In data warehousing, slowly changing dimensions (SCDs) refer to the ability to capture and store historical and changing data over time. Snowflake schema is a widely used data modeling technique in data warehousing systems. In this blog post, we will explore how to implement slowly changing dimensions in a Snowflake schema.

What are Slowly Changing Dimensions?

Slowly changing dimensions are those dimensions in a data warehouse that change over time. The changes can be classified into different types:

1. Type 1 SCD: In this type, the existing record is overwritten with the new data, thereby losing the historical information.

2. Type 2 SCD: In this type, a new record is added to the dimension table for each change, maintaining historical data.

3. Type 3 SCD: In this type, the dimension table has separate columns to store the current and previous versions of the data, preserving limited historical information.

Implementing Slowly Changing Dimensions in Snowflake Schema

Snowflake schema is a dimensional model that consists of a central fact table surrounded by multiple dimension tables, connected through primary-foreign key relationships. Here’s how you can implement slowly changing dimensions in a Snowflake schema:

Type 1 SCD

To implement Type 1 SCD in Snowflake schema, you can directly update the existing record in the dimension table with the new values whenever a change occurs. This approach is suitable when preserving historical information is not a requirement.

-- Example SQL code for Type 1 SCD
UPDATE dimension_table
SET attribute = 'new_value'
WHERE dimension_key = 'desired_key';

Type 2 SCD

To implement Type 2 SCD in Snowflake schema, you need to create a separate table, commonly known as a “history” or “changes” table, to store the historical versions of the dimension data. When a change occurs, a new record is inserted into the dimension table, and the previous record is moved to the history table.

-- Example SQL code for Type 2 SCD
INSERT INTO dimension_table (dimension_key, attribute)
VALUES ('new_key', 'new_value');

INSERT INTO history_table (dimension_key, attribute, effective_date, expiration_date)
SELECT dimension_key, attribute, current_date, NULL
FROM dimension_table
WHERE dimension_key = 'desired_key';

UPDATE history_table
SET expiration_date = current_date
WHERE dimension_key = 'desired_key';

Type 3 SCD

To implement Type 3 SCD in Snowflake schema, you need to add additional columns to the dimension table to store the current and previous versions of the data. The current version is updated whenever a change occurs, and the previous version is preserved in the respective column.

-- Example SQL code for Type 3 SCD
UPDATE dimension_table
SET previous_value = current_value,
    current_value = 'new_value'
WHERE dimension_key = 'desired_key';

Conclusion

Slowly changing dimensions are essential in capturing and storing historical and changing data in a data warehouse. By implementing different types of slowly changing dimensions in a Snowflake schema, you can effectively manage and analyze changing data over time. Whether it’s Type 1, Type 2, or Type 3 SCD, Snowflake schema provides the flexibility and scalability to handle these requirements in a structured manner.

#datawarehousing #snowflakeschema