In my previous post, we explored why we use OLAP systems (Data Warehouses) for analytics. But once you have a warehouse, how do you organize the data inside it? This is where Data Modeling comes in.
To make data easy to query, we use Dimensional Modeling, which organizes data into two types of tables: Facts and Dimensions.
The Building Blocks: Facts & Dimensions
1. Fact Tables
These are the central repositories for measurable business metrics.
- What they store: Quantitative measurements (facts) like sales amounts, quantities, or durations.
- Structure: Usually the largest tables, containing foreign keys that link to related dimension tables.
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Example: A
Sales_Facttable containingrevenue,quantity, anddiscount.
2. Dimension Tables
These provide the descriptive context that makes fact table measurements meaningful.
- What they store: Attributes used for filtering and grouping, such as product names, customer demographics, or dates.
- Structure: Typically smaller in terms of row count and often denormalized for speed.
The Star Schema
The Star Schema is the most fundamental and widely used pattern. It looks like a star because the central fact table is surrounded by a single layer of dimension tables.
Why use a Star Schema?
- Query Simplicity: It requires fewer joins, making it easier for business users to understand and query.
- Performance: Because there are fewer joins, queries generally execute faster.
- Tool Compatibility: Most BI tools (like Tableau or Power BI) are optimized for this structure.
The Snowflake Schema
The Snowflake Schema is an extension of the star schema. In this model, the dimension tables are normalized into multiple related tables.
Why use a Snowflake Schema?
- Storage Efficiency: Normalization reduces data redundancy, which is helpful if your dimension tables are massive.
- Data Integrity: It reduces the risk of inconsistencies because attributes are updated in only one place.
- Maintenance: Changes to hierarchical data (like a product category) are easier to manage.
Side-by-Side Comparison
| Feature | Star Schema | Snowflake Schema |
|---|---|---|
| Complexity | Simple (1 join per dimension) | Complex (multiple joins) |
| Data Redundancy | Higher (Denormalized) | Lower (Normalized) |
| Query Speed | Generally faster | Potentially slower due to joins |
| User Experience | Intuitive for business users | Less intuitive |
Which one should you choose?
- Choose Star Schema if you prioritize query speed and want to make it easy for non-technical users to build their own reports.
- Choose Snowflake Schema if you have very large dimension tables where storage costs are a concern or if you need to strictly enforce data integrity.
Summary: The Star Schema is built for speed and simplicity, while the Snowflake Schema is built for storage efficiency and organization.
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