What is the method for designing an Individual Fact Table?

Data MiningDatabaseData Structure

There are the following methods for designing an Individual Fact Table which is as follows −

Choosing the Data Mart − It can be choosing the data mart in the simplest method is the same as choosing the legacy source of information. Typical data marts involve purchase orders, shipments, retail sales, payments, or user connections. These can be an instance of single-source data marts.

In some cases, it can define a data mart that should contain multiple-legacy sources. The instance of a multiple-source data mart is user profitability, where legacy sources that define revenue should be combined with legacy sources that represent costs.

It is powerfully that the data warehouse designer limit risk by performing only single-source data marts at first, to decrease the number of lengthy extract system development functions. It can also prescribe implementing these independent data marts only in the context of a group of conformed dimensions, therefore the data marts can plug into the data warehouse bus.

Declaring the Fact Table Grain − It is essential to represent very clearly exactly what a fact table data is in the suggested dimensional design. The design cannot proceed, and without a clear description, the data architects will misuse valuable time arguing about what a dimension is and what a fact is.

The fact table grain is preferred to be as low, or as granular, as available. There are several benefits to selecting a low-level grain, including single transaction, or single day snapshot, or single document line item.

The lower the method of granularity, the more powerful the design. It can be the view that a low method of granularity is far superior at responding to unexpected new queries and far superior at responding to the establishment of more new data elements than the larger method of granularity.

Choosing the Dimensions − Because the grain of the fact table is firmly created, the choice of dimensions is moderately straightforward. The grain will often decide a primary or token set of dimensions. For example, the token set of dimensions for a line element on an order has to contain the order date, the user, the product, and an appropriate degenerate dimension including only the order number.

The fact table in a dimensional model is a group of simultaneous measurements at a specific granularity. The general measurements are numeric, but they don’t have to be numeric.

Choosing the Facts − The grain of the fact table also enables the single facts to be chosen, and it creates it clear what the scope of these facts must be.

raja
Updated on 09-Feb-2022 13:14:47

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