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  • Log analysis

    Discussion of how data warehousing and analytic technologies are applied to logfile analysis. Related subjects include:

    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 14, 2017

    Cloudera Altus

    I talked with Cloudera before the recent release of Altus. In simplest terms, Cloudera’s cloud strategy aspires to:

    In other words, Cloudera is porting its software to an important new platform.* And this port isn’t complete yet, in that Altus is geared only for certain workloads. Specifically, Altus is focused on “data pipelines”, aka data transformation, aka “data processing”, aka new-age ETL (Extract/Transform/Load). (Other kinds of workload are on the roadmap, including several different styles of Impala use.) So what about that is particularly interesting? Well, let’s drill down.

    *Or, if you prefer, improving on early versions of the port.

    Read more

    April 17, 2017

    Interana

    Interana has an interesting story, in technology and business model alike. For starters:

    And to be clear — if we leave aside any questions of marketing-name sizzle, this really is business intelligence. The closest Interana comes to helping with predictive modeling is giving its ad-hoc users inspiration as to where they should focus their modeling attention.

    Interana also has an interesting twist in its business model, which I hope can be used successfully by other enterprise software startups as well. Read more

    April 13, 2017

    Analyzing the right data

    0. A huge fraction of what’s important in analytics amounts to making sure that you are analyzing the right data. To a large extent, “the right data” means “the right subset of your data”.

    1. In line with that theme:

    2. Business intelligence interfaces today don’t look that different from what we had in the 1980s or 1990s. The biggest visible* changes, in my opinion, have been in the realm of better drilldown, ala QlikView and then Tableau. Drilldown, of course, is the main UI for business analysts and end users to subset data themselves.

    *I used the word “visible” on purpose. The advances at the back end have been enormous, and much of that redounds to the benefit of BI.

    3. I wrote 2 1/2 years ago that sophisticated predictive modeling commonly fit the template:

    That continues to be tough work. Attempts to productize shortcuts have not caught fire.

    Read more

    October 21, 2016

    Rapid analytics

    “Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.

    1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:

    2. In early 2011, I coined the phrase investigative analytics, about which I said three main things: Read more

    October 10, 2016

    Notes on anomaly management

    Then felt I like some watcher of the skies
    When a new planet swims into his ken

    — John Keats, “On First Looking Into Chapman’s Homer”

    1. In June I wrote about why anomaly management is hard. Well, not only is it hard to do; it’s hard to talk about as well. One reason, I think, is that it’s hard to define what an anomaly is. And that’s a structural problem, not just a semantic one — if something is well enough understood to be easily described, then how much of an anomaly is it after all?

    Artificial intelligence is famously hard to define for similar reasons.

    “Anomaly management” and similar terms are not yet in the software marketing mainstream, and may never be. But naming aside, the actual subject matter is important.

    2. Anomaly analysis is clearly at the heart of several sectors, including:

    Each of those areas features one or both of the frameworks:

    So if you want to identify, understand, avert and/or remediate bad stuff, data anomalies are the first place to look.

    3. The “insights” promised by many analytics vendors — especially those who sell to marketing departments — are also often heralded by anomalies. Already in the 1970s, Walmart observed that red clothing sold particularly well in Omaha, while orange flew off the shelves in Syracuse. And so, in large college towns, they stocked their stores to the gills with clothing in the colors of the local football team. They also noticed that fancy dresses for little girls sold especially well in Hispanic communities … specifically for girls at the age of First Communion.

    Read more

    October 3, 2016

    Notes on the transition to the cloud

    1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:

    Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.

    This is a good example of Monash’s Laws of Commercial Semantics.

    2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.

    This fact now seems to be widely understood.

    Read more

    September 6, 2016

    “Real-time” is getting real

    I’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. Some of those debates are by now pretty much settled. In particular:

    A big issue that does remain open is: How fresh does data need to be? My preferred summary answer is: As fresh as is needed to support the best decision-making. I think that formulation starts with several advantages:

    Straightforward applications of this principle include: Read more

    May 30, 2016

    Adversarial analytics and other topics

    Five years ago, in a taxonomy of analytic business benefits, I wrote:

    A large fraction of all analytic efforts ultimately serve one or more of three purposes:

    • Marketing
    • Problem and anomaly detection and diagnosis
    • Planning and optimization

    That continues to be true today. Now let’s add a bit of spin.

    1. A large fraction of analytics is adversarial. In particular: Read more

    October 15, 2015

    Basho and Riak

    Basho was on my (very short) blacklist of companies with whom I refuse to speak, because they have lied about the contents of previous conversations. But Tony Falco et al. are long gone from the company. So when Basho’s new management team reached out, I took the meeting.

    For starters:

    Basho’s product line has gotten a bit confusing, but as best I understand things the story is:

    Technical notes on some of that include:? Read more

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