2015
• Access layer of data warehouse
• Subset of data ware house
• Oriented to specific business unit or department
E.g. marketing
• Is not another physical entity
@folio_3 www.folio3.com Copyright 2015
To analyze multidimensional data interactively
from multiple perspectives
@folio_3 www.folio3.com Copyright 2015
• Computational process of discovering patterns in
large data sets.
• To extract information and transform it into an
understandable structure for further use.
@folio_3 www.folio3.com Copyright 2015
Creation and study of the visual representation
of data E.g. scatter plot, bar chart.
@folio_3 www.folio3.com Copyright 2015
Retrieve and present a subset of data for a
particular purpose
@folio_3 www.folio3.com Copyright 2015
Data Information Knowledge
Dimensional
Modeling (OLTP to
OLAP Structure)
@folio_3 www.folio3.com Copyright 2015
Dimensional Modeling
@folio_3 www.folio3.com Copyright 2015
@folio_3 www.folio3.com Copyright 2015
@folio_3 www.folio3.com Copyright 2015
Terminology
Dimensions
The time independent,
textual and descriptive
attributes by which users
describe objects.
Who, where, what, how,
when.
Angles/Dimensions with
which a data can be
viewed.
E.g. Product category,
Date-time of a transaction.
Facts
Business Measurements
(Quantified). E.g. quantity,
amount, cost, taxes.
Things that can be
summed or aggregated.
E.g. sales of a product.
Built from the lowest level
of detail (grain)
Data at consideration
Time dependent
@folio_3 www.folio3.com Copyright 2015
Dimensional Modeling Process
 Sub-setting
 De-normalization
i.e. collapsing hierarchies of dimensions by de-
normalization to 2NF
 Summarization
i.e. Summation of Facts
@folio_3 www.folio3.com Copyright 2015
Modeling Design Steps
1. Identify the Business Process
Source of “measurements”
2. Identify the Grain
What does 1 row in the fact table represent or mean?
3. Identify the Dimensions
Descriptive context, true to the grain
4. Identify the Facts
Numeric additive measurements, true to the grain
@folio_3 www.folio3.com Copyright 2015
Design Steps - Example
@folio_3 www.folio3.com Copyright 2015
Case Study: Users Points System
 Consider a System simply explained as:
It has users and groups of users.
Every user can perform certain actions like
message, comment, meeting etc.
For every action user get some points that are
also added to the points of user groups that this
user belongs.
The system also has many other features that are
not relevant to points.
Let’s assume the system has over 100 tables to
store various things.
@folio_3 www.folio3.com Copyright 2015
Step 1: Identify the Business Process
 Question 1: Do we start doing dimensional
modeling to all the 100 tables in the system?
Answer: No
 Question 2: So which tables should be
selected?
Answer: The tables that are relevant to the
business requirements.
@folio_3 www.folio3.com Copyright 2015
Business Requirements
 Three types of points are required for
reporting:
1. Per month points
2. Average lifetime points at end of each month
 For:
1. Individual users
2. User groups
3. Individual users per action
4. User groups per action
@folio_3 www.folio3.com Copyright 2015
Step 2: Identify the Grain
Analyzing the business requirements, following grains
are identified.
1. Points per individual per month
2. Points per user group per month
3. Points per user per action per month
4. Average Lifetime Points per individual per month
5. Average Lifetime Points per user group per month
6. Average Lifetime Points per user per action per
month
“Grain = What does 1 row in the fact table represent”
@folio_3 www.folio3.com Copyright 2015
Step 3: Identify the Dimensions
Simply speaking, the content after ‘per’ in
grain are the dimensions. They are found to
be:
1. Date (granularity: month)
2. Uses
3. User groups
4. Actions
“Dimension: descriptive context true to grain”
@folio_3 www.folio3.com Copyright 2015
Step 4: Identify the Facts
4 Facts are identified
1. User Points
2. User Lifetime Average Points
3. User Group Points
4. User Group Lifetime Average Points
“Facts: Numeric additive measures true to grain”
@folio_3 www.folio3.com Copyright 2015
Tables Schema
Once Grain, facts and dimensions are identified, table
schema is to be formed using these.
Please note:
 It is not necessary to keep all facts in different tables.
 They can be part of single table.
 Alternatively, there can be multiple fact tables for a
single fact as per its relationship with dimensions.
 Every dimension will be in different table and each
dimension can be connected to many fact tables.
