Netezza

Analysis of Netezza and its data warehouse appliances. Related subjects include:

April 21, 2011

In-memory, parallel, not-in-database SAS HPA does make sense after all

I talked with SAS about its new approach to parallel modeling. The two key points are:

The whole thing is called SAS HPA (High-Performance Analytics), in an obvious reference to HPC (High-Performance Computing). It will run initially on RAM-heavy appliances from Teradata and EMC Greenplum.

A lot of what’s going on here is that SAS found it annoyingly difficult to parallelize modeling within the framework of a massively parallel DBMS such as Teradata. Notes on that aspect include:

Read more

April 17, 2011

Netezza TwinFin i-Class overview

I have long complained about difficulties in discussing Netezza’s TwinFin i-Class analytic platform. But I’m ready now, and in the grand sweep of the product’s history I’m not even all that late. The Netezza i-Class timing story goes something like this:

My advice to Netezza as to how it should describe TwinFin i-Class boils down to:  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Edit: This disclosure has been superseded by a March, 2012 version.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 11, 2011

Comments on the 2011 Forrester Wave for Enterprise Data Warehouse Platforms

The Forrester Wave: Enterprise Data Warehouse Platforms, Q1 2011 is now out,* hot on the heels of the Gartner Magic Quadrant. Unfortunately, this particular Forrester Wave is riddled with inaccuracy.  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

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

October 15, 2010

Notes on data warehouse appliance prices

I’m not terribly motivated to do a detailed analysis of data warehouse appliance list prices, in part because:

That said, here are some notes on data warehouse appliance prices. Read more

October 10, 2010

It can be hard to analyze analytics

When vendors talk about the integration of advanced analytics into database technology, confusion tends to ensue. For example: Read more

October 3, 2010

Notes and links October 3 2010

Some notes, follow-up, and links before I head out to California:  Read more

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