The document elaborates on the concept of a star schema, a dimensional modeling approach for organizing data in a data warehouse that separates data into facts and dimensions. It provides insights into how to derive star schemas from entity-relationship models, with a focus on sales data management, including entities such as customers, products, and sales transactions. Key features and advantages of star schemas include structural simplicity, improved query performance, and easier data retrieval despite some limitations in analysis flexibility.

1. Data Warehouse: 20
Star Schema
Prof Neeraj Bhargava
Vaibhav Khanna
Department of Computer Science
School of Engineering and Systems Sciences
Maharshi Dayanand Saraswati University Ajmer

2. What is a Star Schema
• A schema is a collection of database objects,
including tables, views, indexes, and
synonyms.
• The term star schema is another way of
referring to a "dimensional modeling"
approach to defining your data model.
• Dimensional modeling creates multiple star
schemas, each based on a business process
such as sales tracking or shipments.

3. Star schema

4. Facts and Dimensions
• Star schemas divide data into facts and
dimensions.
• Facts are the measurements of some event such
as a sale and are typically numbers.
• Dimensions are the categories that we use to
identify facts, such as date, location, and product.
• The name "star schema" comes from the fact that
the diagrams of the schemas typically show a
central fact table with lines joining it to the
dimension tables, so the graphic impression is
similar to a star.

5. Deriving a Star Schema from ER Model
• •A fact table is formed for each kernel entity. The key
of the table is the combination of the keys of its
associated detail entities.
• •A dimension table is formed for each detail entity by
collapsing related dependent entities into it.
• •Where dependent relationships exist between kernel
entities, the child entity inherits all of the dimensions
(and key attributes) from the parent entity.
• •Numerical attributes within kernel or core entities
should be aggregated by key attributes.

6. Sales Kernel Star Schema

7. Sales Item Kernel Star Schema

8. Entities in a sales model
• Customer - the people and companies we sell to
• Product - the things we sell
• Sales - a transaction between our company and a
customer
• Sales planning - setting sales goals and
procedures for the coming period
• Sales plan - a statement of goals for sales and the
steps to achieve those goals Date of sale - the
date of a sales transaction

9. Sales Entities
• Distribution Channel - the means by which the
product was sold to the customer
• Salesperson - the person who sold the product
to the customer
• Competitor - the other companies who offer
competing products
• Sales Office - a location where a group of
salespeople are managed

10. Star Schema Features
• Star schemas are a central part of an access and
performance optimization layer.
• The goal for star schemas is structural simplicity and
high performance data retrieval
• The star schema accepts data redundancy (de
normalization) in its dimension tables for the sake of
easy user understanding and better data retrieval
performance.
• The star schemas are knit together through conformed
dimensions and conformed facts.
• A common criticism of star schemas is that they limit
analysis flexibility compared to 3NF designs.

11. Advantages of Star Schema
• Star schemas are de normalised meaning the typical
rules of normalization applied to transactional
relational databases are relaxed during star-schema
design and implementation.
• Simpler queries – star-schema join-logic is generally
simpler than the join logic required to retrieve data
from a highly normalized transactional schema.
• Simplified business reporting logic.
• Query performance gains
• Fast aggregations – the simpler queries against a star
schema can result in improved performance for
aggregation operations.

12. Assignment
• Explain the concept and usage of Star Schema
• Illustrate the usage of star schema in sales
data management and analysis.