@folio_3 www.folio3.com Copyright 2015
Tables Schema
 Tables Schema should be the translation of
the Grain defined in step 2
@folio_3 www.folio3.com Copyright 2015
Star Schema – fact_points_user
Grains covered:
1. Points per individual per month
2. Average lifetime points per individual per month
@folio_3 www.folio3.com Copyright 2015
Star Schema – fact_points_user_action
Grains covered:
1. Points per individual per action
per month
2. Average lifetime points per
individual per action per month
@folio_3 www.folio3.com Copyright 2015
Star Schema – fact_points_group
Grains covered:
1. Points per user group per month
2. Average lifetime points per user group per month
@folio_3 www.folio3.com Copyright 2015
Star Schema for User Points Grains
Grains covered:
1. Points per user group per action
per month
2. Average lifetime points per user
group per action per month
@folio_3 www.folio3.com Copyright 2015
Example Query
SELECT fp.*, du.username, da.action_name
FROM fact_points_user_action fp
JOIN dim_user du ON fp.dim_user_id = du.dim_user_id
JOIN dim_date dd ON fp.dim_date_id = dd.dim_date_id
JOIN dim_action da ON fp.dim_action_id = da.dim_action_id
WHERE dd.month = 3 AND dd.year = 2014;
@folio_3 www.folio3.com Copyright 2015
Data Transformation: OLTP to OLAP
@folio_3 www.folio3.com Copyright 2015
Data Transformation
 Once the OLAP Schema has been designed, data
is to be moved from the ERD (OLTP) DB to this
new OLAP DB.
 This can be achieved using dedicated scripts or
cron jobs.
 One simple example for the elaborated case is to
set up a cron that gets executed at every month
end and move relevant data from ERD DB to
OLAP DB after calculations (if any).
@folio_3 www.folio3.com Copyright 2015
Conclusion
 Dimensional Modeling helps to keep data in a
form that is relevant and quickly accessible for
reporting and analysis.
@folio_3 www.folio3.com Copyright 2015

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Dimensional Modeling Basic Concept with Example

  • 1. DIMENSIONAL MODELING Structuring Data for Better Reporting and Analysis Sajjad Zaheer 21 Aug 2014, Folio3 @folio_3 www.folio3.com Copyright 2015
  • 2. 1. Getting into the Context @folio_3 www.folio3.com Copyright 2015
  • 3. Online Transaction Processing • Core database • Usually ER model • For transactions and routine tasks @folio_3 www.folio3.com Copyright 2015
  • 4. Data about data, i.e information about data tables in OLTP System. @folio_3 www.folio3.com Copyright 2015
  • 5. Extract from source (OLTP) Transform, according to requirement Load into Data Warehouse @folio_3 www.folio3.com Copyright 2015
  • 6. • For effective querying, analysis and decision- making • OLAP (Online Analytical Processing) Design • Subject-oriented, Integrated, Time-varying, non- volatile collection of data @folio_3 www.folio3.com Copyright 2015
  • 7. • Access layer of data warehouse • Subset of data ware house • Oriented to specific business unit or department E.g. marketing • Is not another physical entity @folio_3 www.folio3.com Copyright 2015
  • 8. To analyze multidimensional data interactively from multiple perspectives @folio_3 www.folio3.com Copyright 2015
  • 9. • Computational process of discovering patterns in large data sets. • To extract information and transform it into an understandable structure for further use. @folio_3 www.folio3.com Copyright 2015
  • 10. Creation and study of the visual representation of data E.g. scatter plot, bar chart. @folio_3 www.folio3.com Copyright 2015
  • 11. Retrieve and present a subset of data for a particular purpose @folio_3 www.folio3.com Copyright 2015
  • 12. Data Information Knowledge Dimensional Modeling (OLTP to OLAP Structure) @folio_3 www.folio3.com Copyright 2015
  • 16. Terminology Dimensions The time independent, textual and descriptive attributes by which users describe objects. Who, where, what, how, when. Angles/Dimensions with which a data can be viewed. E.g. Product category, Date-time of a transaction. Facts Business Measurements (Quantified). E.g. quantity, amount, cost, taxes. Things that can be summed or aggregated. E.g. sales of a product. Built from the lowest level of detail (grain) Data at consideration Time dependent @folio_3 www.folio3.com Copyright 2015
  • 17. Dimensional Modeling Process  Sub-setting  De-normalization i.e. collapsing hierarchies of dimensions by de- normalization to 2NF  Summarization i.e. Summation of Facts @folio_3 www.folio3.com Copyright 2015
  • 18. Modeling Design Steps 1. Identify the Business Process Source of “measurements” 2. Identify the Grain What does 1 row in the fact table represent or mean? 3. Identify the Dimensions Descriptive context, true to the grain 4. Identify the Facts Numeric additive measurements, true to the grain @folio_3 www.folio3.com Copyright 2015
