Prior to the 10g release of Discoverer, companies that wanted to present Oracle OLAP data had to develop their own applications using BI Beans, typically in JDeveloper. With recent releases, there are now several choices for front-ends that display Oracle OLAP data: Discoverer 10g, Custom-built BI Beans, Spreadsheet Add-in for OLAP, OLAP Web Agent, or 3rd party tools.
In Oracle OLAP, Oracle has separated the physical storage of data warehouse data from the logical analysis of OLAP applications. What's the magic that makes this work? How does this impact performance? What operations are precluded by various design decisions? Why would you want to use Oracle OLAP or BI Beans instead of simply using regular views or relational tables?
In Oracle10g, Oracle has introduced significant new features in the OLAP Engine. These new features increase the possibility of creating many sorts of business intelligence applications, such as collaborative budgeting, collaborative forecasting, predictive analysis, and modeling applications. All of this is now available within the Oracle 10g database.
Are you considering building a business intelligence/data warehousing solution? Are you experiencing integration pain from a "best of breed" solution or a metadata mess? Who has the latest data? What impact will this change have? How can you turn your queries into reports? How do you take advantage of the latest Oracle Database features?
When building business intelligence applications data is important, but the key piece is making the data easily accessible so that the end-user can use it to improve upon the business. This session explains how developers can take advantage of their existing J2EE and data warehouse servers as a platform to create extremely powerful interactive business intelligence applications, which can be used for guided analyses by management.
When would you want to use Discoverer and when would you want to code an application directly in BI Beans? This presentation will contrast Discoverer 10g with custom-built BI Beans applications and discuss why you should consider these products.
Are you considering building a business intelligence/data warehousing solution? Are you experiencing integration pain from a "best of breed" solution or a metadata mess? Who has the latest data? What impact will this change have? How can you turn your queries into reports? How do you take advantage of the latest Oracle Database features?
Are you considering building a business intelligence/data warehousing solution? Are you experiencing integration pain from a "best of breed" solution or a metadata mess? Who has the latest data? What impact will this change have? How can you turn your queries into reports? How do you take advantage of the latest Oracle Database features?
This presentation will describe the components of Oracle OLAP. Armed with information answering the questions "What is an analytic workspace?" and "What metadata is required to support the Java OLAP API and BI Beans?", a designer will be able to design a data warehouse or data mart for use by a business intelligence application.
This presentation will produce an actual, completed, OLAP-ready datamart, with a working BI application. It will address the steps necessary and options in building a Oracle OLAP cubes, including using Oracle Warehouse Builder, Oracle Enterprise Manager, and Analytic Workspace Manager. It will then move into developing simple and complex applications using JDeveloper, and BI Beans.
In Oracle9i, Oracle has separated the physical storage of data warehouse data from the logical analysis of OLAP applications. Physically, data can be stored in relational tables, or in Analytic Workspaces (multidimensional storage). Analytic Workspaces make possible all sorts of interesting business intelligence applications, such as collaborative budgeting, collaborative forecasting, predictive analysis, and modeling applications.
The Oracle OLAP Spreadsheet Add-In provides access to Oracle OLAP data from within Microsoft Excel. This presentation will demonstrate how this add-in can provide users access to very powerful analytics within an application they are already comfortable using.
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