When you work with real-world data, storing numbers in rows and columns often isn’t enough. Businesses need faster insights, and that’s where the multidimensional data model in data warehouse comes in. Think of it as a data cube where each side represents something—time, location, product, or sales.
What It Means
Dimension → the perspective you analyse data from (Product, City, Time).
Fact → the values you want to measure (Sales, Revenue, Profit).
This approach helps answer questions like: “What were the top-selling products in Mumbai during Diwali?” without writing overly complex queries.
Why It’s Useful
Faster reporting and queries
Easy for non-technical users
Flexible comparisons across dimensions
Clear dashboards and analytics
Schema Types
Star Schema → simple structure, one fact table connected to dimensions
Snowflake Schema → more detailed, with normalised dimension tables
Galaxy Schema → multiple fact tables sharing dimensions, useful for large-scale businesses
OLAP Operations
With OLAP (Online Analytical Processing), you can:
Roll-up → move from daily to monthly or yearly summaries
Drill-down → go deeper into details
Slice & Dice → filter data by time or city
Pivot → rotate the cube for a new view
Why Developers and Students Should Care
If you’re starting out in data analytics or engineering, understanding the multidimensional data model in data warehouse and OLAP operations can give you an edge. It’s the foundation of how modern businesses turn raw data into insights.
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