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Snowflake schema used by example query. The example schema shown to the right is a snowflaked version of the star schema example provided in the star schema article. The following example query is the snowflake schema equivalent of the star schema example code which returns the total number of television units sold by brand and by country for 1997.
In computing, a materialized view is a database object that contains the results of a query.For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function.
Snowflake Inc. is an American cloud-based data storage company. Headquartered in Bozeman, Montana , it operates a platform that allows for data analysis and simultaneous access of data sets with minimal latency . [ 1 ]
They give a static snapshot of the data and may include data from remote sources. The accuracy of a materialized view depends on the frequency of trigger mechanisms behind its updates. Materialized views were introduced by Oracle Database, while IBM Db2 provides so-called "materialized query tables" (MQTs) for the same purpose.
Angular (also referred to as Angular 2+) [4] is a TypeScript-based free and open-source single-page web application framework. It is developed by Google and by a community of individuals and corporations. Angular is a complete rewrite from the same team that built AngularJS.
The view model is an abstraction of the view exposing public properties and commands. Instead of the controller of the MVC pattern, or the presenter of the MVP pattern, MVVM has a binder, which automates communication between the view and its bound properties in the view model. The view model has been described as a state of the data in the ...
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
When metadata is crosswalked between two or more unrelated sources, there will be data elements that cannot be reconciled in an ideal manner. The key to a successful metadata crosswalk is intelligent flexibility. It is essential to focus on the important goals and be willing to compromise to reach a practical conclusion to projects." [3]