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OLAP cubes are among the most powerful resources available for business intelligence and analytics. Here's what the product manager for Microsoft Analysis Services said about OLAP back in 2002:
OLAP multidimensional databases combine incredible performance with unsurpassed analytical power and, in my opinion, are the foundation of the BI platform.
The multidimensional data model is vastly superior to the relational data model when it comes to the expressiveness of analytical operations. The ability to have random access to any point in space, both detailed data and aggregates, makes it a breeze to express calculations that would otherwise take pages of SQL statements using a relational database.
This remains true about OLAP today, and it is likely to remain true for a long time. While technologies change, the underlying concepts remain the same. But OLAP seems to be falling out of favor recently. Why would that be?
The top Google search result for OLAP claims "OLAP Is Becoming Obsolete". This is actually a paid advertisement that invites users to download a paper titled Selecting the Right Database Technology for Your Business Analytics Project. Interestingly, it says very little about OLAP and does not even use the word obsolete anywhere in the paper. And the only negative thing it says about OLAP is the single sentence below:
Storing the results of these pre-calculations takes exponentially more storage resources than the actual raw data does, limiting the size of raw data that can make up a cube to gigabyte scale.
But that is actually false, because the most popular storage format for OLAP cubes are multidimensional structures which require far less storage than the original source data. Production OLAP cubes have exceeded more than 20 terabytes of raw data, and their continued growth is limited only by computing power, memory and storage.
In the real world, there is no reason to believe that OLAP is becoming obsolete. But there are more signs that people think it is, notwithstanding the facts. The statement below is from the Microsoft website more recently:
For new projects, we generally recommend tabular models. (rather than OLAP cubes)
Microsoft has been a leader in promoting OLAP, so why are they now downplaying it and steering people towards tabular models? The reason they give is that "tabular models are faster to design, test, and deploy..." But that is true only when the requirements and data are very simple. When the requirements or the data become more complex, even a little bit, the complexity of developing a tabular model explodes and quickly becomes far, far more complex than a multidimensional OLAP project with exactly the same requirements and data. And the end products are less flexible and far less able to adapt to changing requirements.
So what gives? Why would Microsoft make a claim that relies on an implausible assumption of simplicity which does not exist in most enterprise environments? And why would another company claim that OLAP is becoming obsolete in a paid advertisement with only a single dubious claim to back it up?
Here's what I think is going on: OLAP is most valuable when the underlying source data is clean and well-integrated. And clean, well-integrated data is difficult to achieve for many organizations. Claiming that OLAP is obsolete is a marketing ploy to promote products that work well with poor-quality data and data that is not well integrated.
There is a legitimate need for such products, and they have a huge market potential – but vendors should make that case and sell those products without claiming that OLAP is becoming obsolete because it is not. For organizations with clean, well-integrated data OLAP is far and away the best choice for business intelligence and analytical applications.