Get trial

English

The Benefits and Challenges of Slowly Changing Dimensions 

The Benefits and Challenges of Slowly Changing Dimensions
author
Richard Lehnerdt Jun 28, 2023

Slowly changing dimensions (SCDs) are a type of data tracking which allows for a more granular understanding of how data evolves over time. Originally, SCDs were introduced by Ralph Kimball for dimensions, but nowadays, SCDs are often used for the historization of raw data as well. This is why the layer with historicized data is called a "Persisted staging layer." In this article, we discuss some of the advantages and drawbacks of implementing these types of dimensions in your data tracking system. 

A slowly changing dimension in data management and data warehousing is a dimension which contains relatively static data which can change slowly but unpredictably, rather than according to a regular schedule. 

 

 

Thoughtful young programmer coding on computer in the evening at home

It's important to note that the changes in the data do not necessarily have to be "slow." Some Data Automation tools enable the historization of data that can often be changed at a faster pace. This allows for the tracking and recording of data changes, even if they occur rapidly or unpredictably.

Implementing Slowly Changing Dimensions (SCD) without Data Warehouse (DWH) automation can be a complex task. It involves a lot of manual work and requires efficient ETL (Extract, Transform, Load) processes to ensure the accuracy of data. However, it is feasible to implement SCD without DWH automation, but it would be time-consuming and prone to errors.

DWH automation can simplify and accelerate the SCD implementation process by providing pre-built components and automation tools. This can help reduce human error and ensure data consistency. 

 

Definition of Slowly Changing Dimensions 

  • A Slowly Changing Dimension (SCD) is a type of data warehouse architecture that captures and stores historical data over long time periods. 

  • It is used to track changes in dimension attributes of an entity over time. 

  • It is a data modeling technique that allows the user to capture changes in attribute values over time. 

Slowly changing dimension Example 

A good example of a slowly changing dimension is a customer's address history. In this case, the customer's address can change over time and we need to keep track of these changes in our data warehouse.  

To implement this in data modeling, we would create a dimension table for the customer with columns like customer ID, customer name, and customer address. We would also include a start date and end date column to indicate when the address was valid.  

Using the Kimball methodology, we would add a surrogate key to the dimension table to uniquely identify each record. We would then create a separate fact table that includes the surrogate key from the dimension table, along with other key business metrics like sales.  

When a customer's address changes, we would add a new record to the customer dimension table with the new address and a start date. We would then update the end date of the previous record to indicate when the old address was no longer valid.  

This way, when we query the fact table to see sales by customer, we can join it to the dimension table and get the correct address based on the date range of the sales. 

Benefits of Slowly changing dimensions

  • SCDs help organizations track information and changes that occur in the environment. 

  • They reduce the need to re-run data analysis when changes are made in the source system. 

  • They are useful for history tracking and audit trails. 

Challenges of Slowly changing dimensions

  • Slow changing dimensions require a lot of manual effort to maintain. 

  • They can be difficult to implement, as they require complex data modeling and programming. 

  • They can also be difficult to query, as they require complex SQL queries to retrieve historical data. 

 

startup business, software developer working on desktop  computer at modern officeSlowly Changing Dimensions serve as a powerful tool for effectively tracking changes in the environment and analyzing historical data. While they may pose challenges in terms of maintenance and querying, the advantages of maintaining a reliable history of changes can prove to be invaluable. It is crucial for organizations to thoroughly assess the benefits and challenges associated with SCDs before making a decision to implement them. By carefully considering these factors, organizations can make informed choices that align with their data management goals and optimize their ability to leverage historical data for insights and decision-making. 

DWH automation tools, like AnalyticsCreator will automatically generate the architecture and the code for SCD as default. This can dramatically speed up the process of data integration, transformation, and loading. This reduces the risk of human error and ensures that changes are accurately tracked and recorded. Additionally, automation can help to save time and resources, as well as improve the overall efficiency of the data warehousing process. By using DWH automation, organizations can more easily implement and maintain slowly changing dimensions, allowing them to make better use of historical data for analysis and decision-making. 

 

Related Blogs

Analyze trends & compare data over time with snapshot historization in Azure

Analyze trends & compare data over time with snapshot historization in Azure
GO TO >

Why You Need a Holistic Data Model and a Data Catalog for Your Azure Environment

Why You Need a Holistic Data Model and a Data Catalog for Your Azure Environment
GO TO >

New connector for exporting data from DWH to many target systems

New connector for exporting data from DWH to many target systems
GO TO >

Analyze trends & compare data over time with snapshot historization in Azure

Analyze trends & compare data over time with snapshot historization in Azure
GO TO >

Why You Need a Holistic Data Model and a Data Catalog for Your Azure Environment

Why You Need a Holistic Data Model and a Data Catalog for Your Azure Environment
GO TO >

New connector for exporting data from DWH to many target systems

New connector for exporting data from DWH to many target systems
GO TO >

Analyze trends & compare data over time with snapshot historization in Azure

Analyze trends & compare data over time with snapshot historization in Azure
GO TO >

Why You Need a Holistic Data Model and a Data Catalog for Your Azure Environment

Why You Need a Holistic Data Model and a Data Catalog for Your Azure Environment
GO TO >

New connector for exporting data from DWH to many target systems

New connector for exporting data from DWH to many target systems
GO TO >