Multidimensional Data Model in Data Warehouse: When companies handle mountains of data daily, how do they make sense of it all? That’s where the multidimensional data model in data warehouse steps in, working like a magic sorting hat to organise, store, and fetch data in the blink of an eye.
But wait—what is multidimensional data model exactly? How does it help businesses?
What is Multidimensional Data Model?
Imagine your business data organised not in boring rows and columns, but in a 3D cube—like a Rubik’s cube. Each side of the cube represents a different aspect of your business, like time, products, geography, and sales.
In technical terms, the multidimensional data model in data warehouse structures data into dimensions and facts, making it easy to analyze from different angles quickly.
“Data is the new oil, and multidimensional models are the refineries.” — Clive Humby, Data Scientist
In simple words:
- Dimension: Perspective from which data can be viewed (like Product, Time, Location).
- Fact: Numerical data (like Sales, Profit) that you want to analyse.
By using a multidimensional data model in data warehouse, businesses can answer complex questions like, “What were our top-selling products in Mumbai during the Diwali season?“—with just a few clicks!
Why the Need of Multidimensional Data Model?
You might wonder, why not just use regular databases? Here’s why:
- Faster queries: With multidimensional structure, fetching reports is super-fast.
- User-friendly: Business users don’t have to be tech experts.
- Complex analysis: Compare across multiple dimensions easily.
- Better visualisation: Data can be represented beautifully using graphs, cubes, and dashboards.
The need of multidimensional data model arises because modern businesses don’t just want storage—they need powerful, flexible, and lightning-fast data insights.
Schemas for Multidimensional Data Model
The structure or blueprint of a multidimensional data model in data warehouse is called a schema. Let’s break it down:
- Star Schema
- The most common schema.
- A central fact table linked to dimension tables.
- Simple and fast!
- Snowflake Schema
- A more complex version of star schema.
- Dimension tables are further normalised into multiple related tables.
- Fact Constellation Schema (Galaxy Schema)
- Multiple fact tables share dimension tables.
- Used for complex business models.
These schemas for multidimensional data model make it easy to handle large-scale data for analytics and reporting.
Architecture of Multidimensional Data Model
Now let’s talk about the architecture of multidimensional data model—the backbone that supports all this magic.
The basic architecture includes:
- Data Sources: ERP, CRM, and other systems feeding raw data.
- Data Staging Area: Cleaning, transforming, and loading data.
- Data Warehouse: Centralised, multidimensional storage.
- OLAP Engine: Allows fast query processing.
- Front-End Tools: Dashboards, reporting tools, and analytics software.
In a way, the architecture of multidimensional data model acts like a relay race—each component handing over cleaned and organised data to the next.
OLAP Operations in Multidimensional Data Model
Operations performed on a multidimensional data model in data warehouse are known as OLAP operations (Online Analytical Processing). They help users interact with their data cubes effectively. The main OLAP operations in multidimensional data model are:
- Roll-up: Summarising data (e.g., moving from daily sales to monthly sales).
- Drill-down: Going deeper into details (e.g., from yearly sales to monthly, to daily).
- Slice: Selecting one dimension from the cube (e.g., Sales for 2024 only).
- Dice: Selecting two or more dimensions (e.g., Sales in Mumbai and Delhi during Diwali).
- Pivot (rotate): Changing the perspective to view data differently.
OLAP operations are the heartbeats that keep business intelligence alive and kicking.
With these operations in multidimensional data model, even complex business queries become manageable and fast.
Let’s say you’re running a retail chain across India. You would want to analyse:
- Which product sells the most during Holi in Delhi?
- How did online sales perform compared to offline stores during lockdowns?
Using the multidimensional data model in data warehouse, and applying OLAP operations in multidimensional data model, you can answer all these questions in minutes!
Benefits of Learning Multidimensional Data Models
Learning about multidimensional data model in data warehouse is not just for IT professionals. Today, even marketing managers, sales executives, and finance professionals need to know how to interact with data.
If you’re keen on upgrading your skills, you must explore courses at Ze Learning Labb. ZELL offers professional certifications like:
- Data Science Course
- Data Analytics Course
- Digital Marketing Course
These courses are designed for the Indian market and focus heavily on practical, real-world applications. Do you want to give it a try? Check out ZELL’s Trial Classes to give you a small glimpse into the training process here at Ze Learning Labb.
On A Final Note…
The multidimensional data model in data warehouse is a lifesaver for any organisation serious about making data-driven decisions. By understanding schemas for multidimensional data model, knowing OLAP operations in multidimensional data model, and appreciating the architecture of multidimensional data model, you open doors to powerful insights and smarter business strategies.
If you’re excited to master these skills, don’t forget to check out the specialised courses at Ze Learning Labb. Learning today can build the career you dream of tomorrow!
After all, in today’s world, “Those who understand data will rule the future!”