the truths about dating and mating pdf - Consolidating data warehouse tables

The sources could be internal operational systems, a central data warehouse, or external data.Denormalization is the norm for data modeling techniques in this system.

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Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.

Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities.

The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts.

The combination of facts and dimensions is sometimes called a star schema. The main source of the data is cleansed, transformed, catalogued, and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.

OLTP systems emphasize very fast query processing and maintaining data integrity in multi-access environments.

For OLTP systems, effectiveness is measured by the number of transactions per second. The schema used to store transactional databases is the entity model (usually 3NF).However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system.Many references to data warehousing use this broader context.The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse".In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments.Queries are often very complex and involve aggregations.

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