Slowly Altering Dimension (SCD) in Energy BI, Half 1, Introduction to SCD

Slowly altering dimension (SCD) is an information warehousing idea coined by the superb Ralph Kimball. The SCD idea offers with transferring a selected set of information from one state to a different. Think about a human sources (HR) system having an Worker desk. As the next picture exhibits, Stephen Jiang is a Gross sales Supervisor having ten gross sales representatives in his crew:

SCD in Power BI, Stephen Jiang is the sales manager of a team of 10 sales representatives
Picture 1: Stephen Jiang is the gross sales supervisor of a crew of 10 gross sales representatives

Right now, Stephen Jiang received his promotion to the Vice President of Gross sales position, so his crew has grown in dimension from 10 to 17. Stephen is identical individual, however his position is now modified, as proven within the following picture:

SCD in Power BI, Stephen's team after he was promoted to Vice President of Sales
Picture 2: Stephen’s crew after he was promoted to Vice President of Gross sales

One other instance is when a buyer’s tackle adjustments in a gross sales system. Once more, the client is identical, however their tackle is now totally different. From an information warehousing standpoint, we’ve totally different choices to cope with the information relying on the enterprise necessities, main us to several types of SDCs. It’s essential to notice that the information adjustments within the transactional supply techniques (in our examples, the HR system or a gross sales system). We transfer and rework the information from the transactional techniques through ETL (Extract, Transform, and Load) processes and land it in an information warehouse, the place the SCD idea kicks in. SCD is about how adjustments within the supply techniques replicate the information within the information warehouse. These sorts of adjustments within the supply system don’t occur fairly often therefore the time period slowly altering. Many SCD sorts have been developed over time, which is out of the scope of this put up, however to your reference, we cowl the primary three sorts as follows.

SCD sort zero (SCD 0)

With any such SCD, we ignore all adjustments in a dimension. So, when an individual’s residential tackle adjustments within the supply system (an HR system, in our instance), we don’t change the touchdown dimension in our information warehouse. In different phrases, we ignore the adjustments throughout the information supply. SCD 0 is additionally known as mounted dimensions.

SCD sort 1 (SCD 1)

With an SCD 1 sort, we overwrite the outdated information with the brand new. A wonderful instance of an SCD 1 sort is when the enterprise doesn’t want the client’s outdated tackle and solely must preserve the client’s present tackle.

SCD sort 2 (SCD 2)

With any such SCD, we preserve the historical past of information adjustments within the information warehouse when the enterprise must preserve the outdated and present information. In an SCD 2 state of affairs, we have to keep the historic information, so we insert a brand new row of information into the information warehouse each time a transactional system adjustments. A change within the transactional system is among the following:

  • Insertion: When a brand new row inserted into the desk
  • Updating: When an current row of information is up to date with new information
  • Deletion: When a row of information is faraway from the desk

Let’s proceed with our earlier instance of a Human Useful resource system and the Worker desk. Inserting a brand new row of information into the Worker dimension within the information warehouse for each change throughout the supply system causes information duplications within the Worker dimensions within the information warehouse. Subsequently we can not use the EmployeeKey column as the first key of the dimension. Therefore, we have to introduce a brand new set of columns to ensure the distinctiveness of each row of the information, as follows:

  • A brand new key column that ensures rows’ uniqueness within the Worker dimension. This new key column is solely an index representing every row of information saved in an information warehouse dimension. The brand new secret is a so-called surrogate key. Whereas the Surrogate Key ensures every row within the dimension is exclusive, we nonetheless want to keep up the supply system’s major key. By definition, the supply system’s major keys are actually known as enterprise keys or alternate keys within the information warehousing world.
  • Begin Date and an Finish Date column symbolize the timeframe throughout which a row of information is in its present state.
  • One other column exhibits the standing of every row of information.