  • 19. Design Steps - Example @folio_3 www.folio3.com Copyright 2015
  • 20. Case Study: Users Points System  Consider a System simply explained as: It has users and groups of users. Every user can perform certain actions like message, comment, meeting etc. For every action user get some points that are also added to the points of user groups that this user belongs. The system also has many other features that are not relevant to points. Let’s assume the system has over 100 tables to store various things. @folio_3 www.folio3.com Copyright 2015
  • 21. Step 1: Identify the Business Process  Question 1: Do we start doing dimensional modeling to all the 100 tables in the system? Answer: No  Question 2: So which tables should be selected? Answer: The tables that are relevant to the business requirements. @folio_3 www.folio3.com Copyright 2015
  • 22. Business Requirements  Three types of points are required for reporting: 1. Per month points 2. Average lifetime points at end of each month  For: 1. Individual users 2. User groups 3. Individual users per action 4. User groups per action @folio_3 www.folio3.com Copyright 2015
  • 23. Step 2: Identify the Grain Analyzing the business requirements, following grains are identified. 1. Points per individual per month 2. Points per user group per month 3. Points per user per action per month 4. Average Lifetime Points per individual per month 5. Average Lifetime Points per user group per month 6. Average Lifetime Points per user per action per month “Grain = What does 1 row in the fact table represent” @folio_3 www.folio3.com Copyright 2015
  • 24. Step 3: Identify the Dimensions Simply speaking, the content after ‘per’ in grain are the dimensions. They are found to be: 1. Date (granularity: month) 2. Uses 3. User groups 4. Actions “Dimension: descriptive context true to grain” @folio_3 www.folio3.com Copyright 2015
  • 25. Step 4: Identify the Facts 4 Facts are identified 1. User Points 2. User Lifetime Average Points 3. User Group Points 4. User Group Lifetime Average Points “Facts: Numeric additive measures true to grain” @folio_3 www.folio3.com Copyright 2015
  • 26. Tables Schema Once Grain, facts and dimensions are identified, table schema is to be formed using these. Please note:  It is not necessary to keep all facts in different tables.  They can be part of single table.  Alternatively, there can be multiple fact tables for a single fact as per its relationship with dimensions.  Every dimension will be in different table and each dimension can be connected to many fact tables. @folio_3 www.folio3.com Copyright 2015
  • 27. Tables Schema  Tables Schema should be the translation of the Grain defined in step 2 @folio_3 www.folio3.com Copyright 2015
  • 28. Star Schema – fact_points_user Grains covered: 1. Points per individual per month 2. Average lifetime points per individual per month @folio_3 www.folio3.com Copyright 2015
  • 29. Star Schema – fact_points_user_action Grains covered: 1. Points per individual per action per month 2. Average lifetime points per individual per action per month @folio_3 www.folio3.com Copyright 2015
  • 30. Star Schema – fact_points_group Grains covered: 1. Points per user group per month 2. Average lifetime points per user group per month @folio_3 www.folio3.com Copyright 2015
  • 31. Star Schema for User Points Grains Grains covered: 1. Points per user group per action per month 2. Average lifetime points per user group per action per month @folio_3 www.folio3.com Copyright 2015
  • 32. Example Query SELECT fp.*, du.username, da.action_name FROM fact_points_user_action fp JOIN dim_user du ON fp.dim_user_id = du.dim_user_id JOIN dim_date dd ON fp.dim_date_id = dd.dim_date_id JOIN dim_action da ON fp.dim_action_id = da.dim_action_id WHERE dd.month = 3 AND dd.year = 2014; @folio_3 www.folio3.com Copyright 2015
  • 33. Data Transformation: OLTP to OLAP @folio_3 www.folio3.com Copyright 2015
  • 34. Data Transformation  Once the OLAP Schema has been designed, data is to be moved from the ERD (OLTP) DB to this new OLAP DB.  This can be achieved using dedicated scripts or cron jobs.  One simple example for the elaborated case is to set up a cron that gets executed at every month end and move relevant data from ERD DB to OLAP DB after calculations (if any). @folio_3 www.folio3.com Copyright 2015
  • 35. Conclusion  Dimensional Modeling helps to keep data in a form that is relevant and quickly accessible for reporting and analysis. @folio_3 www.folio3.com Copyright 2015

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