Slowly Changing Dimensions (SCDs) are a foundational concept in dimensional modeling and data warehousing. They help track changes in dimension data over time—like changes to customer addresses, product descriptions, or employee roles—so you can maintain historical accuracy or reflect only the most recent updates depending on business requirements.
This article explores the differences between Type 1 and Type 2 SCDs, where and why you’d use them, and how to implement them efficiently across Snowflake, Databricks, and Microsoft Fabric using Coalesce.
What are Slowly Changing Dimensions (SCDs)?
Slowly Changing Dimensions (SCDs) refer to data attributes in dimension tables that evolve gradually over time rather than changing regularly or rapidly. SCD techniques help determine whether to overwrite old values or preserve historical data.
Type 1 Slowly Changing Dimensions
Definition
Type 1 SCDs overwrite old data with new data. No historical data is preserved—only the latest value is retained.
Use case example
If a customer’s email address changes, and you don’t need to track previous addresses, a Type 1 update will replace the old email with the new one.
Benefits
- Simple to implement
- Saves storage space
- Useful when historical accuracy isn’t important
Type 2 Slowly Changing Dimensions
Definition
Type 2 SCDs preserve historical changes by creating a new record for each change. Metadata such as effective_date, end_date, or is_current helps track which version is active.
Use case example
Tracking a customer’s address history to understand regional buying behaviors or migration patterns over time.
Benefits
- Preserves historical data
- Enables time-based analytics
- Crucial for auditability and trend analysis
Real-World Use Cases for SCDs
| Industry | Type | Use Case |
| Retail | Type 2 | Tracking changes in customer loyalty tier or shipping addresses |
| Finance | Type 2 | Preserving account status changes for compliance |
| HR Systems | Type 1 | Updating employee phone numbers without keeping historical logs |
| SaaS | Type 2 | Tracking subscription plan changes over time for churn analysis |
Implementing SCDs in modern data platforms
Modern data platforms make it easier to implement SCDs—but not always easier to manage them over time. Below is a quick overview of how SCDs are handled across major data platforms:
Snowflake
- Use Dynamic Tables for declarative SCD logic
- Coalesce accelerates implementation with a visual interface and reusable templates
- How to implement slowly changing dimensions with dynamic tables and Coalesce
- Learn more: Snowflake and Coalesce
Databricks
- Implement SCD logic with Delta Lake and Merge statements
- How to implement a dimensional data warehouse with Databricks SQL: Part 1, Part 2, Part 3
- Coalesce makes this process low-code and standardized across teams
- Learn more: Databricks and Coalesce
Microsoft Fabric
- SCDs can be implemented using Lakehouse SQL and pipelines
- How to implement slowly changing dimension type 1 using Power Query
- How to implement slowly changing dimension type 2 using Data Factory
- With Coalesce’s native support, teams can easily define and manage SCD logic visually
- Learn more: Microsoft Fabric and Coalesce
Build analytics-ready data projects with Coalesce
Understanding when to use Type 1 vs. Type 2 Slowly Changing Dimensions is critical for designing effective, analytics-ready data models. With modern platforms like Snowflake, Databricks, and Microsoft Fabric—and the power of Coalesce—you can implement scalable, governed SCD strategies that meet business needs without added technical debt.
Coalesce makes implementing Type 1 and Type 2 SCDs easier, faster, and more scalable by:
- Automating SCD patterns through reusable, customizable templates
- Providing visibility into transformation logic through a visual DAG
- Supporting versioned history and metadata tracking natively
- Accelerating deployment across Snowflake, Databricks, and Microsoft Fabric
Whether you’re building audit-ready Type 2 dimensions or lightweight Type 1 updates, Coalesce helps reduce complexity and improve data quality across the board.
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