In data warehousing, slowly changing facts refer to the type of data that changes gradually over time but needs to be stored and analyzed. One common technique to handle slowly changing facts is by using a Snowflake schema.

A Snowflake schema is a dimensional modeling technique that organizes data into multiple dimensions and fact tables. It is an extension of the star schema, where dimension tables are normalized into multiple smaller tables, forming a snowflake-like shape.

Understanding Slowly Changing Facts

Slowly changing facts typically arise in scenarios where data attributes change over time but need to be retained for analysis purposes. For example, consider a sales data warehouse where the price of a product may change periodically. If a historical analysis is required, it is necessary to capture the price at each point in time.

Implementing Slowly Changing Facts in Snowflake Schema

To handle slowly changing facts in a Snowflake schema, you can adopt one of the following strategies:

Type 1: Overwriting Existing Data

This strategy involves overwriting the existing data with the new values. In this approach, historical information is not maintained, and the latest value is always used. This method is suitable when historical data is not essential for analysis, and only the current state matters.

CREATE TABLE sales (
  product_id INT,
  price DECIMAL(10, 2)
);

Type 2: Adding New Rows

In this strategy, instead of overwriting data, new rows are added for each change. The existing rows remain unaffected, allowing historical analysis. Each row has an effective date range associated with it, indicating when the particular value was active.

CREATE TABLE sales (
  product_id INT,
  price DECIMAL(10, 2),
  valid_from DATE,
  valid_to DATE
);

Type 3: Adding Columns

This strategy involves adding new columns to the existing table to store both the current and historical values. This approach allows tracking the changes without adding new rows. However, it might lead to wider tables and additional complexity when querying the data.

CREATE TABLE sales (
  product_id INT,
  price DECIMAL(10, 2),
  prev_price DECIMAL(10, 2),
  last_updated TIMESTAMP
);

Choosing the Right Strategy

The choice of strategy depends on the specific requirements of your data warehouse. Consider the following factors:

• Historical analysis: If historical analysis is critical, prefer Type 2 or Type 3 strategies to maintain a record of changes over time.
• Data volume: Type 1 strategy is the most space-efficient, as it doesn’t duplicate data. However, it might not be suitable for scenarios with extensive historical data.
• Query complexity: Type 2 and Type 3 strategies might introduce additional complexity while querying data, as you need to consider effective dates or manage multiple columns.

By understanding the nature of your slowly changing facts and the trade-offs of different strategies, you can design an effective Snowflake schema that best suits your data warehousing needs.

Happy data warehousing!

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