1
Data Warehouse
Data warehouse is the storage of data collected from multiple heterogeneous sources to
be used for analytical reporting, decision making, and query, rather than for transactional
purposes. Data warehouse is created from historical data based on transaction data, but data from
other sources can also be included in the data warehouse. The data are collected from various
sources systems and after cleaning and transforming, the data are loaded into the warehouse from
where the decision makers access those for decision making analysis (Watson et al, 200). Since
early 1990s, data warehouse is being used by decision makers as the foundation of advanced
decision making support application (Shim et al, 2002). Data warehousing refers to the process
of creation of data warehouse, and integration and consolidation of data (Oracle, 2017).
March and Henver (2007) opine that the quality of managerial decision making is highly
dependent upon consolidated high quality information made available timely and in easy-to-
grasp manner. The concept of data warehouse has emerged from this crucial need of integrated
relational data (Watson and Haley, 1997). Data warehouse serves as the repository of data, both
internal and external, that is supposed to serve the purpose of understanding and evaluating the
business within its environmental context (Mendling et al, 2017). The quality of information
resources can be made really actionable with the addition of models and other technicalities like
user inter-faces and tools. The actionable resources can be used as business intelligence that
supports the functions of identification of problems and opportunities and making strategic
decision, its implementation and evaluation. Use of sophisticated online analytical processing
(OLAP) has enabled many companies to use data warehouse towards increasing sales, reducing
costs, and offering new and improved products (Cooper et al., 2000; Heun. 2000; Whiting, 1999;
Levinson. 2000; Watson and Haley, 1998).
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Data Warehouse Schema Architecture
In a data warehouse environment, the relational data are transformed into some special
architecture. Among the many schema models of data warehousing, the most commonly used
models are Star schema, Snowflake Schema, and Fact constellation Schema (Watson, Fuller and
Ariyachandra, 2004). The model to be used depends upon requirements of the project, available
tools, and preferences of the data warehousing team.
Star schema
Star schema is the simplest of all the data warehouse schemas. The name of the schema is
derived from the model’s resemblance with star. In the center of the model is the fact table and
the dimension tables are on the points of the star.
Data Warehouse
Data warehouse is the storage of data collected from multiple heterogeneous sources to
be used for analytical reporting, decision making, and query, rather than for transactional
purposes. Data warehouse is created from historical data based on transaction data, but data from
other sources can also be included in the data warehouse. The data are collected from various
sources systems and after cleaning and transforming, the data are loaded into the warehouse from
where the decision makers access those for decision making analysis (Watson et al, 200). Since
early 1990s, data warehouse is being used by decision makers as the foundation of advanced
decision making support application (Shim et al, 2002). Data warehousing refers to the process
of creation of data warehouse, and integration and consolidation of data (Oracle, 2017).
March and Henver (2007) opine that the quality of managerial decision making is highly
dependent upon consolidated high quality information made available timely and in easy-to-
grasp manner. The concept of data warehouse has emerged from this crucial need of integrated
relational data (Watson and Haley, 1997). Data warehouse serves as the repository of data, both
internal and external, that is supposed to serve the purpose of understanding and evaluating the
business within its environmental context (Mendling et al, 2017). The quality of information
resources can be made really actionable with the addition of models and other technicalities like
user inter-faces and tools. The actionable resources can be used as business intelligence that
supports the functions of identification of problems and opportunities and making strategic
decision, its implementation and evaluation. Use of sophisticated online analytical processing
(OLAP) has enabled many companies to use data warehouse towards increasing sales, reducing
costs, and offering new and improved products (Cooper et al., 2000; Heun. 2000; Whiting, 1999;
Levinson. 2000; Watson and Haley, 1998).
, 2
Data Warehouse Schema Architecture
In a data warehouse environment, the relational data are transformed into some special
architecture. Among the many schema models of data warehousing, the most commonly used
models are Star schema, Snowflake Schema, and Fact constellation Schema (Watson, Fuller and
Ariyachandra, 2004). The model to be used depends upon requirements of the project, available
tools, and preferences of the data warehousing team.
Star schema
Star schema is the simplest of all the data warehouse schemas. The name of the schema is
derived from the model’s resemblance with star. In the center of the model is the fact table and
the dimension tables are on the points of the star.
