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Introduction

Slowly Changing Dimensions in Data Warehouse is an of import concept that is used to enable the historic aspect of data in an belittling system. Every bit you know, the information warehouse is used to analyze historical data, it is essential to store the different states of data.

In information warehousing, we have fact and dimension tables to store the information. Dimensional tables are used to clarify the measures in the fact tables. In a data environment, data is initiated at operational databases and data will exist extracted-transformed-loaded (ETL) to the data warehouse to suit the belittling environment.

Customer, Product are examples for Dimensional tables. These dimension attributes are modified over time and in the data warehouse, we demand to maintain the history. In operational systems, nosotros may overwrite the modified attributes as we may not demand the historical aspects of data. Since our primary target in data warehousing is to analyze data with the perspective of history, we may not be able to just overwrite the data and we need to implement special techniques to maintain the history because belittling and volume aspects of the data warehouse. This implementation is done using Slowly Changing Dimensions in Data Warehouse.

What are Slowly Changing Dimensions

Before discussing the details of Slowly Changing Dimensions (SCDs), permit us list the dissimilar slowly changing dimensions as shown in the below table.

SCD Blazon	Summary
Type 0	Ignore any changes and inspect the changes.
Type 1	Overwrite the changes
Type 2	History volition be added as a new row.
Type iii	History will be added as a new column.
Type 4	A new dimension will exist added
Blazon vi	Combination of Type 2 and Blazon 3

Now allow us look at each blazon of slowly changing dimension.

SCD Type 0

There are situations where you ignore whatsoever changes. For example, when an employee joined an system, there are joined related attributes such equally joined Designation and JoinedDate, etc. that should not change over fourth dimension.

The post-obit is the instance for Type 0 of Slowly Changing Dimensions in Data Warehouse.

Type 0 Slowly Changing Dimensions in Data Warehouse

In the above Customer Dimension, FirstDesignation, JoinedDate and DateFirstPurchase are the attributes that will non be updated which is Blazon 0 SCD.

SCD Type 1

In the Type 1 SCD, you simply overwrite data in dimensions. At that place can be situations where you don't accept the unabridged information when the tape is initiated in the dimension. For example, when the customer record is initiated, you may not get all attributes. Therefore, when the customer record is initiated at the operational database, in that location will be empty or null records in the customer records. Once the ETL is executed, those empty records will be created in the data warehouse. In one case these attributes are filled in the operational databases, that has to be updated in the information warehouse.

Type 1 SCDs are identifying if the existing attributes are null and you are receiving a value from the operational table.

Type 1 Slowly Changing Dimensions in Data Warehouse

In the in a higher place Customer Dimension tabular array, the AnnualIncome of customers CustomerKey 11015 and 11019 are NULL. When these records are updated in the operational database, those values should be updated in the data warehouse without considering those are historical values.

SCD Blazon 2

Blazon ii Slowly Changing Dimensions in Data warehouse is the nigh popular dimension that is used in the data warehouse. As we discussed data warehouse is used for data analysis. If you need to clarify information, you demand to accommodate historical aspects of information. Let the states see how we can implement SCD Type ii.

For the SCD Type two, we need to include three more attributes such as StartDate, EndDate and IsCurrent as shown below.

Type 2 Slowly Changing Dimensions in Data Warehouse

In the above customer dimension, there are two records and let us say that client whose CustomerCode is AW00011012, has been promoted to Senior Management. All the same, if you just update the record with the new value, yous will non run across the previous records. Therefore, a new record will be created with a new CustomerKey and a new Designation. However, other attributes will exist remaining the same.

Implementation of Type 2 Slowly Changing Dimensions in Data Warehouse.

As you can come across in the higher up figure, CustomerCode AW00011012 has a new record with 11013. All the new transactions will be related to CustomerKey 11013 while previous transactional are related to CustomerKey 11012. This mechanism helps to preserve the celebrated attribute of the customer as shown in the beneath query.

