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Implementing Slowly Changing Dimension Type 3 (SDC Type 3) in Snowflake using Python and SQL

In this video of Data Engineering Series, I have discussed how to implement an important topic of Data warehousing, Slowly Changing Dimension Type 3 (SDC Type 3) in Snowflake using Python and SQL. SCD Type 3, also known as Slowly Changing Dimension Type 3, is a data warehousing and data modeling technique used to track historical changes in data over time. In SCD Type 3, only limited historical data is maintained, typically in two columns: one for the current value and one for the previous (or original) value. This approach is used when you want to maintain some historical information but don't need a complete history of all changes. Here are the key characteristics of SCD Type 3: Two Columns: SCD Type 3 maintains two columns for each attribute that can change over time. These columns are usually named something like "Current Value" and "Previous Value." Limited Historical Data: Unlike SCD Type 2, which maintains a full history of changes, SCD Type 3 retains only the most recent previous value. This means you can track what the value was before the most recent change, but you lose the historical context of earlier changes. Overwriting: When a change occurs, the current value in the "Current Value" column is updated with the new value, and the previous value in the "Previous Value" column is overwritten with the old value. This means you can only see the state of the data before the most recent change. SCD Type 3 is useful in scenarios where you need to capture some historical information but don't need a comprehensive history. It can help save storage space compared to SCD Type 2, which can be especially important when dealing with large datasets. However, it also has limitations in that it doesn't provide a complete historical record of all changes, making it less suitable for certain analytical and auditing purposes. #dataengineeringessentials #snowflake #snowflakes #sql #python #datawarehouse #datawarehousing