Mastering Slowly Changing Dimensions (SCD1 and SCD2) with dbt and Snowflake


Slowly Changing Dimensions (SCDs) play a crucial role in data warehousing, enabling the tracking and management of historical changes in dimensional data over time. In this article, we delve into how dbt (data build tool) and Snowflake, a cloud data platform, can be leveraged to implement SCD1 and SCD2 handling effectively.

Understanding Slowly Changing Dimensions (SCDs):

1. SCD1 (Type 1):

  • In SCD1, the old data is simply overwritten with new data.
  • It does not maintain any historical changes, making it suitable for scenarios where historical tracking is not required.

2. SCD2 (Type 2):

  • SCD2 maintains a historical record of changes by creating new records for each change while retaining the old ones.
  • It includes attributes like start and end dates or version numbers to track the history of changes.

Implementing SCD1 and SCD2 Handling with dbt and Snowflake:

1. SCD1 Implementation:

  • In dbt, SCD1 handling involves straightforward updates to existing records.
  • Using Snowflake’s MERGE statement, dbt can efficiently update records based on a unique key.
  • Example: Updating customer information with the latest details without preserving historical changes.

2. SCD2 Implementation:

  • dbt facilitates SCD2 handling by maintaining historical records alongside the current ones.
  • Snowflake’s Time Travel feature enables querying data at specific points in time, facilitating SCD2 implementation.
  • Example: Tracking changes in product prices over time by creating new records for price updates while retaining previous versions.

Best Practices and Considerations:

1. Data Modeling:

  • Designing effective data models with appropriate primary and foreign keys is essential for SCD implementation.
  • Utilizing dbt’s modeling capabilities to define relationships and transformations simplifies SCD handling.

2. Versioning and Auditing:

  • Incorporating versioning and auditing mechanisms ensures traceability and accountability for data changes.
  • Snowflake’s native features like Streams and Change Data Capture (CDC) can aid in auditing historical changes.

3. Performance Optimization:

  • Optimizing performance is critical, especially for SCD2 implementations handling large volumes of data.
  • Utilizing Snowflake’s clustering keys and dbt’s incremental models can enhance query performance.

Case Study: Real-World SCD Implementation:

1. Scenario Overview:

  • Illustrative example of implementing SCD1 and SCD2 handling for a retail company’s customer and product dimensions.

2. Implementation Steps:

  • Detailed walkthrough of creating dbt models, Snowflake tables, and implementing SCD logic for both SCD1 and SCD2.

3. Results and Insights:

  • Analysis of the impact of SCD handling on data accuracy, query performance, and historical tracking.

Get more useful articles on dbt

  1. ,
Author: user