Anchoring script for student Orientation in university
1.List down Industry domains where Data warehousing technologies have been deployed?
2. List and define the OLAP operations.
3. What are the data integration challenges and how to cater them?
One of the most compelling front-end applications for OLAP is a PC spreadsheet program. Below is the list of some popular operations that are supported by the multidimensional spreadsheet applications.
Takes the current aggregation level of fact values and does a further aggregation on one or more of the dimensions. Equivalent to doing GROUP BY to this dimension by using attribute hierarchy. Decreases a number of dimensions – removes row headers.
SELECT [attribute list], SUM [attribute names]
FROM [table list]
WHERE [condition list]
GROUP BY [grouping list];
Opposite of roll-up.
Summarizes data at a lower level of a dimension hierarchy, thereby viewing data in a more specialized level within a dimension. Increases a number of dimensions – adds new headers
Performs a selection on one dimension of the given cube, resulting in a sub-cube.
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Reduces the dimensionality of the cubes. Sets one or more dimensions to specific values and keeps a subset of dimensions for selected values. Dice Define a sub-cube by performing a selection of one or more dimensions. Refers to range select condition on one dimension, or to select condition on more than one dimension. Reduces the number of member values of one or more dimensions.
Pivot (or rotate)
Rotates the data axis to view the data from different perspectives. Groups data with different dimensions.
Accesses more than one fact table that is linked by common dimensions. Combines cubes that share one or more dimensions.
Drill down to the bottom level of a data cube down to its back-end relational tables. Cross-tab
Spreadsheet style row/column aggregates.