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Data Warehouse: Subject Oriented

A data warehouse is a relational database designed for analysis rather than transactions that contains integrated historical data from multiple sources. It has subject-oriented data, is non-volatile so data is never altered once added, and is time-variant with all data identified by time period. Analysts use large amounts of historical data in data warehouses to understand businesses in detail.

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34 views1 page

Data Warehouse: Subject Oriented

A data warehouse is a relational database designed for analysis rather than transactions that contains integrated historical data from multiple sources. It has subject-oriented data, is non-volatile so data is never altered once added, and is time-variant with all data identified by time period. Analysts use large amounts of historical data in data warehouses to understand businesses in detail.

Uploaded by

Anonymous QMzMmdRsf
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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Data Warehouse

A data warehouse is a relational database that is designed for query and business analysis
rather than for transaction processing. It contains historical data derived from transaction data.
This historical data is used by the business analysts to understand about the business in detail.
A data warehouse should have the following characteristics
Subject oriented: A data that gives information about particular subject. For example, to know
about a company's sales, a data warehouse needs to build on sales data. Using this data
warehouse we can find the last year sales. This ability to define a data warehouse by subject
(sales) makes it a subject oriented. For example, "sales" can be a particular subject.
Integrated: Bringing data from different sources and putting them in to a consistent format. This
includes resolving the units of measures, naming conflicts etc.
data warehouse integrates data from multiple data sources. For example, source A and source B
may have different ways of identifying a product, but in a data warehouse, there will be only a
single way of identifying a product.
Non-volatile: Once the data enters into the data warehouse, the data should not be updated.
Once data is in the data warehouse, it will not change. So, historical data in a data warehouse
should never be altered.
Time variant: all data in DW is identified with particular time period.
To analyze the business, analysts need large amounts of data. So, the data warehouse should
contain historical data.
Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6
months, 12 months, or even older data from a data warehouse. This contrasts with a transactions
system, where often only the most recent data is kept. For example, a transaction system may
hold the most recent address of a customer, where a data warehouse can hold all addresses
associated with a customer.

the grain of a fact table defines the level of detail that is stored, and which dimensions are
included make up this grain

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