Analysis of columnar data warehouse DBMS vendor ParAccel, maker of PADB (ParAccel Analytic DataBase). Related subjects include:

April 7, 2012

Many kinds of memory-centric data management

I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:

Getting more specific than that is hard, however, because:

Consider, for example, some of the in-memory data management ideas kicking around. Read more

February 8, 2012

Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same

This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:

*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.

Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more

July 5, 2011

Eight kinds of analytic database (Part 1)

Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.

Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning.  Read more

June 20, 2011

The Vertica story (with soundbites!)

I’ve blogged separately that:

And of course you know:

Read more

June 20, 2011

Columnar DBMS vendor customer metrics

Last April, I asked some columnar DBMS vendors to share customer metrics. They answered, but it took until now to iron out a couple of details. Overall, the answers are pretty impressive.  Read more

February 28, 2011

Updating our vendor client disclosures

Edit: This disclosure has been superseded by a March, 2012 version.

From time to time, I disclose our vendor client lists. Another iteration is below. To be clear:

With that said, our vendor client disclosures at this time are:

Read more

February 5, 2011

Comments on the Gartner 2010/2011 Data Warehouse Database Management Systems Magic Quadrant

Edit: Comments on the February, 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems — and on the companies reviewed in it — are now up.

The Gartner 2010 Data Warehouse Database Management Systems Magic Quadrant is out. I shall now comment, just as I did to varying degrees on the 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants.

Note: Links to Gartner Magic Quadrants tend to be unstable. Please alert me if any problems arise; I’ll edit accordingly.

In my comments on the 2008 Gartner Data Warehouse Database Management Systems Magic Quadrant, I observed that Gartner’s “completeness of vision” scores were generally pretty reasonable, but their “ability to execute” rankings were somewhat bizarre; the same remains true this year. For example, Gartner ranks Ingres higher by that metric than Vertica, Aster Data, ParAccel, or Infobright. Yet each of those companies is growing nicely and delivering products that meet serious cutting-edge analytic DBMS needs, neither of which has been true of Ingres since about 1987.  Read more

February 3, 2011

ParAccel PADB technical notes

I posted last October about PADB (ParAccel Analytic DataBase), but held back on various topics since PADB 3.0 was still under NDA. By the time PADB 3.0 was released, I was on blogging hiatus. Let’s do a bit of ParAccel catch-up now.

One big part of PADB 3.0 was an analytics extensibility framework. If we match PADB against my recent analytic computing system checklistRead more

January 24, 2011

Choices in analytic computing system design

When I posted a long list of architectural options for analytic DBMS, I left a couple of IOUs in for missing parts. One was in the area of what is sometimes called advanced-analytics functionality, which roughly speaking means aspects of analytic database management systems that are not directly related to conventional* SQL queries.

*Main examples of “conventional” = filtering, simple aggregrations.

The point of such functionality is generally twofold. First, it helps you execute analytic algorithms with high performance, due to reducing data movement and/or executing the analytics in parallel. Second, it helps you create and execute sophisticated analytic processes with (relatively) little effort.

For now, I’m going to refer to an analytic RDBMS that has been extended by advanced-analytics functionality as an analytic computing system, rather than as some kind of “platform,” although I suspect the latter term is more likely to wind up winning.  So far, there have been five major categories of subsystem or add-on module that contribute to making an analytic DBMS a more fully-fledged analytic computing system:

Read more

October 22, 2010

Notes and links October 22, 2010

A number of recent posts have had good comments. This time, I won’t call them out individually.

Evidently Mike Olson of Cloudera is still telling the machine-generated data story, exactly as he should be. The Information Arbitrage/IA Ventures folks said something similar, focusing specifically on “sensor data” …

… and, even better, went on to say:  Read more

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