An Online Analytical Processing (OLAP) cube is a powerful tool to analyze large amounts of data quickly. There are a number of products available that can create an OLAP cube. This article will discuss some of the factors that should be considered in choosing an OLAP technology and evaluate two major vendors’ products – Microsoft SQL Server Analysis Services 2008 (SSAS) and IBM Cognos PowerCube.
Three factors that are important in considering OLAP technologies are:
1. Your existing database and reporting technologies.
2. The amount of data that will be brought into the cube.
3. The skill set of workers that will design and create cubes.
Microsoft SQL Server
If your data warehouse platform is based on Microsoft SQL Server, especially if you are also using SQL Server Integration Services (SSIS), then SSAS is a natural extension of the architecture. SSIS can used to load and process the SSAS cube. SQL Server data will be optimized for loading into the SSAS cube. (It is important to note that SSAS can also source from Oracle, DB2, Access and Teradata data sources.) This Microsoft database environment can also be used as source data for an IBM Cognos cube, but the Cognos cube cannot be used with Microsoft reporting tools such as Reporting Services. IBM Cognos does have a software product that allows Powercube browsing with Excel; Microsoft SSAS cubes can be browsed natively with Excel pivot tables.
If your portal and collaboration solution is Microsoft SharePoint there, are exciting features in SharePoint 2010 that combines PerformancePoint and new PowerPivot software. These will leverage SSAS cubes and will not support IBM Cognos Powercubes.
IBM Cognos environment
If your database platform is not Microsoft and your reporting environment is purely IBM Cognos Business Intelligence, then using Powercubes is a natural extension. Analysis Services cubes may also be used in this environment if other considerations make this desirable, but all things being equal, Cognos is a better fit.
Size of data
The size of the source data and resulting cube is of paramount importance in your choice. Cognos Powercubes have an inherent limit of 2 Gb, although there are workaround techniques. Microsoft SSAS cubes are commonly 300-400 Gb in size with source data measured in terabytes. Multi-terabyte SSAS cubes are in use today. SSAS also gives the ability to use relational tables for aggregation (known as ROLAP) or a hybrid (known as HOLAP). This allows for even more scalability. For large amounts of data, Microsoft is a clear winner.
Microsoft SSAS requires a more technical skill set for developers than IBM Cognos Powercubes. Microsoft cubes will require a working knowledge of the multi-dimensional language, MDX. For developers that know SQL it will look similar, but is a paradigm shift analogous to moving from procedural languages to object oriented languages. There will be a learning curve. The return for that investment is much more flexibility, programmability and extensibility.
IBM Cognos Transformer, the software used to design a Powercube, was designed with the developer or power user in mind. IBM envisions strong financial analysts creating their own cubes. The result is simpler and easier to use but lacks the rich capabilities present in SSAS. Organizations with a medium amount of data and limited technical resources can build solutions quicker using IBM Cognos cubes.
In conclusion, Microsoft SQL Server Analysis Services will provide more scalability, extensibility and functionality with a cost of complexity and learning curve. This makes it a more strategic choice and IBM Cognos Powercubes a tactical choice in the appropriate situations. Powercubes are best used with smaller sets of data in an existing Cognos environment and when a less advanced technical skill set is required by the designers.