A fact table is a data table composed of analysis fields.
Latitude table is a combination table of analysis indexes in this field.
Explanation 2:
Simply put;
A fact table is a transaction table.
The dimension table is a basic table.
Used to explain the specific content of keyword latitude in the fact table.
Explanation 3:
Fact data table
In data warehouse architecture, the central table contains numerical measures and keys that associate facts with dimension tables. Fact tables contain data describing specific events in the business, such as bank transactions or product sales.
Dimension table
In a data warehouse, a table whose entries describe the data in a fact table. A dimension table contains data on which dimensions are created.
Give another practical example. Banks keep accounts for deposits. Table A stores actual data, including account number, affiliated institution number, deposit amount, etc. Table B stores the correspondence between the institution number and the institution name. Then a is a fact table and b is a dimension table.
Fact table
Each data warehouse contains one or more fact tables. Fact sheets may contain business sales data, such as cash registration transactions.
The generated data, fact table usually contains a large number of rows. The main feature of fact table is that it contains digital data (facts), which can be summarized as historical data and provided to relevant units. Each fact table contains a multi-part index, which contains the primary key of the related latitude table as the foreign key, while the dimension table contains the characteristics of the fact record. The fact data table should not contain descriptive information, nor should it contain any data except numerical measurement fields and related index fields that make facts correspond to corresponding items in the latitude table.
There are two kinds of "measures" in fact data table: one is cumulative measure and the other is non-cumulative measure. The most useful measure is the cumulative measure, and the cumulative number is very meaningful. Users can get summary information by accumulating indicators, such as. It can summarize the sales of a group of stores in a specific period of time. Non-cumulative measures can also be used in fact tables, and the results of a single summary are generally meaningless. For example, when measuring the temperature at different locations in a building, it is meaningless to add up the temperatures at all different locations in the building, but it is meaningful to take the average value.
Generally speaking, fact tables should be associated with one or more latitude tables, and users can use one or more dimension tables when creating cubes using fact tables.
Dimension table
The dimension table can be regarded as a window for users to analyze data. Latitude tables contain the characteristics of fact records in fact tables, some of which provide descriptive information, while others specify how to summarize the data in fact tables in order to provide useful information for analysts. Dimension tables contain feature hierarchies that help summarize data. For example, a dimension table containing product information usually contains a hierarchical structure, which divides products into several categories, such as food, beverage and non-consumer goods, and each category of these products is further subdivided several times until each product reaches the lowest level.
In dimension tables, each table contains factual features independent of other dimension tables. For example, the Customer dimension table contains data about customers. Column fields in a dimension table can divide information into different structural levels.