Attribute tables add dimensional context to your fact data—the "who, what, where, when" that transforms raw numbers into meaningful insights. This guide covers creating attribute tables, linking them to facts, and following best practices for dimensional modeling.
What Are Attribute Tables?
Attribute tables (also called dimension tables) contain descriptive information that adds context to your fact data. They answer questions like: Who is this customer? What product category is this? Where did this transaction occur?
Common Attribute Table Examples
Names, addresses, segments, account managers
Descriptions, categories, brands, SKUs
Regions, countries, states, cities
Fiscal periods, quarters, weeks, holidays
Creating an Attribute Table
You can create attribute tables by uploading data files or by converting unassigned tables that have already been uploaded.
Understanding Primary Keys
Every attribute table needs a primary key—a column with unique values that identifies each row. This key is what connects your attribute table to fact tables.
Primary Key Requirements
- Unique: No duplicate values allowed
- Non-null: Every row must have a value
- Stable: Values shouldn't change over time
- Matching: Must match the foreign key column in your fact table
Example: Customer Attribute Table
| CustomerID 🔑 | CustomerName | Region | Segment |
|---|---|---|---|
C001
|
Acme Corporation | Northeast | Enterprise |
C002
|
Beta Industries | West | Mid-Market |
C003
|
Gamma Solutions | Southeast | SMB |
CustomerID is the primary key—it uniquely identifies each customer and links to CustomerID in your sales fact table.
Best Practices for Attribute Tables
✅ Do This
- One table per entity type (customers, products, locations)
- Use consistent key naming across tables
- Include all attributes you might filter or group by
- Keep attribute data current and maintained
- Document what each column represents
❌ Avoid This
- Combining multiple entities in one table
- Duplicate keys (causes relationship errors)
- Null values in key columns
- Overly wide tables with unused columns
- Changing key values after relationships exist
Updating Attribute Data
Attribute data may change over time—customers move regions, products get recategorized. Here's how to handle updates:
If you use Add Data and include records that already exist, you'll create duplicate keys. This breaks relationships and causes "many-to-many" errors. Always deduplicate your data before incremental adds.
Next Steps
With fact and attribute tables created, you're ready to link them together through relationships—the connections that enable filtering and grouping.
Attribute tables are essential for transforming raw numbers into meaningful business insights. Follow the best practices outlined here—one table per entity, consistent keys, no duplicates—and your analytical model will be robust and maintainable. Remember to keep attribute data current as your business evolves.
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