The types of analyses performed by applications that run against your data warehouse will help you decide whether to store the data entirely in analytic workspaces or distributed between analytic workspaces and relational tables.
Analytic workspaces provide an alternative to materialized views for generating and storing aggregate data. They provide complex aggregation methods that are not available in materialized views, such as weighted calculations, non-additive methods, and models. You might also choose analytic workspaces when you have storage issues concerning aggregate data. Analytic workspaces always present fully solved data to the application, regardless of whether the data is entirely pre-aggregated, partially pre-aggregated, or entirely aggregated on demand. The flexibility of the OLAP aggregation system enables you to pre-aggregate within the limitations of your data refresh window without compromising run-time response time. Moreover, analytic workspaces can store pre-aggregated data very efficiently.
You may also prefer to use analytic workspaces for applications that support predictive analysis functions, such as models, forecasts, and what-if scenarios. Moreover, analytic workspaces are highly optimized for performing single-row calculations, which they can compute at run-time to support custom measures.
A distributed solution may be optimal for query and reporting applications that use the advanced calculation capabilities of analytic workspaces less frequently. For these types of applications, you can create and populate analytic workspaces at run-time for more intensive analysis; the results can be sent directly to the analyst or written to relational tables. The implementation of a distributed model can, of course, vary widely since it encompasses solutions that range from storing all data in relational tables to storing all data in analytic workspaces. The BI Beans can run against analytic workspaces or relational tables.
OLAP Related Interview Questions
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OLAP Related Practice Tests
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The Multidimensional Data Model
The Sample Schema
Developing Java Applications For Olap
Defining A Logical Multidimensional Model
Creating An Analytic Workspace
Sql Access To Analytic Workspaces
Exploring A Standard Form Analytic Workspace
Adding Measures To A Standard Form Analytic Workspace
Predicting Future Performance
Acquiring Data From Other Sources
Administering Oracle Olap
Materialized Views For The Olap Api
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