Netezza

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

February 11, 2010

Intelligent Enterprise’s Editors’/Editor’s Choice list for 2010

As he has before, Intelligent Enterprise Editor Doug Henschen

(Actually, he’s really called it an “award.”)

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February 10, 2010

Comments on the Gartner 2009/2010 Data Warehouse Database Management System Magic Quadrant

February, 2011 edit: I’ve now commented on Gartner’s 2010 Data Warehouse Database Management System Magic Quadrant as well.

At intervals of a little over a year, Gartner Group publishes a Data Warehouse Database Management System Magic Quadrant. Gartner’s 2009 data warehouse DBMS Magic Quadrant — actually, January 2010 — is now out.* For many reasons, including those I noted in my comments on Gartner’s 2008 Data Warehouse DBMS Magic Quadrant, the Gartner quadrant pictures are a bad use of good research. Rather than rehash that this year, I’ll merely call out some points in the surrounding commentary that I find interesting or just plain strange. Read more

January 25, 2010

Netezza Skimmer

As I previously complained, last week wasn’t a very convenient time for me to have briefings. So when Netezza emailed to say it would release its new entry-level Skimmer appliance this morning, while I asked for and got a Friday afternoon briefing, I kept it quick and basic.

That said, highlights of my Netezza Skimmer briefing included:

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October 25, 2009

Reports of perfectly-balanced hardware configurations are greatly exaggerated

Data warehouse appliance and software appliance vendors like to claim that they’ve worked out just the right hardware configuration(s), and that a single configuration is correct for a fairly broad range of workloads. But there are a lot of reasons to be dubious about that. Specific vendor evidence includes:

What’s more, the claim never made a lot of sense anyway. With the rarest of exceptions, even a single data warehouse’s workload will contain different queries that strain different parts of the system in different ratios. Calculating the “ideal” hardware configuration for that single workload would be forbiddingly difficult. And even if one could calculate it, it almost surely would be different than another user’s “ideal” configuration. How a single hardware configuration can be “ideally balanced” for a broad class of use cases boggles the imagination.

October 5, 2009

Oracle Exadata 2 capacity pricing

Summary of Oracle Exadata 2 capacity pricing

Analyzing Oracle Exadata pricing is always harder than one would first think. But I’ve finally gotten around to doing an Oracle Exadata 2 pricing spreadsheet. The main takeaways are:

Longer version

When Oracle introduced Exadata last year it was, well, expensive. Exadata 2 has now been announced, and it is significantly cheaper than Exadata 1 per terabyte of user data, based on:

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September 30, 2009

Facts and rumors

September 29, 2009

What Nielsen really uses in data warehousing DBMS

In its latest earnings call, Oracle made a reference to The Nielsen Company that was — to put it politely — rather confusing. I just plopped down in a chair next to Greg Goff, who evidently runs data warehousing at Nielsen, and had a quick chat. Here’s the real story.

September 19, 2009

Oracle gives a few customer database size examples

In its recent quarterly conference call, Oracle said (as per the Seeking Alpha transcript):

AC Neilsen, for instance, we deployed a 45-terabyte data [mart], they called it; Adidas, 13 terabytes; Australian Bureau of Statistics, 250 terabytes; and of course, some of our high-end ones that you have probably heard of in the past, AT&T, 250 terabytes; Yahoo!, 700 terabytes — just gives you an idea of the size of the databases that are out there and how they are growing, and that’s driving the need for greater throughput.

I don’t know what’s being counted there, but I wouldn’t be surprised if those were legit user-data figures.

Some other notes:

September 3, 2009

Teradata and Netezza are doing MapReduce too

Netezza told me a while ago that it planned to introduce MapReduce, and agreed yesterday this was no longer NDAed. Stephen Brobst of Teradata* let slip at XLDB that Teradata has MapReduce too, apparently implemented but not yet generally available.

I don’t have details in either case.  Netezza and Teradata evidently aren’t taking MapReduce as seriously as Aster Data, or even Greenplum or Vertica. But MapReduce has become pretty much of a “checkmark” item for large-database analytic DBMS vendors even so.

*Technically, Brobst is not and never has been a Teradata employee — but he’s widely and correctly regarded as being “of Teradata” even so. 🙂

September 3, 2009

SAS on Netezza and other Netezza extensibility

I chatted with SAS CTO Keith Collins yesterday about the new SAS/Netezza in-database parallel data mining scoring offering. My impression is that this is very similar to SAS’ current Teradata support, notwithstanding SAS’ and Teradata’s apparent original intention of offering in-database modeling by now as well.

I gather this is a big performance-enhancing deal, just as it is for SPSS or Oracle’s own data mining over Oracle.  However, I must confess to not yet understanding why.  That is, I don’t know what’s so complicated about data mining scoring algorithms that makes hand-coding them in SQL particularly forbidding. My naive view of data mining is that you do a big regression to get a bunch of weights, and the resulting scoring algorithm is a linear combination of a few dozen variables.  Evidently, that’s not quite right.

Anyhow, it turns out that SAS held off on this work until it could be done for TwinFin. That’s largely because TwinFin lets partners write code on Intel CPUs, while previously they had to write in C for Netezza’s FPGAs. I got a similar sense from at least one other Netezza partner as well.

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