Hyperion Essbase Dimension Terminology

I found the the language used in Hyperion Essbase documentation is very useful for describing the dimension hierarchy. To communicate effectively, sometime we need precise teams to describe things. The terms defined in the Hyperion Essbase documentation helps.

Essbase and IBM DB2

I read an interesting article, “IBM DB2–Minus OLAP” from the SQL Server magazine. Essbase used to be OEM-ed and re-branded by IBM as IBM DB2 OLAP server for ten years. The relationship stopped two yeas ago.
Many DB2 customers actually built their custom analytics applications on the top of Essbase.

BI Applications and Embedded BI, Part 2

This is a topic I wrote in six month ago. In the Part I of this series of articles, I mentioned that a warehouse like architecture is required in a heterogeneous environment. I want to elaborate more about this. In the future posts, I will also describe the integration technology I learned for supporting the embedded BI.

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!

Aggregation for Data Warehouse, Part 5

This is the final posting about the aggregation for data warehouse. In the prior posts, I described the following approaches of providing aggregation for data warehouse.

1. ETL and aggregated tables

2. Hand code summarization processes and aggregated tables

3. Materialized Views

I think that for a large scale data warehouse project, a two tiered approach may be favorable. When I say a two tiered approach, I mean that you can have a data warehouse, and …

Oracle acquires Hyperion

Oracle today announced its acquisition of Hyperion…