A partial overview of Netezza database software technology
Netezza is having its user conference Enzee Universe in Boston Monday–Wednesday, June 21-23, and naturally will be announcing new products there, and otherwise providing hooks and inducements to get itself written about. (The preliminary count is seven press releases in all.) To get a head start, I stopped by Netezza Thursday for meetings that included a 3 ½ hour session with 10 or so senior engineers, and have exchanged some clarifying emails since. Read more
| Categories: Data warehouse appliances, Data warehousing, Netezza, Theory and architecture, Workload management | 15 Comments |
Notes on a spate of Netezza-related blog posts
Fearing that last year’s tight travel budgets would hamper attendance, Netezza – like a number of other vendors – decided to forgo a traditional user conference. Instead, it took its Enzee Universe show on the road, essentially spreading the conference across eight cities. I was asked to keynote six of the installments.
After the first one, Netezza Marketing VP Tim Young took me aside for two pieces of constructive criticism. The surprising one* was that he felt I had been INSUFFICIENTLY critical of Netezza. Since then, every other conversation we’ve had about content creation has also featured ringing reassurances that Tim truly wants independent, non-pandering work.
*The unsurprising one was that I’d rushed. Well, duh. After months of telling me I had a 1 hour slot, Netezza cut me to ½ hour a few days beforehand. And my talk had been designed to be high-speed even in the longer time slot …
As a result, I accepted a subsequent gig from Netezza that I would barely consider from most other vendors. Namely, for this year’s Enzee Universe – June 21-23, aka Monday-Wednesday of this week, at the Westin Waterfront Hotel in Boston – I would do some contemporaneous blogging. The parameters we agreed on included: Read more
| Categories: Data warehouse appliances, Data warehousing, Netezza, Presentations | 3 Comments |
Objectivity Infinite Graph
I chatted Wednesday night with Darren Wood, the Australia-based lead developer of Objectivity’s Infinite Graph database product. Background includes:
- Objectivity is a profitable, decades-old object-oriented DBMS vendor with about 50 employees.
- Like some other object-oriented DBMS of its generation, Objectivity is as much a toolkit for building DBMS as it is a real finished DBMS product. Objectivity sales are typically for custom deals, where Objectivity helps with the programming.
- The way Objectivity works is basically:
- You manage objects in memory, in the format of your choice.
- Objectivity bangs them to disk, across a network.
- Objectivity manages the (distributed) pointers to the objects.
- You can, if you choose, hard code exactly which objects are banged to which node.
- Objectivity’s DML for reading data is very different from Objectivity’s DML for writing data. (I think the latter is more like the program code itself, while the former is more like regular DML.)
- The point of Objectivity is not so much to have fast I/O. Rather, it is to minimize the CPU cost of getting the data that comes across the wire into useful form.
- Darren got the idea of putting a generic graph DBMS front-end on Objectivity while doing a relationship analytics project for an Australian intelligence agency.
- Darren redoubled his efforts to sell the project internally at Objectivity after reading what I wrote about relationship analytics back in 2006 or so.
- There is now a 5 or so person team developing Infinite Graph.
- Infinite Graph is just now going out to beta test.
Infinite Graph is an API or language binding on top of Objectivity that:
- Hides a lot of Objectivity’s complexity.
- Is suitable for graph/relationship analytics.
| Categories: Analytic technologies, Object, Objectivity and Infinite Graph, RDF and graphs, Surveillance and privacy | 10 Comments |
Best practices for analytic DBMS POCs
When you are selecting an analytic DBMS or appliance, most of the evaluation boils down to two questions:
- How quickly and cost-effectively does it execute SQL?
- What analytic functionality, SQL or otherwise, does it do a good job of executing?
And so, in undertaking such a selection, you need to start by addressing three issues:
- What does “speed” mean to you?
- What does “cost” mean to you?
- What analytic functionality do you need anyway?
| Categories: Benchmarks and POCs, Data warehousing, Exadata, Netezza, ParAccel, Teradata | 7 Comments |
The underlying technology of QlikView
QlikTech* finally decided both to become a client and, surely not coincidentally, to give me more technical detail about QlikView than it had when last we talked a couple of years ago. Indeed, I got to spend a couple of hours on the phone not just with Anthony Deighton, but also with QlikTech’s Hakan Wolge, who wrote 70-80% of the code in QlikView 1.0, and remains in effect QlikTech’s chief architect to this day.
*Or, as it now appears to be called, Qlik Technologies.
Let’s start with some quick reminders:
- QlikTech makes QlikView, a widely popular business intelligence (BI) tool suite.
- QlikView is distinguished by the flexibility of navigation through its user interface.
- To support this flexibility, QlikView preloads all data you might want to query into memory.
Let’s also dispose of one confusion right up front, namely QlikTech’s use of the word associative: Read more
| Categories: Business intelligence, Database compression, Memory-centric data management, QlikTech and QlikView | 36 Comments |
Kickfire update
A Kickfire competitor tipped me off that he got 3 Kickfire salesmen’s resumes in 24 hours. I ran this by Kickfire CEO Bruce Armstrong, who confirmed that Kickfire has had a layoff, but gave me no further details.
Bruce also told me that Kickfire is now up to 10 paying customers, and that there are repeat deals.
| Categories: Data warehouse appliances, Data warehousing, Kickfire, Market share and customer counts | 3 Comments |
Ingres VectorWise technical highlights
After working through problems w/ travel, cell phones, and so on, Peter Boncz of VectorWise finally caught up with me for a regrettably brief call. Peter gave me the strong impression that what I’d written in the past about VectorWise had been and remained accurate, so I focused on filling in the gaps. Highlights included: Read more
| Categories: Actian and Ingres, Analytic technologies, Benchmarks and POCs, Columnar database management, Data warehousing, Database compression, Open source, VectorWise | 2 Comments |
Rainstor update
I was tired and cranky when I talked with my former clients at Rainstor (formerly Clearpace) yesterday, so our call was shorter than it otherwise might have been. Anyhow, there’s a new version called Rainstor 4, the two main themes of which are:
- Compliance-specific features.
- Bottleneck Whack-A-Mole.
The point is that Rainstor is focusing its efforts on enterprises that: Read more
Fun with quotes in the VectorWise press release
Ingres forgot to prebrief me on the VectorWise announcement, and despite valiant efforts hasn’t succeeded in connecting with me since they realized the lapse. Meanwhile, I took a look at the VectorWise press release, and found the quotes to be somewhat amusing.
Read more
| Categories: Actian and Ingres, VectorWise | 8 Comments |
The most important part of the “social graph” is neither social nor a graph
“Social graph” is a highly misleading term, and so is “social network analysis.” By this I mean:
There’s something akin to “social graphs” and “social network analysis” that is more or less worthy of all the current hype – but graphs and network analysis are only a minor part of the whole story.
In particular, the most important parts of the Facebook “social graph” are neither social nor a graph. Rather, what’s really important is an aggregate Profile of Revealed Preferences, of which person-to-person connections or other things best modeled by a graph play only a small part.