Analysis of data management technology optimized for text data. Related subjects include:

August 24, 2013

Hortonworks business notes

Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes  from my call — for which Rob Bearden didn’t bother showing up — include, in no particular order:

In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social.  Read more

August 4, 2013

Data model churn

Perhaps we should remind ourselves of the many ways data models can be caused to churn. Here are some examples that are top-of-mind for me. They do overlap a lot — and the whole discussion overlaps with my post about schema complexity last January, and more generally with what I’ve written about dynamic schemas for the past several years..

Just to confuse things further — some of these examples show the importance of RDBMS, while others highlight the relational model’s limitations.

The old standbys

Product and service changes. Simple changes to your product line many not require any changes to the databases recording their production and sale. More complex product changes, however, probably will.

A big help in MCI’s rise in the 1980s was its new Friends and Family service offering. AT&T couldn’t respond quickly, because it couldn’t get the programming done, where by “programming” I mainly mean database integration and design. If all that was before your time, this link seems like a fairly contemporaneous case study.

Organizational changes. A common source of hassle, especially around databases that support business intelligence or planning/budgeting, is organizational change. Kalido’s whole business was based on accommodating that, last I checked, as were a lot of BI consultants’. Read more

June 13, 2013

How is the surveillance data used?

Over the past week, discussion has exploded about US government surveillance. After summarizing, as best I could, what data the government appears to collect, now I ‘d like to consider what they actually do with it. More precisely, I’d like to focus on the data’s use(s) in combating US-soil terrorism. In a nutshell:

Consider the example of Tamerlan Tsarnaev:

In response to this 2011 request, the FBI checked U.S. government databases and other information to look for such things as derogatory telephone communications, possible use of online sites associated with the promotion of radical activity, associations with other persons of interest, travel history and plans, and education history.

While that response was unsuccessful in preventing a dramatic act of terrorism, at least they tried.

As for actual success stories — well, that’s a bit tough. In general, there are few known examples of terrorist plots being disrupted by law enforcement in the United States, except for fake plots engineered to draw terrorist-leaning individuals into committing actual crimes. One of those examples, that of Najibullah Zazi, was indeed based on an intercepted email — but the email address itself was uncovered through more ordinary anti-terrorism efforts.

As for machine learning/data mining/predictive modeling, I’ve never seen much of a hint of it being used in anti-terrorism efforts, whether in the news or in my own discussions inside the tech industry. And I think there’s a great reason for that — what would they use for a training set? Here’s what I mean.  Read more

June 10, 2013

Where things stand in US government surveillance

Edit: Please see the comment thread below for updates. Please also see a follow-on post about how the surveillance data is actually used.

US government surveillance has exploded into public consciousness since last Thursday. With one major exception, the news has just confirmed what was already thought or known. So where do we stand?

My views about domestic data collection start:

*Recall that these comments are US-specific. Data retention legislation has been proposed or passed in multiple countries to require recording of, among other things, all URL requests, with the stated goal of fighting either digital piracy or child pornography.

As for foreign data: Read more

February 21, 2013

One database to rule them all?

Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times. :)

Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:

To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more

February 13, 2013

It’s hard to make data easy to analyze

It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.

Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:

*Complex event/stream processing terminology is always problematic.

My thoughts on all this start:  Read more

February 5, 2013

Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — concepts

The 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems is out. I’ll split my comments into two posts — this one on concepts, and a companion on specific vendor evaluations.


Let’s start by again noting that I regard Gartner Magic Quadrants as a bad use of good research. On the facts:

When it comes to evaluations, however, the Gartner Data Warehouse DBMS Magic Quadrant doesn’t do as well. My concerns (which overlap) start:

Read more

November 27, 2012

B2C internet software

I recently opined that, especially for cutting-edge internet businesses, analytic applications were not a realistic option; rather, analytic application subsystems are the most you can currently expect. Erin Griffith further observed that the problem isn’t just confined to analytics:

 “We didn’t need 90 percent of the stuff they were offering, and when we told them what we did need — integration with social, curation tools, individual boutiques and analytics — they had nothing”

… a suitable solution to merge his editorial staff’s output with his separate site for selling tickets to events and goods … was not available, so had to build his own hybrid publishing and commerce platform. Likewise, Birchbox had to build a custom backend so that it could include videos and editorial content alongside its e-commerce site.

…  it’s DIY or die.

With that as background, let’s consider why building business-to-consumer internet software is so complicated.

I’d suggest that a consumer website starts with four major conceptual parts: Read more

October 16, 2012

Hadapt Version 2

My clients at Hadapt are coming out with a Version 2 to be available in Q1 2013, and perhaps slipstreaming some of the features before then. At that point, it will be reasonable to regard Hadapt as offering:

Solr is in the mix as well.

Hadapt+Hadoop is positioned much more as “better than Hadoop” than “a better scale-out RDBMS”– and rightly so, due to its limitations when viewed strictly from an analytic RDBMS standpoint. I.e., Hadapt is meant for enterprises that want to do several of:

Hadapt has 6 or so production customers, a dozen or so more coming online soon, 35 or so employees (mainly in Cambridge or Poland), reasonable amounts of venture capital, and the involvement of a variety of industry luminaries. Hadapt’s biggest installation seems to have 10s of terabytes of relational data and 100s of TBs of multi-structured; Hadapt is very confident in its ability to scale an order of magnitude beyond that with the Version 2 product, and reasonably confident it could go even further.

At the highest level, Hadapt works like this: 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

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