What Is OLAP?
OLAP stands for online analytical processing.
• Online: You have access to live data (rather than static data).
• Analytical processing: You can analyze your data for reporting. You can create reports that are:
– Calculation rich
– Supported by time-based analysis
– Ideal for applications with unpredictable, ad hoc query requirements
Examining an OLAP Question
• An OLAP question is a multidimensional query, as in the following:
– For each region of the world, what was the percentage change in revenue for our top 20% products, over a rolling three-month period this year compared to the same period last year?
• This is a simple business question, but the actual query can be quite complex.
Dimensional modeling (DM) is the name of a logical design technique often used for data warehouses. It is considered to be different from entity-relationship modeling (ER).
Dimensional model is the underlying data model used by many of the commercial OLAP products available today in the market. In this model, all data is contained in two types of tables called Fact Table and Dimension Table.
The multidimensional logical model has the following elements:
• Represent factual data
• Are organized by one or more dimensions
• Populate the cells of a logical cube
• Can be numeric data, text, dates, Booleans, and so on
• Measures are of two types:
• Stored measures store the result in data cells.
• Calculated measures evaluate calculated data from a formula.
• Form the “edges” of the measure
• Provide pointers to the actual cells inside the multidimensional measures
• A hierarchy is a parent-child relationship between the members of a dimension.
• Hierarchies enable logical groupings of dimension members for the purposes of:
• Navigation of data
• Aggregation of measures
• Allocation of data in a budgeting or planning application
• Certain calculations, such as shares and indexes
• Dimensions can have more than one hierarchy.
• Hierarchies enable you to navigate from the lowest level to the highest level or from the highest to the lowest.
• You can aggregate data from the lowest level to the highest level.
• Attributes provide descriptive information about the dimension members.
• Attributes are also useful when you are selecting dimension members for analysis:
• Select the products whose color (attribute) is “Blue.”
• Select the customers who have two children.
• Select the promotions that are of type “Multipack.”
• Select all time periods whose description contains “January.”
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