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Cognos OLAP: Understanding performance differences

January 31, 2013 | By | Add a Comment

OLAPThis weekend, I was reading a very interesting discussion on LinkedIn, on the topic of OLAP (specifically Cognos OLAP). On this thread, ex-Cognoid Norman Bobo does an excellent job of describing the differences between Cognos PowerCubes and TM1.

In his comments, Norman describes in detail several differences between the two technologies. Here is a summary of his key points:

1) Purposes – PowerCubes are a read-only BI solution – TM1 is read/write
2) Dates – PowerCubes have the concept of date dimensions – TM1 does not
3) Levels – PowerCubes have the concept of dimension levels – TM1 does not
4) Attributes – TM1 has strong support for attributes – PowerCubes do not
5) Cube building – very different (TM1 is an in-memory solution, PowerCubes are not)
6) Data Scalability – PowerCubes do not manage large dimensions very well
7) User Scalability – TM1 is not so easily scaled
8) Cognos BI Integration – as a legacy Cognos technology, PowerCubes are more tightly integrated

(I strongly encourage you read the entire thread here)

From a User Experience perspective, I believe IBM would position their various offerings as specialized OLAP approaches to optimize for specific types of applications:

  • Cubing Services dynamic caching layer for large user communities and large data sets, with planned performance and optimization facilities. Central management and IT administration for these large OLAP applications
  • PowerCubes for large user communities and moderate data sets, with fast, consistent performance. Owned and operated by the business, mobile for broad deployment options
  • TM1 for user communities with larger highly dynamic data sets and read/write requirements. Owned and operated by the business.
  • In truth, each solution does has it’s own unique strengths and weaknesses:

    OLAP Differences
    It is also interesting to consider how each solution approaches roll ups and aggregation. Specifically:

    PowerCube – data structures are pre-aggregated:

  • Build time includes the aggregations so read times are faster
  • Calculations are more expensive at build time; not read time
  • User requests for data is retrieved with fast, consistent performance
  • No roll ups or aggregations need to be computed for user data request

  • TM1 OLAP – sources aggregate on the fly:

  • Data loads establish leaf cell values; on demand aggregations and calcs are cached
  • Large data or many calculations will affect 1st time data request performance
  • Pre-Cache ‘high-level’ views following data load for faster first time query
  • Users can alter views / hierachies or contribute data on the fly and recalculate

  • Cubing Services – optimizes the most common paths or caches as you go:

  • Pre-aggregated data using the Optimization Advisor
  • Fast first time query as optimizer routes data requests along optimized paths
  • Run time cache builds up on query results improving performance as cache builds

  • So, which is best for Read-only BI? Well, of course it depends on the customer requirements. As Norman points out in his article, data volumes and user volumes are key considerations. Some general guidelines are as follows:

    OLAP Differences
    It is certainly important to be aware of the frequency & type of reporting required by the user community, as well as the size and geographic location of the user community (e.g. centralised or decentralised). It also worth noting that Cognos internal testing clearly demonstrated that Powercubes scaled very well with increasing concurrent user volumes, whereas TM1 cubes did not.

    Other points of note:

  • TM1 may require additional hardware to scale
  • TM1 is a good option when complex calculations or roll ups are required
  • TM1 is also the engine of Cognos Metrics Studio and Cognos Express
  • PowerCubes can be built ‘in memory’ by setting up a ‘RAM disk’
  • Leverage Business Viewpoint & Cognos Framework Manager for synergy in shared dimensions (e.g. Transformer Dimension ‘re-use’) and common descriptions of data

  • Key takeaways….

  • No single OLAP style or product can meet all needs without losing either quality, performance or scalability
  • Cognos OLAP portfolio covers the full spectrum of performance management needs
  • Each is optimized for the targeted environment/applications/tasks

  • Footnote:
    Interestingly, many years ago I actually built a neural network model (using Cognos 4Thought) of PowerCube build statistics from Cognos customers around the world, in order to identify the factors which had the biggest impact on Cube build times.

    Transformer Cube Build Factors

    Transformer Cube Build Factors

    The neural network model clearly demonstrated the strong correlation between cube build times and the number of categories in the PowerCube.

    Filed in: Business Intelligence, Cognos

    Patrick Spedding

    About the Author (Author Profile)

    Patrick Spedding is Senior Director of BI R&D for Rocket Software, and IBM Champion for IBM Collaboration Solutions. He is also a Non-Executive Director on the Board of Eastside Radio in Sydney, Australia. Prior roles include Director of Product Management for IBM Cognos, Director of Field Marketing for Cognos, Founder of Tableau partner See-Change Solutions, and SAS Solution Manager for BI and Strategy Management. Patrick's qualifications include an MBA degree in Marketing (AIU), Diploma in Management (University of Michigan), BSc (Hons) in Mathematics (Loughborough University, UK), Fellow of the Australian Institute of Management (FAIM), and member of the Australian Institute of Company Directors (AICD). Find Patrick on Google+

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