Handling slowly changing dimensions (SCD) is a common challenge in data modeling when dealing with data that changes over time. Slowly changing dimensions refer to the situation where the attributes of a dimension (e.g., customer, product) can change slowly, and the historical values need to be preserved for analysis and reporting. Snowflake offers several approaches to handle SCDs, and the choice depends on the specific requirements of the data model. Here are some common approaches:
**1. Type 1 (Overwrite):**
In the Type 1 approach, whenever a change occurs in the dimension attribute, the existing record is updated with the new values. This approach doesn’t maintain historical changes and only reflects the current state of the data. It is suitable when historical values are not important, and only the latest data matters.
**2. Type 2 (Add Rows with Versioning):**
The Type 2 approach involves creating a new record with a new version or timestamp whenever a change occurs in the dimension attribute. This way, historical changes are preserved as new rows with different versions. Typically, a surrogate key and effective date columns are used to track versioning. Type 2 is useful when you need to maintain a complete history of changes.
**3. Type 3 (Add Columns for Changes):**
In Type 3 SCD, additional columns are added to the dimension table to store some specific historical changes. For example, you might add “previous_value” and “previous_update_date” columns to track the last update. Type 3 is suitable when you only need to capture a few specific historical changes and don’t require a full historical record.
**4. Type 4 (Temporal Table – Without History Table):**
Snowflake supports native temporal tables, where you can create a table with a “TIMESTAMP” column to automatically track changes. This allows you to query data as of a specific point in time without the need for separate history tables. Snowflake handles the versioning and temporal queries automatically.
**5. Type 6 (Hybrid Approach):**
Type 6 is a combination of multiple SCD approaches. It involves maintaining both the current and historical attributes in the dimension table and also tracking certain specific historical changes in separate columns. This approach offers a balance between preserving historical data and managing data storage efficiently.
**6. Slowly Changing Dimensions Using Streams:**
Snowflake’s STREAMS feature can be used to capture changes in the dimension table, allowing you to track updates and insert new records into a separate history table automatically.
**7. Slowly Changing Dimensions Using Snowpipe:**
Snowpipe, Snowflake’s data ingestion feature, can be used to load and process SCD changes in real-time or near real-time. Snowpipe can capture changes from external sources and load them into dimension tables, making it easy to manage SCD changes.
The choice of the approach depends on the specific business requirements, data volume, and reporting needs. In some cases, you might even use a combination of approaches to handle different aspects of slowly changing dimensions within the data model. By understanding the available options and evaluating the trade-offs, you can design an efficient and effective solution to manage SCDs in Snowflake data models.