SCD Type 2 implementation in Fabric Data Warehouse (Slowly Changing Dimension SCD Type 2) by taik18

SCD Type 2 implementation in Fabric Data Warehouse (Slowly Changing Dimension SCD Type 2) by taik18
''Improving Version Control and Historical Trend Analysis in Fabric Data Warehouse''


Slowly Changing Dimension (SCD) Type 2 is a common technique used in data warehousing to handle changes to dimensional data over time while preserving historical information. In the context of a Fabric Data Warehouse, which likely deals with data related to the textile industry, implementing SCD Type 2 involves setting up a mechanism to track and manage changes to dimensional data such as product information, customer details, and other relevant entities.

Identifying Dimensional Entities: Begin by identifying the dimensional entities in your Fabric Data Warehouse that are subject to change over time. These could include dimensions such as products, customers, suppliers, locations, etc.

Designing Dimension Tables: Create dimension tables in your data warehouse schema to store the attributes of these entities. Each dimension table should have a primary key that uniquely identifies each record.

Introducing Versioning: Add additional columns to your dimension tables to track the history of changes. Typically, these columns include:

Effective Date: Indicates when a particular version of a record became effective.
Expiry Date: Indicates when a particular version of a record expires or is superseded by a new version.
Current Indicator: A flag to indicate the most current version of a record.
Surrogate Key: A unique identifier for each version of a record, independent of the natural key.
Updating Records: When a change occurs to a dimensional entity (e.g., a product's price changes or a customer's address is updated), instead of updating the existing record, insert a new record with the updated attributes and appropriately set the effective and expiry dates. Update the expiry date of the previous version to reflect the point in time when it was superseded.

Querying Historical Data: When querying the dimension tables, consider the effective and expiry dates to retrieve the appropriate version of the data for a given point in time. This allows historical analysis and reporting while maintaining data integrity.

Handling Type 2 SCD in ETL Processes: In your ETL (Extract, Transform, Load) processes, implement logic to identify changes to dimensional data and apply the appropriate inserts and updates to the dimension tables. This may involve comparing incoming data with existing records based on natural keys and managing the versioning accordingly.

Maintaining Referential Integrity: Ensure that foreign key relationships between fact tables and dimension tables are maintained correctly, considering the versioning of dimensional data.

Performance Considerations: Implement indexing strategies on effective and expiry date columns to optimize query performance when retrieving historical data.

By implementing SCD Type 2 in your Fabric Data Warehouse, you can effectively manage changes to dimensional data over time, maintain historical information, and support accurate reporting and analysis.

FTC Legal Disclaimer - Some links found in the description box of my videos may be affiliate links, meaning I will make commission on sales you make through my link. This is at no extra cost to you to use my links/codes, it's just one more way to support me and my channel! 🙂

SCD Type 2 implementation in Fabric Data Warehouse (Slowly Changing Dimension SCD Type 2) by taik18,fabric,SQL,Dataflow,Power BI,Data Pipeline,Data Factory,scd type 2,real-world,scd type 2 implementation in fabric data warehouse (slowly changing dimension scd type 2) by taik18,power bi,SCD Type 2 implementation in Fabric,scd type 2 implementation in fabric,SCD Type 2 implementation in Warehouse,scd type 2 implementation in warehouse,data pipeline,taik18