April 25, 2013

Analytic application themes

I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.

1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.

Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.

Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:

2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:

Also arising fairly frequently are:

I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.

Read more

January 28, 2013

Attack of the Frankenschemas

In typical debates, the extremists on both sides are wrong. “SQL vs. NoSQL” is an example of that rule. For many traditional categories of database or application, it is reasonable to say:

Reasons to abandon SQL in any given area usually start:

Some would further say that NoSQL is cheaper, scales better, is cooler or whatever, but given the range of NewSQL alternatives, those claims are often overstated.

Sectors where these reasons kick in include but are not limited to: Read more

October 18, 2012

Notes on Hadoop adoption and trends

With Strata/Hadoop World being next week, there is much Hadoop discussion. One theme of the season is BI over Hadoop. I have at least 5 clients claiming they’re uniquely positioned to support that (most of whom partner with a 6th client, Tableau); the first 2 whose offerings I’ve actually written about are Teradata Aster and Hadapt. More generally, I’m hearing “Using Hadoop is hard; we’re here to make it easier for you.”

If enterprises aren’t yet happily running business intelligence against Hadoop, what are they doing with it instead? I took the opportunity to ask Cloudera, whose answers didn’t contradict anything I’m hearing elsewhere. As Cloudera tells it (approximately — this part of the conversation* was rushed):   Read more

July 15, 2012

Issues in regulatory compliance

From time to time, I hear of regulatory requirements to retain, analyze, and/or protect data in various ways. It’s hard to get a comprehensive picture of these, as they vary both by industry and jurisdiction; so I generally let such compliance issues slide. Still, perhaps I should use one post to pull together what is surely a very partial list.

Most such compliance requirements have one of two emphases: Either you need to keep your customers’ data safe against misuse, or else you’re supposed to supply information to government authorities. From a data management and analysis standpoint, the former area mainly boils down to:

The latter, however, has numerous aspects.

First, there are many purposes for the data retention and analysis, including but by no means limited to: Read more

July 2, 2012

Introduction to Yarcdata

Cray’s strategy these days seems to be:

At the moment, the main diversifications are:

The last of the three is what Cray subsidiary Yarcdata is all about. Read more

May 21, 2012

Cool analytic stories

There are several reasons it’s hard to confirm great analytic user stories. First, there aren’t as many jaw-dropping use cases as one might think. For as I wrote about performance, new technology tends to make things better, but not radically so. After all, if its applications are …

… all that bloody important, then probably people have already been making do to get it done as best they can, even in an inferior way.

Further, some of the best stories are hard to confirm; even the famed beer/diapers story isn’t really true. Many application areas are hard to nail down due to confidentiality, especially but not only in such “adversarial” domains as anti-terrorism, anti-spam, or anti-fraud.

Even so, I have two questions in my inbox that boil down to “What are the coolest or most significant analytics stories out there?” So let’s round up some of what I know. Read more

May 13, 2012

Notes on the analysis of large graphs

This post is part of a series on managing and analyzing graph data. Posts to date include:

My series on graph data management and analytics got knocked off-stride by our website difficulties. Still, I want to return to one interesting set of issues — analyzing large graphs, specifically ones that don’t fit comfortably into RAM on a single server. By no means do I have the subject figured out. But here are a few notes on the matter.

How big can a graph be? That of course depends on:

*Even if your graph has 10 billion nodes, those can be tokenized in 34 bits, so the main concern is edges. Edges can include weights, timestamps, and so on, but how many specifics do you really need? At some point you can surely rely on a pointer to full detail stored elsewhere.

The biggest graph-size estimates I’ve gotten are from my clients at Yarcdata, a division of Cray. (“Yarc” is “Cray” spelled backwards.) To my surprise, they suggested that graphs about people could have 1000s of edges per node, whether in:

Yarcdata further suggested that bioinformatics use cases could have node counts higher yet, characterizing Bio2RDF as one of the “smaller” ones at 22 billion nodes. In these cases, the nodes/edge average seems lower than in people-analysis graphs, but we’re still talking about 100s of billions of edges.

Recalling that relationship analytics boils down to finding paths and subgraphs, the naive relational approach to such tasks would be: Read more

September 12, 2011

Hadoop notes

I visited California recently, and chatted with numerous companies involved in Hadoop — Cloudera, Hortonworks, MapR, DataStax, Datameer, and more. I’ll defer further Hadoop technical discussions for now — my target to restart them is later this month — but that still leaves some other issues to discuss, namely adoption and partnering.

The total number of enterprises in the world paying subscription and license fees that they would regard as being for “Hadoop or something Hadoop-related” probably is not much over 100 right now, but I’d expect to see pretty rapid growth. Beyond that, let’s divide customers into three groups:

Hadoop vendors, in different mixes, claim to be doing well in all three segments. Even so, almost all use cases involve some kind of machine-generated data, with one exception being a credit card vendor crunching a large database of transaction details. Multiple kinds of machine-generated data come into play — web/network/mobile device logs, financial trade data, scientific/experimental data, and more. In particular, pharmaceutical research got some mentions, which makes sense, in that it’s one area of scientific research that actually enjoys fat for-profit research budgets.

Read more

April 8, 2011

Revolution Analytics update

I wasn’t too impressed when I spoke with Revolution Analytics at the time of its relaunch last year. But a conversation Thursday evening was much clearer. And I even learned some cool stuff about general predictive modeling trends (see the bottom of this post).

Revolution Analytics business and business model highlights include:

Read more

January 10, 2011

Privacy dangers — an overview

This post is the first of a series. The second one delves into the technology behind the most serious electronic privacy threats.

The privacy discussion has gotten more active, and more complicated as well. A year ago, I still struggled to get people to pay attention to privacy concerns at all, at least in the United States, with my first public breakthrough coming at the end of January. But much has changed since then.

On the commercial side, Facebook modified its privacy policies, garnering great press attention and an intense user backlash, leading to a quick partial retreat. The Wall Street Journal then launched a long series of articles — 13 so far — recounting multiple kinds of privacy threats. Other media joined in, from Forbes to CNet. Various forms of US government rule-making to inhibit advertising-related tracking have been proposed as an apparent result.

In the US, the government had a lively year as well. The Transportation Security Administration (TSA) rolled out what have been dubbed “porn scanners,” and backed them up with “enhanced patdowns.” For somebody who is, for example, female, young, a sex abuse survivor, and/or a follower of certain religions, those can be highly unpleasant, if not traumatic. Meanwhile, the Wikileaks/Cablegate events have spawned a government reaction whose scope is only beginning to be seen. A couple of “highlights” so far are some very nasty laptop seizures, and the recent demand for information on over 600,000 Twitter accounts. (Christopher Soghoian provided a detailed, nuanced legal analysis of same.)

At this point, it’s fair to say there are at least six different kinds of legitimate privacy fear. Read more

← Previous PageNext Page →

Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:

Login

Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.