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This chapter focuses on data warehouse data model tuning techniques.
The system of record of data warehouse data is ideally defined in a metadata repository.
The chapter examines the different data models, especially the basics of the data warehouse data model.
It describes how to build data warehouse data models and how to relate data warehouse entities to each other.
Maintaining the consistency of warehouse data is challenging, especially if the data sources are autonomous and views of the data at the warehouse span multiple sources.
A well-known challenge in data warehousing is the efficient incremental maintenance of warehouse data in the presence of source data updates.
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Data warehouse integrates data from different data sources into large repositories.
Although an application such as Netflix in its infancy may start out with a fully customizable computing cluster environment, as its customer base and data requirements expand, a full warehouse data-centre infrastructure is often inevitable [30].
Most recently, it warehoused data servers during the dot-com boom.
They bet on properties as diverse as office buildings, warehouses, data centers and timberlands.
It is divided into seven elements such as data warehousing, data preprocessing, feature subset selection, modeling, model evaluation, model updating and model release.
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