InetSoft Tools & Technology: What is In-Memory Analysis?

Below is a continuation of the transcript of the April 2017 Webinar hosted by InetSoft on the topic of OLAP, the basics of what it is and an explanation of some of the technology terms related to, what OLAP's benefits are, and what the choices in OLAP technologies and OLAP tools are. The speaker is Mark Flaherty, CMO at InetSoft.

Mark Flaherty (MF): Within the in-memory analysis paradigm, there is the ability to store some of your results in memory. So this gives you a very fast response rate vs. disk. It also reduces your source system impact. So if you’re not wanting to run long queries against your source environment or your source application, and you’re not interested in building a data warehouse, this is a technique that is very successful and very useful. It reduces cost in terms of taking advantage of existing systems. So there is not necessarily a requirement of building a whole new layer of aggregation and summarization via an OLAP cube, creating a data warehouse, creating a star schema, supporting all the requirements of a traditional OLAP implementation.

Ultimately you get the ability to do exploration, reporting, and drilling, filtering of data, all in the context of very easy to use, highly interactive application. We’ll show you in a little bit the InetSoft business intelligence application and in-memory capabilities, and how that relates to analysis.

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What is MDX – Multi-dimensional Expression?

The power and the strength of OLAP are based on its query language, which is called MDX. You could think of it simply as a series of SQL statements that are combined together to operate as a unit. So it has a very SQL-like semantic, and commonly you will find that if you view an MDX statement, it is a series of mini SQL statements that are all working together.

So this allows for a more powerful way to do SQL based analysis against your environment, and it’s also easier to do. If you use MDX, it is a much easier technique to apply to your data analysis than if you used straight SQL. Some of the power that you can get from an OLAP-based configuration comes from some of the algorithms, the statistical analysis that you can do that is built into the technology.

A couple of examples of those are the ability to do prior period analysis. So for example in the highlighted chart you could compare the first month of Q2 with the entire month prior. So this is one way you could spot trends or anomalies between two different periods of time that are of non-equal duration.

The other technique that is built in using MDX is parallel period analysis, where you are looking at one period of time against another period of time. Certainly this can be performed using traditional SQL, but with the power of MDX and the fact that this is built in, the ability to perform this sort of analysis is much easier and purpose built into the technology. There is also the asymmetric analysis where maybe I am trying to look at the month of February versus the average of all of the months of last year. So this a technique that is built into the way that MDX works, and that is one of the common reasons why a lot of database analysts prefer the MDX language over traditional SQL based techniques.

InetSoft's Style Intelligence can perform many of the common OLAP analysis functions. For instance you can do linear regressions and standard deviations, ranking and filtering by hierarchy, conditional calculations, and more. For instance, you can do complex charting that takes into account multiple groupings. When you are thinking OLAP and InetSoft capabilities, you should be thinking pre-built analytical functions that are supportive of the MDX language.

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