Orderly Approach for DWH Construction

Orderly Approach for DWH construction

2/23/2012 3.Planning & Project management/D.S.Jagli 1

Topics to be covered
1. How is it different?
2. Life-cycle approach
3. The Development Phases
4. Dimensional Analysis
5. Dimensional Modeling
i. Star Schema
ii. Snowflake Scheme


3.Planning & Project management
 Reasons for DWH projects failure
1. Improper planning
2. Inadequate project management
 Planning for Data ware house is necessary.
I. Key issues needs to be planned
1. Value and expectation
2. Risk assessment
3. Top-down or bottom –up
4. Build or Buy
5. Single vender or best of breed
II. Business requirement ,not technology
III. Top management support
IV. Justification

3.Planning & Project management
 Example for DWH Project
1. Introduction
Outline for overall plan 2. Mission statement
3. Scope
4. Goals& objectives
5. Key issues & Options
6. Value & expectations
7. Justification
8. Executive sponsorship
9. Implementation
Strategy
10. Tentative schedule
11. Project authorization

3.1 How is it different?
 DWH Project Different from OLTP System Project
 DWH Distinguish features and Challenges for Project
Management
1. Data Acquisition
2. Data Storage
3. Information Delivery


3.2 The life-cycle Approach
Fig: DW functional components and SDLC


DWH Project Plan: Sample outline


3.3 DWH
Development
Phases


3.3 DWH Development Phases
1) Project plan
2) Requirements definition
3) Design
4) Construction
5) Deployment
6) Growth and maintenance

 Interleaved within the design and construction phases are the three
tracks along with the definition of the architecture and the
establishment of the infrastructure.


3.4 Dimensional Analysis

 A data warehouse is an information delivery system.

 It is not about technology, but about solving users’ problems.

 It is providing strategic information to the user.

 In the phase of defining requirements, need to concentrate on
what information the users need, not on how we are going to
provide the required information.

Dimensional Nature of DWH
1. Usage of Information Unpredictable
 In providing information about the requirements for an operational
system, the users are able to give you precise details of the required
functions, information content, and usage patterns.

2. Dimensional Nature of Business Data
 Even though the users cannot fully describe what they want in a data
warehouse, they can provide you with very important insights into
how they think about the business.


Managers think in business dimensions : example


Dimensional Nature of Business Data


Examples of Business Dimensions


INFORMATION PACKAGES—A NEW
CONCEPT
 A novel idea is introduced for determining and recording information
requirements for a data warehouse.

 This concept helps us to give
• A concrete form to the various insights, nebulous thoughts,
opinions expressed during the process of collecting requirements.

 The information packages, put together while collecting requirements, are
very useful for taking the development of the data warehouse to the next
phases.


Requirements Not Fully Determinate
 Information packages enable us to:
1. Define the common subject areas
2. Design key business metrics
3. Decide how data must be presented
4. Determine how users will aggregate or roll up
5. Decide the data quantity for user analysis or query
6. Decide how data will be accessed
7. Establish data granularity
8. Estimate data warehouse size
9. Determine the frequency for data refreshing
10. Determine how information must be packaged


An information package.


Business Dimensions
 Business dimensions form the underlying basis of the new
methodology for requirements definition.

 Data must be stored to provide for the business dimensions.

 The business dimensions and their hierarchical levels form the
basis for all further phases.


Dimension Hierarchies/Categories
 Examples:
1) Product: Model name, model year, package styling, product line, product category,
exterior color, interior color, first model year

2) Dealer: Dealer name, city, state, single brand flag, date first operation

3) Customer demographics: Age, gender, income range, marital status, household
size, vehicles owned, home value, own or rent

4) Payment method: Finance type, term in months, interest rate, agent

5) Time: Date, month, quarter, year, day of week, day of month, season, holiday flag


Key Business Metrics or Facts
 The numbers , users analyze are the measurements or metrics
that measure the success of their departments.

 These are the facts that indicate to the users how their
departments are doing in fulfilling their departmental
objectives.


Example: automobile sales
 The set of meaningful and useful metrics for analyzing
automobile sales is as follows:
 Actual sale price
 MSRP sale price
 Options price
 Full price
 Dealer add-ons
 Dealer credits
 Dealer invoice
 Amount of down payment
 Manufacturer proceeds
 Amount financed

Star Schema
Snowflake Scheme


FROM REQUIREMENTS TO DATA
DESIGN
1. The requirements definition completely drives the data design for the data
warehouse.

2. A group of data elements form a data structure.

3. Logical data design includes determination of the various data elements
,structures of data & establishing the relationships among the data
structures.

4. The information package diagrams form the basis for the logical data
design for the data warehouse.

5. The data design process results in a dimensional data model.


FROM REQUIREMENTS TO DATA DESIGN


Dimensional Modeling Basics: Formation of the automaker sales
fact table.


Formation of the automaker dimension tables.


