The most ?exible and useful facts are fully additive; additive measures can be summed across any of the dimensions associated with the fact table. Semi-additive measures can be summed across some dimensions, but not all; checking account or savings account balance amounts are common semi-additive facts.
Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others. For example if you have the number of items in the warehouse for each day, you can sum up the items for each day (total warehouse of the day), but it make no senso to sum up in the year.
Factless facts are those fact tables that have no measures associated with the transaction. Factless facts are a simple collection of dimensional keys which define the transactions or describing condition for the time period of the fact. It is a fact, plain and simple.
A fact table is found at the center of a star schema or snowflake schema surrounded by dimension tables. A fact table consists of facts of a particular business process e.g., sales revenue by month by product. Facts are also known as measurements or metrics. A fact table record captures a measurement or a metric.
Fact table holds the measures data for measuring the performance of your business. Based on line of business, measures types in fact tables can be: fully-additive, semi-additive or non-additive. Fully-additive (short: additive) fact table holds measures that are (and can be) grouped, summed through all the dimensions.
In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. The grain of a fact table represents the most atomic level by which the facts may be defined.
Semi additive measures are measures that have a different way of aggregation over time (or other specific dimensions). For example, thinking of stock values you want to sum them up by product, location, country etc. but not by time (here you may want to see the last value, the average, the minimum/maximum or whatever).
Facts consistent with the grain of an existing fact table can be added by creating new columns. Dimensions can be added to an existing fact table by creating new foreign key columns, presuming they don't alter the fact table's grain.
According to Kimball: Dimensional models combine normalized and denormalized table structures. The dimension tables of descriptive information are highly denormalized with detailed and hierarchical roll-up attributes in the same table. Meanwhile, the fact tables with performance metrics are typically normalized.
Factless fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregatable numeric values or information. There are two types of factless fact tables: those that describe events, and those that describe conditions.
A set of joins are considered to be a star join when a fact table (large central table) is joined to two or more dimension tables (smaller tables containing descriptions of the column values in the fact table). A Star join is comprised of 3 main parts: Semijoins. Index ANDing of the results of the Semijoins.
The fact table also has a primary (composite) key that is a combination of these four foreign keys. As a rule, each foreign key of the fact table must have its counterpart in a dimension table. Additionally, any table in a dimensional database that has a composite key must be a fact table.
The fact table contains business facts (or measures), and foreign keys which refer to candidate keys (normally primary keys) in the dimension tables. Contrary to fact tables, dimension tables contain descriptive attributes (or fields) that are typically textual fields (or discrete numbers that behave like text).
The Three Types of Fact Tables
- Transaction fact tables.
- Periodic snapshot tables, and.
- Accumulating snapshot tables.
2 Answers. It can, in the form of a "degenerate dimension:" a dimension so insignificant that there's no need to create a table for it. This should be done cautiously, as they make fact table rows wider. Junk dimensions are usually a better option, if you have more than a couple degenerate dimensions.
A "fact" is a numeric value that a business wishes to count or sum. A "dimension" is essentially an entry point for getting at the facts. Dimensions are things of interest to the business. A set of level properties that describe a specific aspect of a business, used for analyzing the factual measures.
What are the types of SCD?
- Type 0 – Fixed Dimension. No changes allowed, dimension never changes.
- Type 1 – No History. Update record directly, there is no record of historical values, only current state.
- Type 2 – Row Versioning.
- Type 3 – Previous Value column.
- Type 4 – History Table.
- Type 6 – Hybrid SCD.
A Type 2 SCD retains the full history of values. When the value of a chosen attribute changes, the current record is closed. A new record is created with the changed data values and this new record becomes the current record.
Dimension: A dimension table has two types of columns, primary keys and descriptive data. For example, Time and Customer.
Junk dimensions are used to reduce the number of dimensions in the dimensional model and reduce the number of columns in the fact table. A junk dimension combines two or more related low cardinality flags into a single dimension. An example of this may be car color (red, black, blue, etc.)
Conformed dimensions are dimensions that are shared by multiple stars. They are used to compare the measures from each star schema [3]. The reuse of conformed dimensions is very common in order to “support true, cross-business process analysis” [6].
The different types of dimension tables are explained in detail below.
- Conformed Dimension: Conformed dimensions mean the exact same thing with every possible fact table to which they are joined.
- Junk Dimension:
- Degenerated Dimension:
- Role-playing dimension:
Current version: 9.2. Overview of facts and dimensions. Facts and dimensions are data warehousing terms. A fact is a quantitative piece of information - such as a sale or a download. Facts are stored in fact tables, and have a foreign key relationship with a number of dimension tables.
A row in a periodic snapshot fact table summarizes many measurement events occurring over a standard period, such as a day, a week, or a month. Periodic snapshot fact tables often contain many facts because any measurement event consistent with the fact table grain is permissible.
According to Ralph Kimball, in a data warehouse, a degenerate dimension is a dimension key in the fact table that does not have its own dimension table, because all the interesting attributes have been placed in analytic dimensions. These degenerate dimensions are natural keys of the "parents" of the line items.
an event, occurrence or state of affairs known to have happened; to be distinguished from opinion or law. Facts can however be found proven in legal proceedings where they may or may not have actually happened. Facts may also be inferred from other facts.
noun. something that actually exists; reality; truth: Your fears have no basis in fact. a truth known by actual experience or observation; something known to be true: Scientists gather facts about plant growth.
A transactional fact table is a fact table where: Each event is stored in the fact table only once. It has a date column indicating when the event occurred. It has an identifier column which identifies each event. The number of rows is the same as the source table.
Aggregate fact tables are simple numeric rollups of atomic fact table data built solely to accelerate query performance. These aggregate fact tables should be available to the BI layer at the same time as the atomic fact tables so that BI tools smoothly choose the appropriate aggregate level at query time.
Centipede fact table is a normalized fact table. Modeller may decide to normalize the fact instead of snow flaking dimensions tables. They are measures re-used across multiple dimension models. A fact table stores some kind of measurements and are captured against a specific time.
The fact table mainly consists of business facts and foreign keys that refer to primary keys in the dimension tables. A dimension table consists mainly of descriptive attributes that are textual fields. When comparing the size of the two tables, a fact table is bigger than a dimensional table.
Snowflake Schema is also the type of multidimensional model which is used for data warehouse. In
snowflake schema, The fact tables, dimension tables as well as sub dimension tables are contained.
Snowflake Schema:
| S.NO | Star Schema | Snowflake Schema |
|---|
| 9. | It has less number of foreign keys. | While it has more number of foreign keys. |
In philosophy, the concept fact is considered in epistemology and ontology. A "fact" can be defined as something that is the case—that is, a state of affairs. Facts may be understood as information that makes a true sentence true. Facts may also be understood as those things to which a true sentence refers.
