Handling Slowly Changing Dimensions (SCD) in Modern Data Pipelines: A Complete Guide with SQL Examples
Explore SCD Types 0, 1, 2, 3, 4, and 6 to effectively track historical data changes and optimize data warehousing strategies
Introduction
Businesses need accurate data for analysis and insights to offer their customers the right services and products. The data landscape has evolved over time from OLTP to Data Lakehouse. However, the need to keep updated with current information and the history of changes, such as customer addresses, products, prices, etc., remains the same for various reasons, such as compliance, better customer service, or business intelligence needs.
Dimensions and facts are fundamental components of DW/BI systems. Dimensions provide the context for your measures (facts). Examples could be sales by region, sales by time period, etc. The methodology that helps you handle changes to a dimension value is known as the Slowly Changing Dimension or SCD. I will explore SCD, its types, how to choose the right SCD, how to implement SCDs in modern data pipelines, and best practices for handling SCDs.
What is Slowly Changing Dimension (SCD)?
A Slowly Changing Dimension (SCD) is a dimension that can change its value over time. A simple example is a customer's address, which can change several times when the customer moves to a different place. The SCD helps businesses track these changes for accurate reporting and analysis.
Types of Slowly Changing Dimensions
Type 0 — Fixed Dimensions (No Change Allowed)
In type 0, the dimension value cannot be changed. Once it is inserted in a dimension table, it can't be updated. This should be static information.
Type 1 — Overwrite Approach
In this Type 1 SCD, we simply overwrite the existing value with the new value. That means historical data is lost.
Type 2 — Versioning with Historical Tracking
In SCD Type 2, a new row is inserted for each change in value, with an effective date to track the historical changes.
Type 3 — Adding New Columns for Changes
In this SCD Type 3, a new column is added to store a previous value, which helps maintain a limited history.
Type 4 — Hybrid Approach (Current + History Table)
In Type 4, the current data is maintained in one table, and changes are stored in a separate historical table.
Type 6 — Hybrid Approach (Type 1 + Type 2 + Type 3)
SCD Type 6 combines features of SCD Types 1, 2, and 3 to maintain both historical and current data.
Implementing SCDs in Modern Data Pipelines
Modern data architectures leverage various tools and frameworks to automate and scale SCD management effectively.
Snowflake’s zero-copy cloning and time travel capabilities make it ideal for implementing different SCD types while maintaining performance.
dbt’s built-in versioning and incremental processing features enable efficient handling of historical changes and documentation of transformation logic.
Apache Airflow orchestrates the entire process by scheduling and monitoring SCD updates, ensuring data consistency and timely processing of dimensional changes.
These tools work together in a data pipeline: Airflow orchestrates the overall flow, dbt handles the transformation logic for different SCD types, and Snowflake manages the underlying data storage and processing, creating a robust and scalable SCD implementation.
Best Practices for Handling SCDs
Use surrogate keys for better query performance.
Implement automated ETL/ELT processes for efficient data updates.
Optimize query performance with proper indexing and partitioning.
Regularly monitor for data consistency and integrity.
Conclusion
Effectively handling Slowly Changing Dimensions can help organizations maintain historical data accurately and meet their business intelligence and reporting requirements in data pipelines. Choosing the right SCD type based on business needs and technical capabilities ensures a scalable data architecture.
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