A data cube is a method used for interpreting data efficiently, especially with multiple dimensions for business analysis. It offers advantages over traditional data warehouses, such as improved speed through pre-aggregation, enabling multi-dimensional analysis, and allowing for better data security. The document outlines the structure of data warehouses, including fact and dimension tables, and different approaches for creating dimensional models.

1. DATA CUBES
• Siddhartha Jain
siddhartha.j37@gmail.com
https://www.linkedin.com/in/siddhartha-jain-25160170/

2. What is data cube?
• A data cube is generally used to easily interpret data. It is especially
useful when representing data together with dimensions as certain
measures of business requirements

3. Why do we need Cubes? What are its benefits
over regular data warehouse?
Speed: Aggregating the data for performance. During cube process SSAS
will pre calculate and physically store aggregation of facts.
Make Business User more capable
Multi-Dimensional Analysis: slice, dice and drill down.
Can Store Hierarchy: Parent Child Relationship.
No Need for Joins.
Security: Can restrict cube slice access according to relevance of data
with the user.
SCD: Slowly Changing Dimension
KPI

4. How Do We Make Cubes. What is the
architecture?

5. What Could be the possible shape of data
warehouse?
Star Schema Snow Flake Schema

6. Fact Table vs. Dimension Table
• Fact tables contain the data corresponding to a particular business process.
Each row represents a single event associated with a process and contains
the measurement data associated with that event.
Example: a retail organization might have fact tables related to customer
purchases, qty, discount, and product returns.
• Dimensions describe the objects involved in a business intelligence effort.
While facts correspond to events, dimensions correspond to people, items,
or other objects.
Example: In the retail scenario used in the example above, we discussed that
purchases, returns, and calls are facts. On the other hand, customers,
employees, items, and stores are dimensions and should be contained in
dimension tables.

7. Ways to create dimensional model
• Top Down
• We have no relational database. We just have an idea what dimensional model looks like.
• We start from designing the dimensional model
• Bottom Up

8. Thank You