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Slowly Changing Dimensions (SCD) is a common term in Data Warehouse that refers to the dimensions whose data changes slowly, rather than changing on a regular time basis. These dimensions can provide a historical perspective on the data to track changes over time. Today, we will dive into the concept of Slowly Changing Dimensions, specifically focusing on the design of historical snapshots using SQL Server 2019, MySQL 8.0, DB2 11.5, Azure SQL, and Azure Synapse.

SCDs are categorized into three types: Type 1, Type 2, and Type 3. However, for the purpose of historical snapshots, Type 2 SCDs are most suitable as they allow us to keep a full history of data changes in the dimension. In a Type 2 SCD, a new record is added to the table whenever there is a change in the key attribute or any other attribute in the dimension. This way, we can keep track of historical changes.

Let's consider a simple example where we have a Product dimension table in a SQL Server 2019 database. We want to keep track of the changes in product price over time. The T-SQL code for creating such a table can be:

CREATE TABLE Product (
    ProductID INT PRIMARY KEY,
    ProductName VARCHAR(255),
    Price DECIMAL(10,2),
    EffectiveDate DATETIME,
    EndDate DATETIME
)

In this table, EffectiveDate and EndDate are used to track the period during which the product price is effective. Whenever the product price changes, a new record is inserted into the table with the new price and the current date as the EffectiveDate. The EndDate of the previous record for that product is updated to the current date.

Designing historical snapshots with SCDs in MySQL 8.0, DB2 11.5, Azure SQL, and Azure Synapse follows the same conceptual model. The syntax for creating the table may vary slightly depending on the specific SQL dialect used.

For instance, here's how you can create a similar Product dimension table in MySQL 8.0:

CREATE TABLE Product (
    ProductID INT PRIMARY KEY,
    ProductName VARCHAR(255),
    Price DECIMAL(10,2),
    EffectiveDate TIMESTAMP,
    EndDate TIMESTAMP
)

With this setup, we are now able to create historical snapshots of our data. This can be particularly important in business analytics, where understanding how data and trends have changed over time can provide valuable insights for decision making.

In conclusion, designing historical snapshots with slowly changing dimensions is a powerful technique for maintaining a history of data changes in a data warehouse. Regardless of the database technology used, the core concept remains the same and can be implemented effectively with a correct understanding of the underlying principles.