SCD to handle changes in dimension data over time

 Slowly Changing Dimensions (SCDs) refer to a technique used in data warehousing and dimensional modeling to handle changes in dimension data over time. It is an essential concept when dealing with historical data and ensuring data integrity in a data warehouse environment. The aspects and implications of Slowly Changing Dimensions are as follows:

1. Types of Slowly Changing Dimensions:

   - Type 0 (Overwrite): In this type, when a dimension attribute changes, the old value is simply overwritten with the new value. This type is suitable when the historical data is not required.

   - Type 1 (Retain History): In this type, when a dimension attribute changes, a new row is created in the dimension table with the updated value, and the old row is preserved. This type allows tracking historical changes but can lead to data redundancy.

   - Type 2 (Add New Column): In this type, when a dimension attribute changes, a new column is added to the dimension table to store the new value, and the old value is retained in the original column. This type avoids data redundancy but can lead to a wide and sparse dimension table.

   - Type 3 (Add New Outrigger Table): In this type, when a dimension attribute changes, a new row is created in a separate outrigger table to store the new value, along with the surrogate key and the effective dates. This type maintains a lean dimension table while capturing historical changes in a separate table.

2. Implications:

   - Data Integrity: SCDs ensure that historical data is maintained accurately, allowing for proper analysis and reporting over time.

   - Performance: The choice of SCD type can impact the performance of the data warehouse. Type 1 and Type 2 can lead to larger dimension tables, while Type 3 introduces additional joins, potentially affecting query performance.

   - Data Modeling: Implementing SCDs requires careful data modeling and design decisions. It involves determining which attributes should be treated as slowly changing and choosing the appropriate SCD type based on business requirements and performance considerations.

   - ETL (Extract, Transform, Load) Complexity: Handling SCDs introduces additional complexity in the ETL processes, as logic needs to be implemented to detect changes, apply the appropriate SCD type, and maintain historical data.

   - Querying and Reporting: SCDs can impact the way queries are written and how reports are generated, especially when dealing with historical data or tracking changes over time.

   - Data Quality: SCDs can help maintain data quality by ensuring that historical data is not lost or overwritten, enabling accurate analysis and decision-making.

Slowly Changing Dimensions are a crucial aspect of data warehousing and dimensional modeling, allowing organizations to manage and track changes in dimension data over time while preserving historical information for analysis and reporting purposes. Proper implementation and understanding of SCDs are essential for maintaining data integrity, ensuring data quality, and enabling accurate and reliable decision-making based on historical data.

Scenarios:

Here are some scenarios that illustrate the use of different types of Slowly Changing Dimensions (SCDs):

1. Type 0 (Overwrite):

   Scenario: In a retail company's data warehouse, the "Product" dimension contains attributes like product name, category, and price. The product prices are frequently updated, and the company is not interested in maintaining historical pricing data.

   Implementation: When a product's price changes, the old price value in the "Product" dimension table is simply overwritten with the new price. The historical pricing information is not retained.

2. Type 1 (Retain History):

   Scenario: A telecom company needs to track changes in customer information, such as address and phone number, over time for regulatory and customer service purposes.

   Implementation: When a customer's address or phone number changes, a new row is added to the "Customer" dimension table with the updated information and a new surrogate key. The old row with the previous address or phone number is retained, allowing the company to track historical changes.

3. Type 2 (Add New Column):

   Scenario: A hospital's data warehouse stores patient information, including marital status, in the "Patient" dimension. The hospital wants to track changes in marital status over time, but the number of changes is expected to be minimal.

   Implementation: When a patient's marital status changes, a new column is added to the "Patient" dimension table to store the updated marital status value. The original marital status column is preserved, allowing the hospital to track historical changes while avoiding data redundancy.

4. Type 3 (Add New Outrigger Table):

   Scenario: An insurance company maintains a data warehouse that stores policy information, including the insured amount, in the "Policy" dimension. The insured amount can change multiple times during the policy's lifetime, and the company needs to track these changes for auditing and reporting purposes.

   Implementation: When the insured amount changes, a new row is added to a separate "Policy History" outrigger table, which stores the new insured amount, the policy surrogate key, and the effective dates. The "Policy" dimension table remains lean, while historical changes are captured in the outrigger table.

These scenarios demonstrate how different types of Slowly Changing Dimensions can be applied based on business requirements, data characteristics, and the need to track historical changes. The choice of SCD type depends on factors such as data volume, frequency of changes, performance considerations, and the level of historical data tracking required for analysis and reporting.