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It is often the type of model used to design online transaction processing systems See also normalized model Extract, transform, and load (ETL) The collection of processes that are used to prepare data for another purpose This is typically applied to data warehousing, whereby the extract process collects data from the appropriate underlying source systems The transformation processes perform cleansing, manipulation, and reorganization of the data in preparation for its intended use Finally, the load processes put the data into the data structures where it is held for data delivery While ETL processes are regularly discussed in the context of building the data warehouse, these techniques can also be used for moving and manipulating data for a variety of other purposes Fact group Part of a business dimensional model that diagrams a collection of individual facts that have identical dimensionality and level of detail (or grain) Factless fact table A fact table that captures the existence of business events that do not have an associated quantitative measurement The existence of the relationship is what is relevant Facts The fundamental measurements of the business These are captured as speci c information about a business event or transaction They are measured, monitored, and tracked over time Facts are typically the amounts and counts that show up as the body of reports Facts are used for any and all calculations that are performed Grain The level of detail showing how fact data is stored and available for analysis Hybrid OLAP (HOLAP) OLAP technology that uses both multidimensional cubes and relational databases to support business functions Information management In its simplest form, this is the work associated with collecting, maintaining, applying, and leveraging data across an organization Infrastructure A basic foundation technology that all other initiatives in the organization can rely on and use This includes basic networking services such as providing shared network drives for storing the group s les (eg, word processing documents, spreadsheets, and presentations) The existence of some sort of computer for each user can also be considered infrastructure
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Basic networking of computers, via a local area or wireless network, is another example of infrastructure Junk dimension A dimension that brings together single attributes that may or may not have any true relationship to each other in order to simplify the model, improve query performance, and/or reduce data storage Master data management (MDM) The processes and tools to help an organization consistently de ne and manage core reference or descriptive data across the organization This may involve providing a centralized view of the data to ensure that its use for all business processes is consistent and accurate Multi-dimensional OLAP (MOLAP) OLAP technology whereby the data is stored in proprietary array structures called multi-dimensional cubes See also OLAP Normalized model A data model organized to clarify pure data relationships and targeted at gaining ef ciencies in data storage and maintenance This is used for the design of transaction processing systems There are speci c rules for normalization Depending upon the number of rules followed (for different purposes) there are different forms, such as third normal form See also entity-relationship model Online analytical processing (OLAP) A collection of common business analysis functions that are dif cult to perform directly with SQL Some of the speci c functions that fall under the OLAP umbrella include time series comparisons, ranking, ratios, penetration, thresholds, and contribution to report or to the whole data population Most business intelligence tools provide this type of functionality The capabilities can be implemented in a variety of different data storage mechanisms See also MOLAP, ROLAP, HOLAP Online transaction processing (OLTP) Online transaction processing (OLTP) systems are the fundamental systems used to run the business These are also called operational systems or operational applications They are often used as sources of data for the data warehouse Operational data store (ODS) A collection of data from operational systems, most often integrated together, that is used for some operational purpose The most critical characteristic here is that this is used for some operational function This operational dependency takes precedence and the ODS should not be considered a central component of the data warehousing
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