The key difference between OLAP dimensions and simple relational dimensions is the central role played by hierarchies in OLAP implementations.An OLAP dimension is strongly structured around its hierarchies, and the metadata of a cube definition includes the hierarchical levels.
This is one of the great strengths of an OLAP implementation.
OLAP is a sibling of dimensional models in the relational database, with intelligence about relationships and calculations defined on the server, that enable faster query performance and more interesting analytics from a broad range of query tools.
The recommended architecture for most purposes feeds the OLAP server from a dimensional data warehouse in the relational DBMS.
Overall the following points are important in OLAP.
1. Meta Data (like semi additive and non additive info),
2. Calculations Defined (Pre-aggregated data) and
3. Analaytical Functions defined on the Server
Example explaining the advantage of Hierarichies in OLAP
A query such as total sales for Q1 2002 is simple to formulate and should return from an OLAP server nearly instantaneously. But the user who wants total sales for an arbitrary period such as January 3 through March 12, 2002, for which no predefined hierarchy exists.
Advantages are as follows
1. It provides an intuitive user interface for browsing data.
2. It gives you spectacular query performance, primarily owing to the intelligent navigation of aggregates and partitions.
3. Parent-child dimension structures are easy and intuitive to implement.
3. It gives you server-defined rules for handling semiadditive and nonadditive measures.
The above explanation holds good for SSAS vs Sql Server Relational, Sql Server Relational vs Microstrategy,
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