Type Two

Type Two – Update Record to Inactive / Create an Active Record

Type two modeling is a very reliable and straightforward technique for preserving history of changes to dimensional tables. It is commonly utilized when a full or partial set of the previous values of a dimension’s attributes must be retained. In type two modeling, every time an update occurs to any of the values in a dimensional table, a new record is physically inserted as active or current into the same table and the old records are marked inactive or historical.

The main advantage of this modeling technique is that it can accommodate and record a massive amount of history and nearly an unlimited changes to slowly changing dimensions. But this advantage also can become a significant drawback. Within this modeling technique, it is possible for dimension tables to grow to massive sizes and adversely affect both system and query performance. In addition, since upon every change this modeling technique requires an update to the linked fact table, implementation is fairly difficult and may require substantial effort to design, develop, and support.

Two unique sub-methods have been established for distinguishing the active (or current) record from the inactive (or historical) records: active flagging and tuple versioning.

In the type two – active flagging sub-method, an “Active” column is constructed within the dimensional table and acts as a flag. Flags are binary fields with permissible values being Y and N, T and F, or 1 and 0. A positive value (Y, T, 1) indicates an active record, while a negative value (N, F, 0) indicates an inactive record.

Example of Type Two – Active Flagging Sub-Method

Suppose that a vendor changes his phone number to 858-555-6555 from 202-555-8639 because the phone company has added a new area code. Using active flagging, the change to the vendor dimension table would be processed as follows…

• The current active record in the vendor dimension table is updated:: – The active flag is changed from T to F.; – The phone number field remains as 202-555-6555.
• A new record is inserted into the vendor dimension table:: – The vendor key is given the next available integer number.; – The vendor name field remains the same as in the old record.; – The phone number has the new value of 858-555-8639.; – The active flag is set to T.

In the type two – tuple versioning sub-method, start date and end date columns are included on the dimension table. The values of these date fields then define the period during which that record has been active. For the most part, the start date is the date the record has been created. Moreover, the end date is the date the record has become inactive, either because a newer record has replaced it or because the original record in the source system no longer exists. On the active record, the end date will either be left null, blank, or identified in another way such as all-nines, according to modeler’s preferences and standards.

The main advantage of the tuple versioning method over active flagging is that it provides an audit trail of the date and sequence of all updates.

Example of Type Two – Tuple Versioning Sub-Method

Continuing with the same example above but using the tuple versioning method, the updated phone number in a record in the vendor dimension table is processed as follows:

• The current active record in the vendor dimension table is updated:: – The end date is updated to the date of the change.; – The phone number field remains as 202-555-6555.
• A new record is inserted into the vendor dimension table:: – The vendor key is given the next available integer number.; – The vendor name field remains the same as in the old record.; – The phone number has the new value of 858-555-8639.; – The start date is the date the record is inserted into the table.; – The end date is intentionally left blank.

Modern data warehouse design assumes that business transactions such as sales, orders, shipments, fulfillments, and receivables can occur at a rapid rate and each the details of each transaction needs to be recorded. Hence a fact table with a dimensional model contains a separate record for each business transaction. While in contrast, the describing or text-based values of the transaction or dimension often remain fairly constant. Often, dimensional tables within the dimensional model do not take changes into account.

But in reality, dimensional values can and do change over time and numerous fields of a given row within a dimension table will need to be updated. This phenomenon in data modeling is known as “slowly changing dimensions” and it can be applied to any dimension table within a data warehouse schema. Moreover, both simple and advanced modeling techniques have been established and can be implemented for handling updates and changes within a dimension table. In addition, slowly changing dimensions assist the data warehouse in precisely recording the past values, providing an efficient method for tracking history, and allowing for the ability to respond to changes to descriptive values of transactions.

Examples of slowly changing dimensions include:: – account name; – customer phone number; – vendor address; – product description

These are good examples as they are text-based values that remain relatively constant, but can change and commonly do change over time. Names, phone numbers, addresses are fairly intuitive and it is easy to see how these values can change slowly over time. But let’s see how a product description could change… A simple ingredient change or a packaging change in a product may be so trivial that the organization does not decide to give the product a new product id. Rather the source system provides the data warehouse with a revised description of the product. Hence the data warehouse needs to track both the old and new descriptions of the product.

Other good examples of common slowly changing dimensions are the region and territory names for a sales force. Many organizations have management that rename their region and territories on a regular basis or the management realigns their regions and territories along customer purchase patterns. Typically the requirement of a data warehouse is to keep a record of the names of the regions and territories and the dates they were active.

Originally pioneered by Ralph Kimball, PhD, four main data modeling techniques have been established for managing dimension tables that contain slowly changing dimensions:

Type One – Overwrite the Record

Type Two – Update Record to Inactive / Create an Active Record

Type Three – Leverage Previous and Current Value Fields

Type Four – Insert Into a History Table

These four data modeling techniques range from the complete loss of historical data to an elegant but technically complex method of saving almost all historical data. Choice of the appropriate technique by the database designer can ensure that the data warehouse contains required historical values and allows for comparisons of current data or data from other time periods.

Posts

Slowly Changing Dimensions – Type Two Models

Type Two – Update Record to Inactive / Create an Active Record

What are Slowly Changing Dimensions?