Data warehousing
Analysis of issues in data warehousing, with extensive coverage of database management systems and data warehouse appliances that are optimized to query large volumes of data. Related subjects include:
HP and Neoview update
I had lunch with some HP folks at TDWI. Highlights (burgers and jokes aside) included:
- HP’s BI consulting (especially the former Knightsbridge) and analytic product groups (including Neoview) are now tightly integrated.
- HP is trying to develop and pitch “solutions” where it has particular “intellectual property.” This IP can come from ordinary product engineering or internal use, because HP Labs serves both sides of the business. Specific examples offered included:
- Telecom. Apparently, HP made specialized data warehouse devices for CDRs (Call Detail Records) long ago, and claims this has been area of particular expertise ever since.
- Supply chain – based on HP’s internal experiences.
- Customer relationship – ditto
- The main synergy suggested between consulting and Neoview is that HP’s experts work on talking buyers into such a complex view of their requirements that only Neoview (supposedly) can fit the bill.
- HP insists there are indeed new Neoview sales.
- Neoview sales seem to be concentrated in what Aster might call “frontline” applications — i.e., low latency, OLTP-like uptime requirements, etc.
- HP says it did an actual 80 TB POC. I asked whether this was for an 80 TB app or something a lot bigger, but didn’t get a clear answer.
Given the emphasis on trying to exploit HP’s other expertise in the data warehousing business, I suggested it was a pity that HP spun off Agilent (HP’s instrumentation division, aka HP Classic). Nobody much disagreed.
| Categories: Analytic technologies, Business intelligence, Data warehouse appliances, Data warehousing, HP and Neoview, Telecommunications | 4 Comments |
Microsoft SQL Server Fast Track
Stuart Frost of Microsoft (nee’ DATAllegro) checked in, with Microsoft’s TDWI-timed announcements. The news part was something called “SQL Server Fast Track“, which is the Microsoft SQL Server equivalent to Oracle’s “recommended configurations” or IBM’s “BCUs.” SQL Server Fast Track is further being portrayed as an incremental step toward Madison, Microsoft’s future high-end data warehousing offering.
| Categories: Data warehousing, Microsoft and SQL*Server, Pricing | 5 Comments |
The questionable benefits of terabyte-scale data warehouse virtualization
Vertica is virtualizing via VMware, and has suggested a few operational benefits to doing so that might or might not offset VMware’s computational overhead. But on the whole,it seems virtualization’s major benefits don’t apply to the large-database MPP data warehousing. Read more
| Categories: Columnar database management, Data warehousing, Database compression, Theory and architecture, Vertica Systems | 2 Comments |
Vertica Virtualizes Via VMware
(In other news, the sixth sick sheik’s sixth sheep is sick … but I digress.)
It seems that every analytic DBMS vendor feels compelled to issue at least one press release the week of winter TDWI. Vertica’s grand revelation this year is that you can use Vertica with VMware.* Of course, VMware working the way it does, you in fact have always been able to use Vertica with VMware. But now things are slightly improved, because Vertica has built install packages you can download, and has been working out recommended configuration settings as well.
| Categories: Data warehousing, Vertica Systems | 2 Comments |
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 |
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 |
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 |