SELECT C . Designation , SUM ( SalesAmount ) SalesAmount , SUM ( TotalProductCost ) TotalProductCost

FROM FactInternetSales F

INNER Bring together Dim_Customer C ON F . CustomerKey = C . CustomerKey

Grouping By C . Designation

Once the query is executed, the following results volition be observed.

Sample dataset for the Type 2 SCD.

Equally you can run across Management designation can exist seen in the to a higher place result which means that it has covered the historical aspects. Type two SCD is one of the implementations where you lot cannot avoid surrogate keys in dimensional tables in the data warehouse.

SCD Blazon 3

Type 3 Slowly Changing Dimension in Data warehouse is a unproblematic implementation where history volition be kept in the additional column. If we relate the same scenario that we discussed under Blazon two SCD to Type 3 SCD, the customer dimension would look like below.

Type 3 SCD

As you tin see, historical aspects of the information are preserved as a unlike cavalcade. However, this method volition not be scalable if y'all want to preserve history. Further, this technique will allow but to continue the last version of the history, dissimilar Type 2 SCD.

Typically, this would be better suited to implement name changes of an employee. In some cases, female employees will change their names after their union. In such situations, you can employ Type three SCD since these types of changes will not occur quickly.

SCD Type four

As we discussed in SCD blazon 2, we maintain the history by adding a different version of the row to the dimension. However, if the changes are rapid in nature Type 2 SCD volition not be scalable.

For example, let u.s. presume we want to keep the client run a risk type depending on his previous payment. Since this is an aspect related to the customer, information technology should be stored in a client dimension. This means every month there will exist a new version of the client record. If yous have 1000 customers, you are looking at 12,000 records per month. Every bit you can imagine this Slowly Changing Dimensions in Information Warehouse is not scalable.

Following is the relationship between the Fact and the Customer Dimension table.

Schema design before implementing Type 4 SCD.

SCD Type 4 is introduced in society to prepare this issue. In this technique, a speedily irresolute column is moved out of the dimension and is moved to a new dimension tabular array. This new dimension is linked to the fact tabular array as shown in the below diagram.

Schema design after implementing Type 4 SCD.

With the above implementation of Type 4 Slowly Changing Dimensions in Data Warehouse, you are eliminating the unnecessary book in the chief dimension. Notwithstanding, still you have the capabilities of performing the required analysis.

SCD Type 6

Type six Slowly Changing Dimensions in Data Warehouse is a combination of Type 2 and Type 3 SCDs. This means that Type six SCD has both columns are rows in its implementation.

Sample dataset for Type 6 SCD.

With this implementation, you tin can further improve the belittling capabilities in the data warehouse. If you want to find out an analysis between electric current and historical occupation, you tin use the following query.

SELECT C . Occupation , C . CurrentOccupation ,

SUM ( SalesAmount ) SalesAmount , SUM ( TotalProductCost ) TotalProductCost

FROM FactInternetSales F

INNER Bring together Dim_Customer C ON F . CustomerKey = C . CustomerKey

Grouping BY C . Occupation , C . CurrentOccupation

Above query volition provide the following result:

Results with Type 6 SCD.

Without Blazon 6, Slowly Changing Dimensions in Data Warehouse, complex queries accept to be used.

In the Blazon 6 SCD, not only the current occupation, you lot tin can employ the first occupation too in lodge to provide more analysis.

Conclusion

Slowly Irresolute Dimensions in Information Warehouse are used to perform dissimilar analyses. This article provides details of how to implement Different types of Slowly Changing Dimensions such equally Type 0, Type 1, Type 2, Type 3, Blazon 4 and Type 6. Type ii and Type half dozen are the almost commonly used dimension in a data warehouse.

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Dinesh Asanka is MVP for SQL Server Category for terminal 8 years. He has been working with SQL Server for more than than xv years, written manufactures and coauthored books. He is a presenter at various user groups and universities. He is always bachelor to learn and share his cognition.

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