Concept of Keys for Dimension table
Surrogate Keys
1. A surrogate key is the primary key for a dimension table and
is independent of any keys provided by source data systems.
2. Surrogate keys are created and maintained in the data
warehouse and should not encode any information about the
contents of records.
3. Automatically increasing integers make good surrogate keys.
4. The original key for each record is carried in the dimension
table but is not used as the primary key.
5. Surrogate keys provide the means to maintain data warehouse
information when dimensions change.

30

Concept of Keys for Dimension table
Business Keys
 Natural keys
 Will have a meaning and can be generated out of the data from source
system or can be used as is from source system field

31

The criteria for combining the tables into a
dimensional model.
1. The model should provide the best data access.
2. The whole model must be query-centric.
3. It must be optimized for queries and analyses.
4. The model must show that the dimension tables interact with
the fact table.
5. It should also be structured in such a way that every
dimension can interact equally with the fact table.
6. The model should allow drilling down or rolling up along
dimension hierarchies.


The Dimensional model :a STAR schema
 With these requirements, we find that a dimensional
model with the fact table in the middle and the dimension
tables arranged around the fact table satisfies the condition


Case study: STAR schema for automaker
sales.


E-R Modeling Versus Dimensional
Modeling
1. OLTP systems capture details of
events transactions 1. DW meant to answer questions on
overall process
2. OLTP systems focus on
individual events 2. DW focus is on how managers
view the business
3. An OLTP system is a window
into micro-level transactions 3. DW focus business trends
4. Picture at detail level necessary 4. Information is centered around a
to run the business business process
5. Suitable only for questions at 5. Answers show how the business
transaction level measures the process
6. Data consistency, non- 6. The measures to be studied in
redundancy, and efficient data many ways along several business
storage critical dimensions


E-R Modeling Versus Dimensional
Modeling
Dimensional modeling for the data
E-R modeling for OLTP warehouse.
systems


Star Schemas
 Data Modeling Technique to map multidimensional decision
support data into a relational database.

 Current Relational modeling techniques do not serve the needs
of advanced data requirements.
 4 Components
 Facts

 Dimensions

 Attributes

 Attribute Hierarchies


Facts
1. Numeric measurements (values) that represent a specific
business aspect or activity.

2. Stored in a fact table at the center of the star scheme.

3. Contains facts that are linked through their dimensions.

4. Updated periodically with data from operational databases


Dimensions
1. Qualifying characteristics that provide additional
perspectives to a given fact

 DSS data is almost always viewed in relation to other data

2. Dimensions are normally stored in dimension tables


Attributes
1. Dimension Tables contain Attributes.

2. Attributes are used to search, filter, or classify facts.

3. Dimensions provide descriptive characteristics about the facts through
their attributed.

4. Must define common business attributes that will be used to narrow a
search, group information, or describe dimensions. (ex.: Time / Location /
Product).

5. No mathematical limit to the number of dimensions (3-D makes it easy to
model).

Attribute Hierarchies
1. Provides a Top-Down data organization
 Aggregation
 Drill-down / Roll-Up data analysis
2. Attributes from different dimensions can be grouped to
form a hierarchy


Concept of Keys for Star schema
Surrogate Keys
 The surrogate keys are simply system-generated sequence numbers and is
independent of any keys provided by source data systems.
 They do not have any built-in meanings.
 Surrogate keys are created and maintained in the data warehouse and should not
encode any information about the contents of records;
 Automatically increasing integers make good surrogate keys.
 The original key for each record is carried in the dimension table but is not used
as the primary key.
Business Keys
Primary Keys
 Each row in a dimension table is identified by a unique value of an attribute
designated as the primary key of the dimension.
Foreign Keys
 Each dimension table is in a one-to-many relationship with the central fact table.
So the primary key of each dimension table must be a foreign key in the fact
table.
43

Star Schema for Sales
Dimension
Tables

Fact Table

Star Schema Representation
 Fact and Dimensions are represented by physical tables in the
data warehouse database.

 Fact tables are related to each dimension table in a Many to
One relationship (Primary/Foreign Key Relationships).

 Fact Table is related to many dimension tables
 The primary key of the fact table is a composite primary key
from the dimension tables.

 Each fact table is designed to answer a specific DSS question


Star Schema
 The fact table is always the larges table in the star schema.

 Each dimension record is related to thousand of fact records.

 Star Schema facilitated data retrieval functions.

 DBMS first searches the Dimension Tables before the larger
fact table


Star Schema : advantages
1. Easy to understand
2. Optimizes Navigation
3. Most Suitable for Query Processing


THE SNOWFLAKE SCHEMA
 Snowflaking” is a method of normalizing the dimension
tables in a STAR schema.


Sales: a simple STAR schema.


Product dimension: partially normalized


When to Snowflake
 The principle behind snowflaking is normalization of the
dimension tables by removing low cardinality attributes and
forming separate tables.

 In a similar manner, some situations provide opportunities to
separate out a set of attributes and form a subdimension.


Advantages and Disadvantages
 Advantages
 Small savings in storage space
 Normalized structures are easier to update and maintain
 Disadvantages
 Schema less intuitive and end-users are put off by the
complexity
 Ability to browse through the contents difficult
 Degraded query performance because of additional joins


???
Thank you

Orderly Approach for DWH Construction

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