A fundamental concept of a data warehouse is the distinction between data and information.
Data is composed of observable and recordable facts that are often found in operational or transactional systems.At Rutgers, these systems include the registrar’s data on students (widely known as the SRDB), human
resource and payroll databases, course scheduling data, and data on financial aid.
In a data warehouse environment, data only comes to have value to end-users when it is organized and presented as information. Information is an integrated collection of facts and is used as the basis for decisionmaking.
For example, an academic unit needs to have diachronic information about its extent of
instructional output of its different faculty members to gauge if it is becoming more or less reliant on
part-time faculty.
The data warehouse is that portion of an overall Architected Data Environment that serves as the single
integrated source of data for processing information. The data warehouse has specific characteristics that
include the following:
Subject-Oriented: Information is presented according to specific subjects or areas of interest, not
simply as computer files. Data is manipulated to provide information about a particular subject. For
example, the SRDB is not simply made accessible to end-users, but is provided structure and organized
according to the specific needs.
Integrated: A single source of information for and about understanding multiple areas of interest. The
data warehouse provides one-stop shopping and contains information about a variety of subjects. Thus
the OIRAP data warehouse has information on students, faculty and staff, instructional workload, and
student outcomes.
Non-Volatile: Stable information that doesn’t change each time an operational process is executed.
Information is consistent regardless of when the warehouse is accessed.
Time-Variant: Containing a history of the subject, as well as current information. Historical
information is an important component of a data warehouse.
Accessible: The primary purpose of a data warehouse is to provide readily accessible information to
end-users.
Process-Oriented: It is important to view data warehousing as a process for delivery of information.
The maintenance of a data warehouse is ongoing and iterative in nature.
Data Warehouse: A data structure that is optimized for distribution. It collects and stores integrated
sets of historical data from multiple operational systems and feeds them to one or more data marts. It
may also provide end-user access to support enterprise views of data.
Data Mart: A data structure that is optimized for access. It is designed to facilitate end-user analysis of
data. It typically supports a single, analytic application used by a distinct set of workers.
Staging Area: Any data store that is designed primarily to receive data into a warehousing environment.
Operational Data Store: A collection of data that addresses operational needs of various operational
units. It is not a component of a data warehousing architecture, but a solution to operational needs.
OLAP (On-Line Analytical Processing): A method by which multidimensional analysis occurs.
Multidimensional Analysis: The ability to manipulate information by a variety of relevant categories
or “dimensions” to facilitate analysis and understanding of the underlying data. It is also sometimes
referred to as “drilling-down”, “drilling-across” and “slicing and dicing”
Hypercube: A means of visually representing multidimensional data.
Star Schema: A means of aggregating data based on a set of known dimensions. It stores data
multidimensionally in a two dimensional Relational Database Management System (RDBMS), such as
Oracle.
Snowflake Schema: An extension of the star schema by means of applying additional dimensions to the
dimensions of a star schema in a relational environment.
Multidimensional Database: Also known as MDDB or MDDBS. A class of proprietary, non-relational
database management tools that store and manage data in a multidimensional manner, as opposed to the
two dimensions associated with traditional relational database management systems.
OLAP Tools: A set of software products that attempt to facilitate multidimensional analysis. Can
incorporate data acquisition, data access, data manipulation, or any combination thereof.
Data is composed of observable and recordable facts that are often found in operational or transactional systems.At Rutgers, these systems include the registrar’s data on students (widely known as the SRDB), human
resource and payroll databases, course scheduling data, and data on financial aid.
In a data warehouse environment, data only comes to have value to end-users when it is organized and presented as information. Information is an integrated collection of facts and is used as the basis for decisionmaking.
For example, an academic unit needs to have diachronic information about its extent of
instructional output of its different faculty members to gauge if it is becoming more or less reliant on
part-time faculty.
The data warehouse is that portion of an overall Architected Data Environment that serves as the single
integrated source of data for processing information. The data warehouse has specific characteristics that
include the following:
Subject-Oriented: Information is presented according to specific subjects or areas of interest, not
simply as computer files. Data is manipulated to provide information about a particular subject. For
example, the SRDB is not simply made accessible to end-users, but is provided structure and organized
according to the specific needs.
Integrated: A single source of information for and about understanding multiple areas of interest. The
data warehouse provides one-stop shopping and contains information about a variety of subjects. Thus
the OIRAP data warehouse has information on students, faculty and staff, instructional workload, and
student outcomes.
Non-Volatile: Stable information that doesn’t change each time an operational process is executed.
Information is consistent regardless of when the warehouse is accessed.
Time-Variant: Containing a history of the subject, as well as current information. Historical
information is an important component of a data warehouse.
Accessible: The primary purpose of a data warehouse is to provide readily accessible information to
end-users.
Process-Oriented: It is important to view data warehousing as a process for delivery of information.
The maintenance of a data warehouse is ongoing and iterative in nature.
Data Warehouse: A data structure that is optimized for distribution. It collects and stores integrated
sets of historical data from multiple operational systems and feeds them to one or more data marts. It
may also provide end-user access to support enterprise views of data.
Data Mart: A data structure that is optimized for access. It is designed to facilitate end-user analysis of
data. It typically supports a single, analytic application used by a distinct set of workers.
Staging Area: Any data store that is designed primarily to receive data into a warehousing environment.
Operational Data Store: A collection of data that addresses operational needs of various operational
units. It is not a component of a data warehousing architecture, but a solution to operational needs.
OLAP (On-Line Analytical Processing): A method by which multidimensional analysis occurs.
Multidimensional Analysis: The ability to manipulate information by a variety of relevant categories
or “dimensions” to facilitate analysis and understanding of the underlying data. It is also sometimes
referred to as “drilling-down”, “drilling-across” and “slicing and dicing”
Hypercube: A means of visually representing multidimensional data.
Star Schema: A means of aggregating data based on a set of known dimensions. It stores data
multidimensionally in a two dimensional Relational Database Management System (RDBMS), such as
Oracle.
Snowflake Schema: An extension of the star schema by means of applying additional dimensions to the
dimensions of a star schema in a relational environment.
Multidimensional Database: Also known as MDDB or MDDBS. A class of proprietary, non-relational
database management tools that store and manage data in a multidimensional manner, as opposed to the
two dimensions associated with traditional relational database management systems.
OLAP Tools: A set of software products that attempt to facilitate multidimensional analysis. Can
incorporate data acquisition, data access, data manipulation, or any combination thereof.
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