Приклади вживання Star schema Англійська мовою та їх переклад на Українською
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Star schema and ways of filling a corner.
Snowflaking" is a method of normalizing the dimension tables in a star schema.
In fact, the star schema is considered a special case of the snowflake schema. .
The snowflake schema is in the same family as the star schema logical model.
A star schema that has many dimensions is sometimes called a centipede schema. .
It is a hybrid approach encompassing thebest of breed between 3rd normal form(3NF) and star schema.
Star schema is also not as flexible in terms of analytical needs as a normalized data model.
Typically these relationships are simplified in star schema to conform to the simple dimensional model.
The star schema consists of one or more fact tables referencing any number of dimension tables.
The cube metadata may be created from a star schema or snowflake_schema of tables in a relational database.
The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries.
It is located at the center of a star schema or a snowflake schema surrounded by dimension tables.
Star schemas tend to be more purpose-built for a particular view of the data, thus not really allowing more complex analytics.
Fast aggregations- the simpler queries against a star schema can result in improved performance for aggregation operations.
The Data Vault can handle massive sets of granular data in a smaller,more normalized physical space in comparison to both 3NF and star schema.
At the centre of the star schema is a fact table that contains key data on which queries are made.
Notice that the snowflake schema query requires many more joins than the star schema version in order to fulfill even a simple query.
The main disadvantage of the star schema is that data integrity is not enforced well since it is in a highly de-normalized state.
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.
The following examplequery is the snowflake schema equivalent of the star schema example code which returns the total number of units sold by brand and by country for 1997.
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.
However, in the snowflake schema, dimensions are normalized into multiple related tables, whereas the star schema's dimensions are denormalized with each dimension represented by a single table.
The star schema gets its name from the physical model's resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star's points.
Example: One million sales transactions in 300 shops in 220countries would result in 1,000,300 records in a star schema(1,000,000 records in the fact table and 300 records in the dimensional table where each country would be listed explicitly for each shop in that country).
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. .
The snowflake schema provides some advantages over the star schema in certain situations, including: Some OLAP multidimensional database modeling tools are optimized for snowflake schemas.
Feeding cubes- star schemas are used by all OLAP systems to build proprietary OLAP 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.
Some basic knowledge of data warehouse schema topology(including star and snowflake schemas). .
Basic knowledge of data warehouse schema topology(including star and snowflake schemas). .
Some basic knowledge of data warehouse schema topology(including star and snowflake schemas). .