Understanding SCD Type-2 Tables and Granularity Changes

Introduction

In this article, we will delve into the world of data modeling and specifically focus on Change Data Capture (CDC) type-2 tables. These tables are designed to capture changes in a dataset over time, allowing for efficient maintenance and analysis of historical data. We will explore the concept of granularity changes within these tables and how they impact data modeling.

What are SCD Type-2 Tables?

SCD stands for Slowly Changing Dimension (or Attributes). It is a data modeling technique used to capture changes in dimension values over time. In an SCD type-2 table, each row represents a single instance of a dimension value at a specific point in time. This allows for the tracking of historical changes in that dimension.

For example, consider an order table with a column representing the delivery date. Each row would represent an order with its corresponding delivery date.

Granularity Changes

Granularity refers to the level of detail at which changes are captured in an SCD type-2 table. In our example, we have two different granularities:

1. Order granularity: This means that the delivery date is only recorded once per order, when the order is created.
2. Line item granularity: This means that each line item on an order has its own delivery date, which can change independently of the overall order.

The Challenge

The question at hand is whether it’s possible to change the granularity of a table from Order level (Delivery date in Order granularity) to Line Item level (delivery date changes are captured at the individual line item level). To achieve this, we need to update the existing tables to reflect these changes while maintaining historical accuracy.

The Solution

The provided SQL statement is an attempt to solve this problem. It joins two tables (t1 and t2) based on their primary keys (Order_Id). The join conditions are set up such that only rows where the start date of one table falls within the end date range of the other table are included in the results.

SELECT t1.Order_Id, t2.Line_Item_Id, t2.Line_Item_Desc, t2.Quantity, 
       t1.Delivery_Dt,
       MAX(t1.Start_Date, t2.Start_Date) AS Start_Date,
       MIN(t1.End_Date, t2.End_Date) AS End_Date
FROM t2 
INNER JOIN T1 
    ON t2.Order_Id = t1.Order_Id
       AND t1.Start_Date < t2.End_Date 
       AND t1.End_Date > t2.Start_Date 
WHERE MAX(t1.Start_Date, t2.Start_Date) <> MIN(t1.End_Date, t2.End_Date)

This approach captures the changes in delivery date at both the order and line item levels. However, this may not be the most efficient or accurate solution.

Alternative Solutions

There are several alternative solutions to consider when dealing with SCD type-2 tables:

• Db2 Temporal Tables: These offer time travel SQL capabilities, allowing for more flexible and efficient handling of historical data.
• Materialized View: This approach involves creating a new table that is periodically updated with the most recent values from the underlying tables. This can be particularly useful when dealing with frequently changing data.
• Change Data Capture (CDC): This technique involves tracking changes made to a dataset and storing them in a separate log or history table. This allows for efficient handling of historical data and can be integrated into various data modeling strategies.

Conclusion

Changing the granularity of an SCD type-2 table from Order level to Line Item level requires careful consideration of the data model and the implications for data integrity and accuracy. While the provided SQL statement offers a possible solution, it is essential to evaluate alternative approaches and consider factors such as efficiency, scalability, and maintainability.

In our next article, we will explore more advanced topics in data modeling and CDC techniques using Db2 Temporal Tables and materialized views.

Last modified on 2023-07-21