Metadata is used throughout Oracle OLAP to define a logical multidimensional model:
You only need to describe your source data; the OLAP tools can generate the equivalent metadata for the analytic workspace and the workspace views. The logical model is transformed along with the data. Figure shows the metadata transformations performed by the OLAP tools. These metadata types are discussed in this chapter.
Transformation of the Logical Model
Creating Metadata for Your Source Data
Defining the logical model is the first stage of metadata creation; the second stage is mapping the logical objects to physical data sources. Different types of metadata have different requirements for the storage format of the source data; you must choose the method that is appropriate for your data source. Moreover, there are multiple methods of creating metadata, including graphical user interfaces and PL/SQL APIs.
For Source Data in a Basic Star or Snowflake Schema
The CWM1 write APIs, which are used by the OLAP Management tool, create a database dimension object for each logical OLAP dimension. The database dimension object imposes the following restrictions on dimension tables and the related fact tables of a star or snowflake schema:
If your source data is a star or snowflake schema and conforms to these additional requirements, then you can use either Oracle Enterprise Manager or the CWM2 APIs, depending on your personal preference. The OLAP Management tool in Oracle Enterprise Manager provides a graphical user interface. The CWM2 APIs enable you to generate a SQL program that you can easily modify and port to other databases. If your source data is a star or snowflake schema that does not conform with these requirements, then use the CWM2 APIs. Figure shows the tools for creating OLAP Catalog metadata.
Tools for Creating OLAP Catalog Metadata for Source Data
This chapter introduces the OLAP Management tool in Oracle Enterprise Manager and the CWM2 APIs.
For Dimension Tables with Complex Hierarchies
If your source data is a star or snowflake schema, but the dimension tables include any of the following variations, then use the CWM2 APIs:
If your schema contains parent-child dimension tables, then you must convert them to level-based dimension tables. The CWM2 write APIs include a package for this transformation.
For Other Schema Configurations
If you are using Oracle Warehouse Builder already to transform your data, then generating an analytic workspace takes only a few additional steps. Warehouse Builder provides a graphical interface for designing a logical model, and deploys the model as metadata. When you use the OLAP Bridge in Warehouse Builder, it generates CWM1 metadata from its Design Repository. Warehouse Builder also creates and populates an analytic workspace, and enables it for use by the BI Beans. If your data is stored in flat files or SQL tables, then you can use a manual method described in this guide. This method enables you to use the OLAP Catalog, but requires you to write data loading programs in the OLAP DML. If you are upgrading from Oracle Express, then you may be able to automate the conversion process.
Creating Metadata for Your Analytic Workspace
The tools for creating analytic workspaces comply with the requirements of database standard form, and transform the source metadata into standard form metadata. You do not need to perform any extra steps to maintain the standard form metadata when you use the OLAP tools to maintain the analytic workspace. You can make changes to the logical model in the metadata for the data source, and the refresh tool makes the appropriate changes to the standard form metadata. However, if you make manual changes to your analytic workspace, such as adding a measure, then you are responsible for making the appropriate changes to the standard form metadata.
Creating Metadata for Your Applications
Applications that use the BI Beans require OLAP Catalog metadata, and those that use Discoverer require an End User Layer. Both types of metadata require the data source to be in relational tables or views for SQL access. Thus, the enablers in Analytic Workspace Manager for these types of applications generate views of analytic workspace objects in the format required by the metadata, and then generate the metadata itself. The enablers transform the standard form metadata provided in the analytic workspace; you do not need to redefine the logical model.
OLAP Related Interview Questions
|Informatica Interview Questions||Data Warehouse ETL Toolkit Interview Questions|
|PL/SQL Interview Questions||Data Warehousing Interview Questions|
|Testing Tools Interview Questions||SQL Database Interview Questions|
|MySQL Interview Questions||ERP Tools Interview Questions|
|Oracle 11g Interview Questions||Hyperion Financial Management Interview Questions|
|Hyperion Essbase 5 Interview Questions||Database Design Interview Questions|
|Data modeling Interview Questions||Oracle Hyperion Planning Interview Questions|
|Biztalk Esb Toolkit Interview Questions|
OLAP Related Practice Tests
|Informatica Practice Tests||PL/SQL Practice Tests|
|Data Warehousing Practice Tests||Testing Tools Practice Tests|
|SQL Database Practice Tests||MySQL Practice Tests|
|ERP Tools Practice Tests||Oracle 11g Practice Tests|
|Hyperion Financial Management Practice Tests||Hyperion Essbase 5 Practice Tests|
|Database Design Practice Tests|
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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