June 30, 2017

Analytics on the edge?

There’s a theory going around to the effect that:

There’s enough truth to all that to make it worth discussing. But the strong forms of the claims seem overblown.

1. This story doesn’t even make sense except for certain new classes of application. Traditional business applications run all over the world, in dedicated or SaaSy modes as the case may be. E-commerce is huge. So is content delivery. Architectures for all those things will continue to evolve, but what we have now basically works.

2. When it comes to real-world appliances, this story is partially accurate. An automobile is a rolling network of custom Linux systems, each running hand-crafted real-time apps, a few of which also have minor requirements for remote connectivity. That’s OK as far as it goes, but there could be better support for real-time operational analytics. If something as flexible as Spark were capable of unattended operation, I think many engineers of real-world appliances would find great ways to use it.

3. There’s a case to be made for something better yet. I think the argument is premature, but it’s worth at least a little consideration. 

There are any number of situations in which decisions are made on or about remote systems, based on models or rules that should be improved over time. For example, such decisions might be made in:

In the canonical case, we might envision a system in which:

This all seems like an awkward fit for any common computing architecture I can think of.

But it’s hard to pin down important examples of that “canonical” case. The story implicitly assumes:

And now we’re begging a huge question: What exactly is there that keeps score as to when the model succeeds and fails? Mathematically speaking, I can’t imagine what a general answer would be like.

4. So when it comes to predictive models executed on real-world appliances I think that analytic workflows will:

And with that much of the apparent need for fancy distributed analytic architectures evaporates.

5. Finally, and notwithstanding the previous point: Across many use cases, there’s some kind of remote log data being shipped back to a central location. It may be the complete log. It may be periodic aggregates. It may happen only what the edge nodes regard as significant events. But something is getting shipped home.

The architectures for shipping, receiving and analyzing such data are in many cases immature. That’s obvious if there’s any kind of streaming involved, or if analysis is done in Spark. Ditto if there’s anything we might call “non-tabular business intelligence”. As this stuff matures, it will in many cases fit very well with today’s cloud thinking. But in any case — it needs to mature.

Truth be told, even the relational case is immature, in that it can easily rely on what I called:

data warehouses (perhaps really data marts) that are updated in human real-time

That quote is from a recent post about Kudu, which:

As always, technology is in flux.

Related links

Comments

One Response to “Analytics on the edge?”

  1. Joe Harris on July 1st, 2017 8:43 am

    Interesting to not (in regards to the cloud) that Amazon already has Amazon Greengrass for edge computing. https://aws.amazon.com/greengrass/

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