Incomputing, thestar schema orstar model is the simplest style ofdata martschema and is the approach most widely used to develop data warehouses and dimensional data marts.^[1] The star schema consists of one or morefact tables referencing any number ofdimension tables. The star schema is an important special case of thesnowflake schema, and is more effective for handling simpler queries.^[2]

The star schema gets its name from thephysical model's^[3] resemblance to astar shape with a fact table at its center and the dimension tables surrounding it representing the star's points.

The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed and weight measurements. Related dimension attribute examples include product models, product colors, product sizes, geographic locations, and salesperson names.

A star schema that has many dimensions is sometimes called acentipede schema.^[4] Having dimensions of only a few attributes, while simpler to maintain, results in queries with many table joins and makes the star schema less easy to use.

Fact tables record measurements or metrics for a specific event. Fact tables generally consist of numeric values, and foreign keys to dimensional data where descriptive information is kept.^[4]Fact tables are designed to a low level of uniform detail (referred to as "granularity" or "grain"), meaning facts can record events at a very atomic level. This can result in the accumulation of a large number of records in a fact table over time. Fact tables are defined as one of three types:

• Transaction fact tables record facts about a specific event (e.g., sales events)
• Snapshot fact tables record facts at a given point in time (e.g., account details at month end)
• Accumulating snapshot tables record aggregate facts at a given point in time (e.g., total month-to-date sales for a product)

Fact tables are generally assigned asurrogate key to ensure each row can be uniquely identified.This key is a simple primary key.

Dimension tables usually have a relatively small number of records compared to fact tables, but each record may have a very large number of attributes to describe the fact data. Dimensions can define a wide variety of characteristics, but some of the most common attributes defined by dimension tables include:

• Time dimension tables describe time at the lowest level of time granularity for which events are recorded in the star schema
• Geography dimension tables describe location data, such as country, state, or city
• Product dimension tables describe products
• Employee dimension tables describe employees, such as sales people
• Range dimension tables describe ranges of time, dollar values or other measurable quantities to simplify reporting

Dimension tables are generally assigned asurrogate primary key, usually a single-column integer data type, mapped to the combination of dimension attributes that form the natural key.

Star schemas aredenormalized, meaning the typical rules of normalization applied to transactional relational databases are relaxed during star-schema design and implementation. The benefits of star-schema denormalization are:

• Simpler queries – star-schema join-logic is generally simpler than the join logic required to retrieve data from a highly normalized transactional schema.
• Simplified business reporting logic – when compared to highly normalized schemas, the star schema simplifies common business reporting logic, such as period-over-period and as-of reporting.
• Query performance gains – star schemas can provide performance enhancements for read-only reporting applications when compared to highlynormalized schemas.
• Fast aggregations – the simpler queries against a star schema can result in improved performance for aggregation operations.
• Feeding cubes – star schemas are used by allOLAP systems to build proprietaryOLAP cubes efficiently; in fact, most major OLAP systems provide aROLAP mode of operation which can use a star schema directly as a source without building a proprietary cube structure.

Consider a database of sales, perhaps from a store chain, classified by date, store and product. The image of the schema to the right is a star schema version of the sample schema provided in thesnowflake schema article.

Fact_Sales is the fact table and there are three dimension tablesDim_Date,Dim_Store andDim_Product.

Each dimension table has a primary key on itsId column, relating to one of the columns (viewed as rows in the example schema) of theFact_Sales table's three-column (compound) primary key (Date_Id,Store_Id,Product_Id). The non-primary keyUnits_Sold column of the fact table in this example represents a measure or metric that can be used in calculations and analysis. The non-primary key columns of the dimension tables represent additional attributes of the dimensions (such as theYear of theDim_Date dimension).

For example, the following query answers how many TV sets have been sold, for each brand and country, in 1997:

SELECTP.Brand,S.CountryASCountries,SUM(F.Units_Sold)FROMFact_SalesFINNERJOINDim_DateDON(F.Date_Id=D.Id)INNERJOINDim_StoreSON(F.Store_Id=S.Id)INNERJOINDim_ProductPON(F.Product_Id=P.Id)WHERED.Year=1997ANDP.Product_Category='tv'GROUPBYP.Brand,S.Country

• Data warehouse
• Fact constellation
• Online analytical processing
• Reverse star schema
• Snowflake schema

• Stars: A Pattern Language for Query Optimized Schema

• Data warehousing
• Data modeling

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• Short description matches Wikidata