SCD 2 is probably the most widespread sort of SCD. After we create the required columns

Let’s revisit our state of affairs when Stephen Jiang was promoted from Gross sales Supervisor to Vice President of Gross sales. The next screenshot exhibits the information within the Worker dimensions within the information warehouse earlier than Stephen received the promotion:

SCD in Power BI, The employee data before Stephen was promoted
Picture 3: The worker information earlier than Stephen was promoted

The EmployeeKey column is the Surrogate Key of the dimension, and the EmployeeBusinessKey column is the Enterprise Key (the first key of the client within the supply system); the Begin Date column exhibits the date Stephen Jiang began his job as North American Gross sales Supervisor, the Finish Date column has been left clean (null), and the Standing column exhibits Present. Now, let’s take a look on the information after Stephen will get the promotion, which is illustrated within the following screenshot:

SCD in Power BI, The employee data after Stephen gets promoted
Picture 4: The worker information after Stephen will get promoted

Because the above picture exhibits, Stephan Jiang began his new position as Vice President of Gross sales on 13/10/2012 and completed his job as North American Gross sales Supervisor on 12/10/2012. So, the information is reworked whereas transferring from the supply system into the information warehouse. As you see, dealing with SCDs is among the most important duties within the ETL processes.

Let’s see what SCD 2 means in relation to information modeling in Energy BI. The primary query is: Can we implement SCD 2 immediately in Energy BI Desktop with out having an information warehouse? To reply this query, we should keep in mind that we all the time put together the information earlier than loading it into the mannequin. Alternatively, we create a semantic layer when constructing an information mannequin in Energy BI. In a earlier put up, I defined the totally different parts of a BI resolution, together with the ETL and the semantic layer. However I repeat it right here. In a Energy BI resolution, we handle the ETL processes utilizing Energy Question, and the information mannequin is the semantic layer. The semantic layer, by definition, is a view of the supply information (often an information warehouse), optimised for reporting and analytical functions. The semantic layer is to not substitute the information warehouse and isn’t one other model of the information warehouse both. So the reply is that we can not implement the SCD 2 performance purely in Energy BI. We have to both have an information warehouse protecting the historic information, or the transactional system has a mechanism to assist sustaining the historic information, comparable to a temporal mechanism. A temporal mechanism is a characteristic that some relational database administration techniques comparable to SQL Server provide to offer details about the information stored in a desk at any time as an alternative of protecting the present information solely. To study extra about temporal tables in SQL Server, verify this out.

After we load the information into the information mannequin in Energy BI Desktop, we’ve all present and historic information within the dimension tables. Subsequently, we’ve to watch out when coping with SCDs. As an example, the next screenshot exhibits reseller gross sales for workers:

SCD in Power BI, SCD in Power BI, Reseller sales for employees without considering SCD
Picture 5: Reseller gross sales for workers with out contemplating SCD

At a primary look, the numbers appear to be right. Nicely, they could be proper; they could be fallacious. It is determined by what the enterprise expects to see on a report. Have a look at Picture 4, which exhibits Stephen’s adjustments. Stephen had some gross sales values when he was a North American Gross sales Supervisor (EmployeeKey 272). However after his promotion (EmployeeKey 277), he’s not promoting anymore. We didn’t take into account SCD once we created the previous desk, which suggests we take into account Stephen’s gross sales values (EmployeeKey 272). However is that this what the enterprise requires? Does the enterprise anticipate to see all staff’ gross sales with out contemplating their standing? For extra readability, let’s add the Standing column to the desk.

SCD in Power BI, Reseller sales for employees and their status without considering SCD
Picture 6: Reseller gross sales for workers and their standing with out contemplating SCD

What if the enterprise must solely present gross sales values just for staff when their standing is Present? In that case, we must issue the SCD into the equation and filter out Stephen’s gross sales values. Relying on the enterprise necessities, we would want so as to add the Standing column as a filter within the visualizations, whereas in different instances, we would want to change the measures by including the Begin DateFinish Date, and Standing columns to filter the outcomes. The next screenshot exhibits the outcomes once we use visible filters to take out Stephen’s gross sales:

SCD in Power BI, SCD in Power BI, Reseller sales for employees considering SCD
Picture 7: Reseller gross sales for workers contemplating SCD

Coping with SCDs is just not all the time so simple as this. Generally, we have to make some adjustments to our information mannequin.

So, do all of the above imply we can not implement any kinds of SCDs in Energy BI? The reply, as all the time, is “it relies upon.” In some eventualities, we will implement an answer just like the SCD 1 performance, which I clarify in one other weblog put up. However we’re out of luck in implementing the SCD 2 performance purely in Energy BI.

Have you ever used SCDs in Energy BI, I’m curious to know in regards to the challenges you confronted. So please share you ideas within the feedback part under.

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