THE TWIGSTER

Types of Measure in Data Warehouse
Additive:
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. Eg: Daily balances fact can be summed up through the customers dimension but not through the time dimension.
Non-Additive:
Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table. Eg: Facts which have percentages, ratios calculated.
Hierarchy: Hierarchy defines parent-child relationships among various levels within a single dimension. For instance in a time dimension, year level is parent of four quarters, each of which is a parent of three months, which are parents of 28 to 31 days, which are parents of 24 hours. Similarly in a geography dimension a continent is a parent of countries, country could be a parent of states, and state could be a parent of cities.
Level: Level is a column within a dimension table that could be used for aggregating data. For example, product dimension could have levels of product type (beverage), product category (alcoholic beverage), product class (beer), product name (miller lite, budlite, corona, etc).
Member: Member is a value within a dimension level that can be used for aggregating and reporting data. For example each product category such as beverage, non-consumable, food, clothing, etc is a member. Each product class such as beer, wine, coke, bottled water would represent a member.
Data Mart Data Mart is a subset of the data warehouse typically serving a functional area such as marketing or finance, or particular location of the business (for instance mid-Western division).