MapReduce user eHarmony chose Netezza over Aster or Greenplum
Depending on which IDG reporter you believe, eHarmony has either 4 TB of data or more than 12 TB, stored in Oracle but now analyzed on Netezza. Interestingly, eHarmony is a Hadoop/MapReduce shop, but chose Netezza over Aster Data or Greenplum even so. Price was apparently an important aspect of the purchase decision. Netezza also seems to have had a very smooth POC. Read more
| Categories: Application areas, Aster Data, Benchmarks and POCs, Data warehousing, Greenplum, MapReduce, Netezza, Oracle, Predictive modeling and advanced analytics, Pricing | 5 Comments |
Microstrategy tidbits
I chatted with Microstrategy Wednesday in a call focused on the upcoming Microstrategy 9. There wasn’t a lot of technical content, but I did glean:
- In Microstrategy 9, virtual ROLAP cubes will be able to draw on multiple relational databases, not just one. (Frankly, I’ve never understood why BI vendors are so slow to put in features like that.)
- Actually, in Microstrategy 9 cubes won’t just be virtual. You’ll be able to instantiate parts of them in memory.
- The in-memory part requires manual intervention. However, that intervention can be as minor as pushing a button to accept the recommendations of a Cube Advisor.
- The Microstrategy Cube Advisor will examine workloads for a month or so to see which queries chew up the most resources.
- Another new feature is “complete” OLAP drilldown from any point in any chart or graph, without pre-programming or pre-specification.
- Microstrategy’s favorite DBMS partners are, in some order, Netezza and Teradata.
- Microstrategy 9 is currently scheduled for March 23 release.
My TDWI Night School course Wednesday night
I imagine everybody who’s actually going to TDWI knows how to read an agenda. But in case you missed it, I’ll be holding forth Wednesday night on How to Select an Analytic DBMS. I’ve already posted the slides.
| Categories: Buying processes, Data warehousing | Leave a Comment |
The Netezza guys propose a POC checklist
The Netezza guys at “Data Liberators” are being a bit too cute in talking about FULL DISCLOSURE yet not actually saying they’re from Netezza — but only a bit, in that their identity is pretty clear even so. That said, they’ve proposed a not-terrible checklist of how to conduct POCs. Of course, vendor-provided as it is, it’s incomplete; e.g., there’s no real mention of a baseball-bat test.
Here’s the first part of the Netezza list, with my comments interspersed. Read more
| Categories: Benchmarks and POCs, Buying processes, Data warehousing, Netezza | 1 Comment |
25 facts about Ingres, give or take a couple
Emma McGrattan of Ingres offers a “25 facts” post about Ingres. 24 really are about Ingres. Some are interesting (who knew Ingres still used a lot of Quel?). Some are if anything understated — e.g., there are lots of current CEOs who are Ingres alums (Dave Kellogg and Dennis Moore jump to mind). Only one is a real eyebrow-raiser.
Point 23 says “The average tenure of an Ingres Engineer is 15+ years.” On the other hand, Point 3 says “The longest serving member of Ingres staff is John Smedley who has been with us since June of 1987.” And most of Ingres’ technical staff left after Ingres was acquired by CA, which occurred a few months shy of 15 years ago. Reconciling all that is challenging.
Actually, I was dubious about a second claim too, namely that Ingres/Star was the first distributed DBMS; I thought that the distributed version of Tandem NonStop SQL actually predated it by a few years. But a somewhat contemporaneous article with a number of distributed DBMS dates shows my memory was wrong on that score.
| Categories: Actian and Ingres | 3 Comments |
Infobright update
Infobright briefed me, and I thought it would be best to invite them to provide a write-up themselves of what customer and other information they did and didn’t want to disclose, for me to publish. Read more
| Categories: Application areas, Data warehousing, Infobright, Open source, Telecommunications, Web analytics | 2 Comments |
IBM in the cloud
IBM is making DB2, Informix Dynamic Server, and other products available in the Amazon cloud. The press release says test and development are free, while production will be charged at an hourly rate. No doubt more price details will be forthcoming when the whole thing is fully in production.
Frankly, I’ve lost track of who all has some kind of cloud or SaaS offering now. The list is at least Oracle, IBM, presumably Microsoft, MySQL (via Elastra, and also at almost every web host), PostgreSQL (ditto, more or less), EnterpriseDB, Kognitio, Vertica, Netezza, Aster Data. No doubt I’m forgetting a bunch more.
| Categories: Cloud computing, Data warehousing, IBM and DB2, Pricing | 2 Comments |
An example of Aster Data’s nPath/MapReduce syntax
Perhaps in response to my prior post on Aster Data’s introduction of MapReduce-based nPath, Steve Wooledge of Aster offers a more detailed example. The particular case he works through is:
… the question: for SEO/SEM-driven traffic that stay on our site only for 5 or less pageviews and then leave our site and never return in the same session, what are the top referring search queries and what are the top path of navigated pages on our site?
| Categories: Analytic technologies, Aster Data, Data warehousing, MapReduce, Web analytics | Leave a Comment |
Aster Data nPath
Edit: Unfortunately, this post and its sequel rely on Aster Data posts that Aster’s buyer Teradata no longer makes easily available.
At the same time as it rolled out its cloud story, Aster Data told of nPath, a MapReduce-based feature in nCluster. As best I understand it, the core idea of nPath is that it preprocesses sequential data via MapReduce so that you can then do ordinary SQL on it. (Steve Wooledge’s blog post about nPath outlines why that might be needed. Point 1 in Mayank Bawa’s August, 2008 post is much more concise. 😉 ) Now, that might seem to contradict the syntax, which is all about MapReduce being invoked via SQL — still, it’s what’s really going on.
That leads to two obvious questions: What is nPath used (or useful) for? and How is the preprocessing done anyway? Read more
| Categories: Aster Data, Data warehousing, MapReduce, Predictive modeling and advanced analytics, Web analytics | 2 Comments |
Aster Data in the cloud
Aster Data is in the news, bragging about a cloud version of nCluster, and providing both a press release and a blog post on the subject. It seems there are three actual customers, two of which have been publicly named. One of them, ShareThis, is in production. (2 terabytes of data on 9 nodes, planning to scale to 10-18 TB on 24 or so nodes by year-end.) All seem to be doing something in the area of internet marketing, web analytics or otherwise — which makes sense, as the same could be said of almost all Aster customers overall. That said, it seems that these customers are doing their primary analytic processing remotely, which makes Aster’s experience in that regard more akin to Kognitio’s than to Vertica’s. Read more