Near real-time analytics


Tags: fail

Big data = big fail?

Martin Fowler recently summarized a “Reporting Database” pattern. The core idea behind this pattern is to split analytical and transactional processing by providing a separate database for the former. A separate database for reporting purposes is nothing new and this pattern is widespread in the enterprise space. What caught my interest though was his opinion on near real-time analytics.

These days the desire seems to be for near-real time analytics. I’m skeptical of the value of this. Often when analyzing data trends you don’t need to react right away, and your thinking improves when you give it time for a proper mulling. Reacting too quickly leads to a form of information hysteresis, where you react badly to data that’s changing too rapidly to get a proper picture of what’s going on. Source

I think that Martin Fowler raised an interesting point here. There is a lot of hype around big data lately. There are tools to store and query terabytes of data and algorithms to analyze them. Big data offers a big promise that we can gain new insights from the data we already have in an easy and cost-effective way. To give one example here is a link to China Mobile case study.

Unfortunately, big data seems not to be a silver bullet. Recent failures (e.g. google flu trends [1] [2]) shows that one can’t blindly apply algorithms and tools in a hope that they will reveal some hidden knowledge. A proper model and good domain knowledge are important ingredients of a successful big data system and one should think before blindly jumping on this bandwagon.

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