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        Analytic technologies

        Discussion of technologies related to information query and analysis. Related subjects include:

        June 20, 2018

        Brittleness, Murphy’s Law, and single-impetus failures

        In my initial post on brittleness I suggested that a typical process is:

        In many engineering scenarios, a fuller description could be:

        So it’s necesseary to understand what is or isn’t likely to go wrong. Unfortunately, that need isn’t always met.? Read more

        June 20, 2018

        Brittleness and incremental improvement

        Every system — computer or otherwise — needs to deal with possibilities of damage or error. If it does this well, it may be regarded as “robust”, “mature(d), “strengthened”, or simply “improved”.* Otherwise, it can reasonably be called “brittle”.

        *It’s also common to use the word “harden(ed)”. But I think that’s a poor choice, as brittle things are often also hard.

        0. As a general rule in IT:

        There are many categories of IT strengthening. Two of the broadest are:

        1. One of my more popular posts stated:

        Developing a good DBMS requires 5-7 years and tens of millions of dollars.

        The reasons I gave all spoke to brittleness/strengthening, most obviously in:

        Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.

        Similar things are true for other kinds of “platform software” or distributed systems.

        2. The UI brittleness/improvement story starts similarly:? Read more

        May 20, 2018

        Some stuff that’s always on my mind

        I have a LOT of partially-written blog posts, but am struggling to get any of them finished (obviously). Much of the problem is that they have so many dependencies on each other. Clearly, then, I should consider refactoring my writing plans. ??

        So let’s start with this. Here, in no particular order, is a list of some things that I’ve said in the past, and which I still think are or should be of interest today. It’s meant to be background for numerous posts I write in the near future, and indeed a few hooks for such posts are included below.

        1.? Data(base) management technology is progressing pretty much as I expected.

        2. Rightly or wrongly, enterprises are often quite sloppy about analytic accuracy.

        Read more

        December 12, 2017

        Notes on artificial intelligence, December 2017

        Most of my comments about artificial intelligence in December, 2015 still hold true. But there are a few points I’d like to add, reiterate or amplify.

        1. As I wrote back then in a post about the connection between machine learning and the rest of AI,

        It is my opinion that most things called “intelligence” — natural and artificial alike — have a great deal to do with pattern recognition and response.

        2. Accordingly, it can be reasonable to equate machine learning and AI.

        3. Similarly, it can be reasonable to equate AI and pattern recognition. Glitzy applications of AI include:

        4. The importance of AI and of recent AI advances differs greatly according to application or data category.? Read more

        August 22, 2017

        Imanis Data

        I talked recently with the folks at Imanis Data. For starters:

        Read more

        August 10, 2017

        Notes on data security

        1. In June I wrote about burgeoning interest in data security. I’d now like to add:

        We can reconcile these anecdata pretty well if we postulate that:

        2. My current impressions of the legal privacy vs. surveillance tradeoffs are basically: Read more

        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.? Read more

        June 16, 2017

        Generally available Kudu

        I talked with Cloudera about Kudu in early May. Besides giving me a lot of information about Kudu, Cloudera also helped confirm some trends I’m seeing elsewhere, including:

        Now let’s talk about Kudu itself. As I discussed at length in September 2015, Kudu is:

        Kudu’s adoption and roll-out story starts: Read more

        June 14, 2017

        The data security mess

        A large fraction of my briefings this year have included a focus on data security. This is the first year in the past 35 that that’s been true.* I believe that reasons for this trend include:

        *Not really an exception: I did once make it a project to learn about classic network security, including firewall appliances and so on.

        Certain security requirements, desires or features keep coming up. These include (and as in many of my lists, these overlap):

        More specific or extreme requirements include:? Read more

        June 14, 2017

        Light-touch managed services

        Cloudera recently introduced Cloudera Altus, a Hadoop-in-the-cloud offering with an interesting processing model:

        Thus, you avoid a potential security risk (shipping your data to Cloudera’s service). I’ve tentatively named this strategy light-touch managed services, and am interested in exploring how broadly applicable it might or might not be.

        For light-touch to be a good approach, there should be (sufficiently) little downside in performance, reliability and so on from having your service not actually control the data. That assumption is trivially satisfied in the case of Cloudera Altus, because it’s not an ordinary kind of app; rather, its whole function is to improve the job-running part of your stack. Most kinds of apps, however, want to operate on your data directly. For those, it is more challenging to meet acceptable SLAs (Service-Level Agreements) on a light-touch basis.

        Let’s back up and consider what “light-touch” for data-interacting apps (i.e., almost all apps) would actually mean. The basics are:? Read more

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