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July 2007 Archives

July 7, 2007

Oracle BI Applications and Embedded BI, Part 1

This is my first article on blogs.oracle.com.  I want to choose a topic related to what I learned lately.  This is the 4th month I joined the Oracle BI applications development.  The data warehouse and BI architecture is not new to me any more.

Oracle BI Applications is build on top of the platform from the Oracle BI Enterprise Edition.  The dashboard and report components can be seamlessly embedded into Oracle Applications, like how it is integrated with Siebel application.  However, it is also a very typical data warehouse architecture. 

Sometime, people feel that only those BI application built directly on top of the OLTP schema can be embedded into the OLTP applications and provide the real time BI.  Many also believe that only the people build the OLTP application can understand how the BI is used in the business flow.  In fact several years ago, I have both of these views. After I worked for Oracle BI Applications for a couple of months and get deeper into it, now I think that both views are wrong.

Embedding the BI into operational flow and empower everyone in the enterprise is absolutely needed.  However, the data stored in the typical OLTP schema is still need to be summarized, computed, aggregated to become meaningful business metrics.  All these processes require computer resources including the processing power and disk space.  The space growth pattern is very different from the space management for OLTP.  A data warehouse requires you to hold the historical data which can grow very fast while the data is not frequently updated.  Unlike the typical OLTP process, which typically include individual transactions and each of them add or update records in several tables, the data warehouse process involves much more data in each run and the batch process is required.  You do not want the transactional system's performance to get impacted.

A key driver is that a typically enterprise system is running a
hetergeneous environment.  The data can actually sitting in many
different places. The embedded BI solution directly based on top of the OLTP tables assumes only a single OLTP application to be used.  It is typically not the case.  The consumer apps of the "intelligence" data is very likely a different apps from the provider apps.   Even worse case is that the intelligence data can only be meaningful and valuable when you combine data from multiple systems!

(TBD)




July 13, 2007

Oracle's 11g Launch Impresses - Intelligent Enterprise

The Intelligence Enterprise's weblog has a new article about Oracle 11g.  One thing worth highlighted is the feature he feel impressed about on the advancement of OLAP.

"Materialized views are a technique for speeding multidimensional queries, such as those exploring sales across regions, products and customers. However, as the number of materialized views mounts along with query volumes and complexity, managing those views becomes difficult. In 11g, Oracle is using an OLAP cube to store up to millions of materialized views so they can be managed more efficiently."

One of the problems of the old materialized view is that  you have to know the level you are going to reported by and create the materialized view accordingly.  The benefit of using an OLAP engine is that the aggregation can be available at many different levels andyou do not need to created multiple specific materialized views for storing the summaries.   However, the OLAP cubes was stored in a different technology and populated in different languages.    You need to write codes to map the data from your source system or warehouse to the cubes.  The process needs to be run periodically in order to have the refreshed data from OLAP.  Also, querying the data from OLAP requires a different programming interface, unless you create the OLAP-to-Relational mapping view.

The new feature of 11g seems combining the best from both world and solve all the problems!

About July 2007

This page contains all entries posted to Dylan Wan in July 2007. They are listed from oldest to newest.

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