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Big Data Analytics

What is a DataWarehouse

A data warehouse is a relational database that is designed for query and analysis
rather than for transaction processing. It usually contains historical data derived from transaction data, but can include data from other sources. Data warehouses separate analysis workload from transaction workload and enable an organization to
consolidate data from several sources.

In addition to a relational database, a data warehouse environment can include an
extraction, transportation, transformation, and loading (ETL) solution, online analytical processing (OLAP) and data mining capabilities, client analysis tools, and
other applications that manage the process of gathering data and delivering it to
business users

A common way of introducing data warehousing is to refer to the characteristics of a
data warehouse as set forth by William Inmon:
■ Subject Oriented
■ Integrated
■ Nonvolatile
■ Time Variant

Subject Oriented
Data warehouses are designed to help you analyze data. For example, to learn more
about your company's sales data, you can build a data warehouse that concentrates on sales. Using this data warehouse, you can answer questions such as "Who was our best
customer for this item last year?" This ability to define a data warehouse by subject
matter, sales in this case, makes the data warehouse subject oriented.

Integration is closely related to subject orientation. Data warehouses must put data
from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this,
they are said to be integrated.

Nonvolatile means that, once entered into the data warehouse, data should not change. This is logical because the purpose of a data warehouse is to enable you to analyze what has occurred.

Time Variant
A data warehouse's focus on change over time is what is meant by the term time
variant. In order to discover trends in business, analysts need large amounts of data.
This is very much in contrast to online transaction processing (OLTP) systems, where
performance requirements demand that historical data be moved to an archive.

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