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One of the most critical challenges faced by data architects is managing Slowly Changing Dimensions (SCD) in the enterprise data warehouse. In this era of Big Data, handling changing data over time is vital for businesses to develop accurate historical reports. This blog post will focus on some enterprise patterns using technologies such as SQL Server 2016 and 2017, MySQL 5.7, DB2 11.1, and Azure SQL to manage SCDs effectively.

SCDs represent those business entities that undergo changes over time. This could be as simple as a change in a customer's address or as complex as a product's attributes evolving. There are several recognized patterns for managing SCDs in a data warehouse: Type 1, Type 2, and Type 3. Each of these methods has its strengths and limitations, and the choice depends on the specific requirement of the business.

In the case of Type 1 SCDs, when a change occurs, the original record is updated with the new information. This method is appropriate when historical data isn't important for analytical purposes. Here's an example of how you might implement this in SQL Server:

UPDATE Customers
SET Address = 'New Address'
WHERE CustomerID = 123

Type 2 SCDs, on the other hand, preserve the entire history of changes. When a change occurs, a new record is added with the new information and the original record remains intact. This is often used when you need to track historical changes. Here's how you could implement this in MySQL:

INSERT INTO Customers_History (CustomerID, Address, ChangeDate)
SELECT CustomerID, Address, NOW()
FROM Customers
WHERE CustomerID = 123;

Type 3 SCDs save the original and the most recent values of the changing attribute. This method is used when it's important to track the immediate past value and the current value, but not the entire history. Here's an example of a Type 3 SCD using DB2:

UPDATE Customers
SET Old_Address = Address,
Address = 'New Address'
WHERE CustomerID = 123;

The Azure SQL Data Warehouse supports another method for handling SCDs called Temporal Tables. Temporal Tables keep a full history of data changes and enable easy point in time analysis. This can be very useful for auditing, data forensics, and analytical purposes.

In conclusion, managing Slowly Changing Dimensions in a data warehouse is a complex task that requires careful planning and execution. The choice of SCD type depends on the specific business requirements and the nature of the data. By leveraging the tools and techniques provided by SQL Server, MySQL, DB2, and Azure SQL, businesses can effectively manage their SCDs and derive valuable insights from their data.

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