OLAP cube
An OLAP cube or data cube ( English OLAP cube or English data cube ), also called a cube operator , is a term used in data warehouse theory for the logical representation of data . The data is (engl. As elements of a multi-dimensional cube cube ) is arranged. The dimensions of the cube describe the data and allow easy access. Data can be selected across one or more axes of the cube. The term OLAP ( O n l ine A nalytical P rocessing ) comes from the data analysis.
This type of representation is advantageous for the analysis of data, as different aspects (dimensions) of the data are accessed in the same way. Hence the use in OLAP applications which analyze or visually process the data in a data warehouse.
Basic operations
- Slicing: cutting out slices from the data cube
- Dicing: This creates a smaller cube that contains a partial volume of the total cube. This is done through partial restrictions on one or more dimensions.
- Pivoting / Rotation: Rotating the data cube so that at least one other dimension becomes visible
- Drill-Down : Break down aggregations of an information object to detailed values; "Zoom in"
- Drill-up / roll-up: counter operation to drill-down; Consolidation to a higher hierarchy level (e.g. from the monthly to the annual view)
- Drill-Across: Dimension on the same hierarchy level; Consideration of neighboring dimension elements (different region, different product, different month)
- Drill-through: while you move vertically through the data (hierarchy) with drill-up or drill-down, drill-through is used to evaluate further OLAP cubes horizontally; sometimes drill-across is equated with drill-through
- Split: The split operator makes it possible to split a value according to several dimensions in order to determine further details (e.g. the sales of a store for a certain quantity of products)
- Merge / Drill-In: In contrast to Split, the granularity is reduced again by removing additional dimensions.
example
OLAP cubes are often used to analyze business data, such as sales, inventory, and sales. The dimensions that can be important here include, for example, time, branch, seller and product.
The cube thus represents the data (also called facts ) sales , inventory , sales depending on the dimensions of time period , branch , seller , customer and product .
The following questions can thus be answered very easily:
- How much coffee was sold in the Marburg branch last week?
- How much coffee is there in the warehouse?
- Which seller sold the most coffee?
- Which branch made the most sales last year?
Technical implementation
The data is stored in a multidimensional ( MOLAP ), relational ( ROLAP ) or hybrid configuration (HOLAP). Some systems load the data completely into the main memory during initialization in order to enable fast access. As a rule, the cube is “ sparse ”, which means that most possible intersections in the cube are not assigned values. The way software handles these parts of the cube makes a decisive contribution to the memory requirements and the performance of the respective system.
The use of a star schema is typical for relational systems . A separation is made into a fact table and several dimension tables grouped around it.