When is a disadvantage not really a disadvantage?

Answer: when you’re giving an interview.

Are there disadvantages to continuous analytics?

The main disadvantage of stream-based continuous analytics is the incorrect perception that stream processing is solely for real-time applications, rather than the more general — and much larger — problems facing data analytics today.

I cam across this gem when reading up on Truviso’s Continuous Analytics Platform, a tool which does pretty much what you expect it to, which is the analyze just incoming data. There are advantages to this; for instance, a pretty common task in data analysis is to look at how are things changing? You can look at a trend graph on-demand, which would involve querying a lot of data and take a long time, or you can perform the same query multiple on small batches of incoming data and store the results.

As far as I can see, that’s all Truviso is really doing. They have an efficient, and fast, add-in to a SQL-based server that will perform a set of pre-defined queries on an incoming batch of data. Looks useful for certain cases – mainly when either your queries are really expensive so you don’t run them often, or you need to be sensitive to changes.

Truviso seems to be targeting the former situation in the section of the interview I quoted. The answer, however, is both entertaining and downright wrong: he’s addressing a misconception about what Continuous Analytics is capable of, and not an actual disadvantage (or limitation) of Continuous Analytics. 

In other words, he’s sidestepping the question and answering the question he really wanted to answer – one that shows Truviso in a positive light.

It’s also classical “job interview” technique.