What is a semi additive measure?
A semi-additive measure is one that is to be summed for some dimensions, but should not be summed across some other dimensions. For the dimensions over which the measure is not additive, a different aggregation rule must be specified.
Is time a semi additive fact?
Semi-additive calculations are the hardest ones: a semi-additive measure uses SUM to aggregate over some dimensions and a different aggregation over other dimensions – a typical example being time. As an example, we use a model that contains the balance of current accounts.
Can semi additive fact be added across all dimensions?
Semi-additive measures can be summed across some dimensions, but not all; balance amounts are common semi-additive facts because they are additive across all dimensions except time. Finally, some measures are completely non-additive, such as ratios.
What is the difference between additive semi additive and non-additive facts provide one example for each type of fact?
Semi-additive measures can be aggregated across some dimensions, but not all dimensions. For example, measures such as head counts and inventory are considered semi-additive. Non-additive measures are measures that cannot be aggregated across any of the dimensions.
Why is inventory semi additive?
We call this behavior semi-additive because we cannot add things up like we normally would across time periods that roll up into our quarter (if that’s the grain of our analysis) but we can add things up across let’s say stores and products.
Is temperature an additive fact?
For instance, temperature is a non- additive attribute that cannot be meaningfully added with other temperatures; however, it is unlikely that someone will mistakenly misinterpret a query that sums temperatures together.
What are the kinds of facts?
There are three types of facts: Summative facts: Summative facts are used with aggregation functions such as sum (), average (), etc. Semi summative facts: There are small numbers of quasi-summative fact aggregation functions that will apply.
What is factless fact table and where do we need to create this table?
A factless fact table is a fact table that does not have any measures. It is essentially an intersection of dimensions (it contains nothing but dimensional keys). There are two types of factless tables: One is for capturing an event, and one is for describing conditions.
Which of the following is an additive fact?
The most flexible and useful facts are fully additive; additive measures can be summed across any of the dimensions associated with the fact table. An example of a fully additive measure is sales (purchases from a store). You can add hourly sales to get the sales for a day, week, month, quarter, or year.
Why is inventory semi-additive?
Which are two types of fact?
Types of Facts
- Summative facts: Summative facts are used with aggregation functions such as sum (), average (), etc.
- Semi summative facts: There are small numbers of quasi-summative fact aggregation functions that will apply.
What’s the difference between additive and semi-additive facts?
Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table. Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.
What are semi additive facts in data warehouse?
Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others. Eg: Daily balances fact can be summed up through the customers dimension but not through the time dimension.
Which is an example of an additive fact?
Additive facts are facts that can be summed up through all of the dimensions in the fact table. A sales fact is a good example for additive fact. Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others.
How to do semi additive calculations in SQL?
To solve semi-additive calculations, you can use the LASTDATE function which comes with a strong limitation: it only searches in the date table because it fails to find the most desirable outcome, which is the last date with transactions.