Implement a Slowly Changing Type 2 Dimension in SQL Server Integration Services – Part 1

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We’d like to keep history in our data warehouse for several dimensions.
We use SQL Server Integration Services (SSIS) to implement the ETL (Extract
Transform and Load). We tried the
built-in Slowly Changing Dimension wizard, but the performance seems poor. How can
we implement the desired functionality with regular SSIS components?


Introduction to Slowly Changing Dimensions

A slowly changing dimension (SCD) keeps track of the history of its individual
members. There are several methods proposed by Ralph Kimball in his book The Datawarehouse

  • Type 1 – Overwrite the fields when the value changes. No history is
  • Type 2 – Create a new line with the new values for the fields. Extra
    columns indicate when in time a row was valid.
  • Type 3 – Keep the old value of a column in a separate column.
  • There are more types of SCDs, but they are mostly a hybrid combination of
    the above.

In this tip, we’ll focus on the type 2 situation. Let’s illustrate
with an example. We have a simple table storing customer data.

scd2 example step 1

The SK_Customer column is a column with an identity
property which will generate a new value for every row. We’d like to keep
history for the Location attribute. When the location changes from Antwerp to Brussels,
we don’t update the row, but we insert a new record:

scd example 2

Using the ValidFrom and ValidTo fields, we
indicate when a record was valid in time. A new surrogate key is generated, but
the business key – CustomerName – remains the same. When a fact table
is loaded, a lookup will be done on the customer table. Depending on the timestamp
of the fact record, one of the two rows will be returned. For example:

scd example fact

All facts are for the same customer. When you would ask the total sales amount
for CustomerA, the result is 31. The total sales per location is 12.75 for Antwerp
and 18.25 for Brussels, even though the data is for the same customer. Using SCD
Type 2, we can analyze our data with historical attributes.

Implementation Methods

There are several methods for loading a Slowly Changing Dimension of type 2 in
a data warehouse. You could opt for a pure T-SQL approach, either with multiple
T-SQL statements or by using the MERGE statement. The latter is explained in the

Using the SQL Server MERGE Statement to Process Type 2 Slowly Changing Dimensions

With SSIS, you can use the built-in Slowly Changing Dimension wizard, which can
handle multiple scenarios. This wizard is described in the tips

Loading Historical Data into a SQL Server Data Warehouse

Handle Slowly Changing Dimensions in SQL Server Integration Services
. The downside
of this wizard is performance: it uses the OLE DB Command for every
update, which can result in poor performance for larger data sets. If you make changes
to the data flow to solve these issues, you can’t run the wizard again as
you would lose all changes.

The last option – aside from using 3rd
party components – is building the SCD Type 2 logic yourself in the data flow,
which we’ll describe in the next section.

Implementation in SSIS

The solution proposed in this tip works for any version of SSIS. We’ll
reprise the example of the customer dimension, but an extra field has been added:
the email attribute. We don’t keep history of the email addresses, so any
new value will overwrite all other values.

First, we read the data from a source, most likely a staging environment. When
using a relational source, you can use the OLE DB Source component with a SQL query
to read the data. Only select columns you actually need for your dimension. The
ValidFrom field is calculated here as well. Since you typically
load data from the previous day, ValidFrom is set to the date of yesterday. If you
don’t have a relational source, you can add this column using a Derived Column

read data from source

In the next step, we’re doing a lookup against the dimension.
Here we’ll verify if the incoming rows are either an insert or an update.
If no match is found, the row is a new dimension member and it needs to be inserted.
If a match is found, the dimension member already exists and we’ll need to
check for SCD Type 2 changes. Unless you have a very large dimension, you can use
the full cache:

ssis lookup transformation editor

Configure the lookup transformation to send non-matching rows to the
no match output
. In SSIS 2005 this option doesn’t exist yet, so you
can either use the error output, or set the transformation to ignore failures and
split out inserts and updates using a conditional split.

In the Connection pane, the following SQL query fetches the surrogate
key, the business key (CustomerName) and the SCD Type 2 columns. For each member,
only the most recent row is retrieved, by filtering on the ValidTo field.

retrieve dimension data

The Location field is renamed to Location_OLD for clarity. In the
Columns pane, match on the business key and select all other columns.

matching and retrieval

Now we can add an OLE DB Destination on the canvas. This destination will write
all new rows to the dimension. Connect the Lookup No Match Output
of the lookup transformation to the destination.

writing new rows

On the mapping pane, map the columns of the data flow with the columns of the
dimension table.

map new rows

The SK_Customer column is left unmapped, as it’s an IDENTITY column
and its values are generated by the database engine. The ValidTo column
is also left blank. New rows have no value for this column.

In part 2 of this tip we’ll continue our configuration of the data flow,
where we’ll check if a row is a type 2 update or not.

Next Steps

  • If you want to know more about implementing slowly changing dimensions in
    SSIS, you can check out the following tips:
  • You can find more SSIS development tips in

    this overview

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About the author

MSSQLTips author Koen Verbeeck

Koen Verbeeck is a BI professional, specializing in the Microsoft BI stack with a particular love for SSIS.

View all my tips

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