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Big Data Analytics

Data Warehousing Objects

Fact tables and dimension tables are the two types of objects commonly used in
dimensional data warehouse schemas.
Fact tables are the large tables in your data warehouse schema that store business
measurements. Fact tables typically contain facts and foreign keys to the dimension
tables. Fact tables represent data, usually numeric and additive, that can be analyzed and examined. Examples include sales, cost, and profit.
Dimension tables, also known as lookup or reference tables, contain the relatively
static data in the data warehouse. Dimension tables store the information you
normally use to contain queries. Dimension tables are usually textual and descriptive and you can use them as the row headers of the result set. Examples are customers
or products.

Fact Tables
A fact table typically has two types of columns: those that contain numeric facts (often called measurements), and those that are foreign keys to dimension tables. A fact table contains either detail-level facts or facts that have been aggregated. Fact tables that contain aggregated facts are often called summary tables. A fact table usually contains facts with the same level of aggregation. Though most facts are additive, they can also be semi-additive or non-additive. Additive facts can be aggregated by simple arithmetical addition. A common example of this is sales. Non-additive facts cannot be added at all. An example of this is averages. Semi-additive facts can be aggregated along some of the dimensions and not along others. An example of this is inventory levels, where you cannot tell what a level means simply by looking at it.

Requirements of Fact Tables

You must define a fact table for each star schema. From a modeling standpoint, the
primary key of the fact table is usually a composite key that is made up of all of its foreign keys.

Dimension Tables
A dimension is a structure, often composed of one or more hierarchies, that
categorizes data. Dimensional attributes help to describe the dimensional value. They
are normally descriptive, textual values. Several distinct dimensions, combined with
facts, enable you to answer business questions. Commonly used dimensions are
customers, products, and time.
Dimension data is typically collected at the lowest level of detail and then aggregated into higher level totals that are more useful for analysis. These natural rollups or aggregations within a dimension table are called hierarchies.

Hierarchies are logical structures that use ordered levels as a means of organizing
data. A hierarchy can be used to define data aggregation. For example, in a time
dimension, a hierarchy might aggregate data from the month level to the quarter level
to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure. Within a hierarchy, each level is logically connected to the levels above and below it.

Data values at lower levels aggregate into the data values at higher levels. A
dimension can be composed of more than one hierarchy. For example, in the product
dimension, there might be two hierarchies—one for product categories and one for
product suppliers.

Dimension hierarchies also group levels from general to granular. Query tools use
hierarchies to enable you to drill down into your data to view different levels of
granularity. This is one of the key benefits of a data warehouse.
When designing hierarchies, you must consider the relationships in business
structures. For example, a divisional multilevel sales organization.
Hierarchies impose a family structure on dimension values. For a particular level
value, a value at the next higher level is its parent, and values at the next lower level are its children. These familial relationships enable analysts to access data quickly.

A level represents a position in a hierarchy. For example, a time dimension
might have a hierarchy that represents data at the month, quarter, and year levels.
Levels range from general to specific, with the root level as the highest or most general level. The levels in a dimension are organized into one or more hierarchies.

Level Relationships
Level relationships specify top-to-bottom ordering of levels from
most general (the root) to most specific information. They define the parent-child
relationship between the levels in a hierarchy.

Hierarchies are also essential components in enabling more complex rewrites. For
example, the database can aggregate an existing sales revenue on a quarterly base to a yearly aggregation when the dimensional dependencies between quarter and year are

Unique Identifiers
Unique identifiers are specified for one distinct record in a dimension table. Artificial unique identifiers are often used to avoid the potential problem of unique identifiers changing. Unique identifiers are represented with the # character. For example, #customer_id.

Relationships guarantee business integrity. An example is that if a business sells
something, there is obviously a customer and a product. Designing a relationship
between the sales information in the fact table and the dimension tables products and
customers enforces the business rules in databases.

Example of Data Warehousing Objects and Their Relationships

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