What do you need to know about fact tables?

What do you need to know about fact tables?

Now we need to define how we measure. Fact tables are where we store these measurements. They hold business data that can be aggregated across dimension combinations. But the fact is that fact tables are not so easily described – they have flavors of their own.

How are fact tables related to query performance?

However, before we delve into what these different fact tables do, let’s talk about an important common factor: sparsity, or the proportional amount of data stored in a fact table. Sparsity is related to grain, and it has an effect on query performance.

How are numerical facts and dimension attributes related?

Remember that numerical facts usually have an implicit time series of observations, and usually participate in numerical computations such as sums and averages, or more complex functional expressions. Dimension attributes, on the other hand, are the targets of constraints, and provide the content of “row headers” (grouping columns) in a query.

Which is an example of a numerical data item?

A more ambiguous example is the limit on a coverage within an automobile insurance policy. The limit is a numerical data item, say $300,000 for collision liability. The limit may not change over the life of the policy, or it changes very infrequently. Furthermore, many queries would group or constrain on this limit data item.

How are fact tables used in data warehouse?

Now that we have established a solid foundation with dimension tables we will now turn our focus to the fact tables. Fact tables are data structures which capture the measurements of a particular business process. The measurements (quantity, amount, etc.) are defined by the collection of related dimensions.

What are foreign keys between fact and dimension tables?

All foreign keys between fact and dimension tables should be surrogate keys, not reused keys from operational data. Additive – measures that can be added across any dimension. Non-additive – measures that cannot be added across any dimension. Semi-additive – measures that can be added across some dimensions.

How are fact tables used in dimensional modeling?

Transactional fact tables are the most common fact in dimensional modeling. Transactional fact tables capture the measurement at its most atomic dimensional level, at the point-in-time of the transaction. This allows the fact table to provide robust dimensional grouping and roll-up and drill-down reporting capabilities to the business user.