Data mart outsourcing
Discussion of services that analyze large databases on an outsourced basis. Related subjects include:
- Data warehousing
- SaaS (Software as a Service)
- 1010data
- TEOCO
- (in The Monash Report) Verix
- (in Text Technologies) Text mining SaaS
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
Departmental analytics — best practices
I believe IT departments should support and encourage departmental analytics efforts, where “support” and “encourage” are not synonyms for “control”, “dominate”, “overwhelm”, or even “tame”. A big part of that is:
Let, and indeed help, departments have the data they want, when they want it, served with blazing performance.
Three things that absolutely should NOT be obstacles to these ends are:
- Corporate DBMS standards.
- Corporate data governance processes.
- The difficulties of ETL.
| Categories: Business intelligence, Data mart outsourcing, Data warehousing, EAI, EII, ETL, ELT, ETLT, Predictive modeling and advanced analytics | 4 Comments |
Clarifying SAND’s customer metrics, positioning and technical story
Talking with my clients at SAND can be confusing. That said:
- I need to revise my figures for SAND’s customer count way downward.
- SAND finally has a reasonably clear positioning.
- SAND’s product actually seems to have a lot of features.
A few months ago, I wrote:
SAND Technology reported >600 total customers, including >100 direct.
Upon talking with the company, I need to revise that figure downward, from > 600 to 15.
Eight kinds of analytic database (Part 2)
In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear. Read more
More on Sybase IQ, including Version 15.2
Back in March, Sybase was kind enough to give me permission to post a slide deck about Sybase IQ. Well, I’m finally getting around to doing so. Highlights include but are not limited to:
- Slide 2 has some market success figures and so on. (>3100 copies at >1800 users, >200 sales last year)
- Slides 6-11 give more detail on Sybase’s indexing and data access methods than I put into my recent technical basics of Sybase IQ post.
- Slide 16 reminds us that in-database data mining is quite competitive with what SAS has actually delivered with its DBMS partners, even if it doesn’t have the nice architectural approach of Aster or Netezza. (I.e., Sybase IQ’s more-than-SQL advanced analytics story relies on C++ UDFs — User Defined Functions — running in-process with the DBMS.) In particular, there’s a data mining/predictive analytics library — modeling and scoring both — licensed from a small third party.
- A number of the other later slides also have quite a bit of technical crunch. (More on some of those points below too.)
Sybase IQ may have a bit of a funky architecture (e.g., no MPP), but the age of the product and the substantial revenue it generates have allowed Sybase to put in a bunch of product features that newer vendors haven’t gotten around to yet.
More recently, Sybase volunteered permission for me to preannounce Sybase IQ Version 15.2 by a few days (it’s scheduled to come out this week). Read more
Stakeholder-facing analytics
There’s a point I keep making in speeches, and used to keep making in white papers, yet have almost never spelled out in this blog. Let me now (somewhat) correct the oversight.
Analytic technology isn’t only for you. It’s also for your customers, citizens, and other stakeholders.
I am not referring here to what is well understood to be an important, fast-growing activity — providing data and its analysis to customers as your primary or only business — nor to the related business of taking people’s data, crunching it for them, and giving them results. That combined sector — which I am pretty alone in aggregating into one and calling data mart outsourcing — is one of the top several vertical markets for a lot of the analytic DBMS vendors I write about. Rather, I’m talking about enterprises that gather data for some primary purpose, and have discovered that a good secondary use of the data is to reflect it back to stakeholders, often the same ones who provided or created it in the first place.
For now I’ll call this category stakeholder-facing analytics, as the shorter phrase “stakeholder analytics” would be ambiguous.* I first picked up the idea early this decade from Information Builders, for whom it had become something of a specialty. I’ve been asking analytics vendors for examples of stakeholder-facing analytics ever since, and a number have been able to comply. But the whole thing is in its early days even so; almost any sufficiently large enterprise should be more active in stakeholder-facing analytics than it currently is.
Read more
| Categories: Analytic technologies, Business intelligence, Data mart outsourcing, Fox and MySpace, PostgreSQL | 4 Comments |
Infobright blog update
I often offer that, if a company puts up a sufficiently good blog post, I’ll link to it. Well, I just noticed that Infobright CEO Mark Burton (somewhere along the way he seems to have dropped the “interim”) put up an excellent post last month.
Highlights on the market share/sector side include: Read more
| Categories: Columnar database management, Data mart outsourcing, Data warehousing, Infobright, Log analysis, Market share and customer counts, Open source, Web analytics | 1 Comment |
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:
- In essence, Netezza Skimmer is 1/3 of Netezza’s previously smallest appliance, for 1/3 the price.
- I.e., Netezza Skimmer has 1 S-blade and 9 disks, vs. 3 S-blades and 24 disks on the Netezza TwinFin 3.
- With 1 disk reserved as a hot spare, that boils down to a 1:1:1 ratio among CPU cores, FPGA cores, and 1-terabyte disks on Netezza skimmer. The same could pretty much be said of Netezza TwinFin, the occasional hot-spare disk notwithstanding.
- Netezza Skimmer costs $125K.
- With 2.8 or so TB of space for user data before compression, that’s right in line with the Netezza price point of slightly <$20K/terabyte of user data.
- That assumes Netezza’s usual 2.25X compression. I forgot to ask when 4X compression was actually being shipped.
- I forgot to ask, but it seems obvious that Netezza Skimmer uses identical or substantially similar components to Netezza TwinFin’s.
- Netezza Skimmer is 7 rack units high.
- In place of the SMP hosts on TwinFin Systems, Netezza Skimmer has a host blade.
- Netezza (specifically Phil Francisco) mentioned that when Kalido uses Netezza Skimmer for its appliance, there will be an additional host computer, but when it uses TwinFin for the same software, the built-in host will suffice. (Even so, I suspect it might be too strong to say that Skimmer’s built-in host computer is underpowered.)
- Netezza also suggested that more appliance OEMs are coming down the pike specifically focused on the affordable Skimmer.
| Categories: Data mart outsourcing, Data warehouse appliances, Data warehousing, Netezza, Pricing | 2 Comments |
Infobright notes
I had lunch w/ Bob Zurek and Susan Davis of Infobright today. This wasn’t primarily a briefing, but a few takeaways are:
- Infobright now has >100 paying customers.
- Typical database size is from the low 100s of gigabytes to the low single-digit number of terabytes.
- Agile development is at or approaching two-week release cycles.
- Like Kickfire, Infobright has a multi-year deal with MySQL that insulates it against many potential Oracle/MySQL shenanigans.
- From an industry perspective, Infobright’s customer base sounds a lot like other vendors’:
- Data mart outsourcing/online analytics
- Log files for websites
- Telecommunications
- Financial services
- OEM, especially in the markets cited above
- “Hey, we’re beginning to see the occasional energy deal”
- A few random others
- Infobright is seeing some household-name customers, who surely have big-name analytic DBMS products, but who also have a policy that open source is the default choice, and if open source can get the job done then the favorite closed-source choices aren’t used.
- Infobright has the usual open-source community story — lots of involvement and engagement in the forums, but contributions are limited mainly to connectivity, utility scripts, etc. (Maybe some national language translation too; I’m not sure.)
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.
- The Nielsen Company has over half a petabyte of data on Netezza in the US. This installation is growing.
- The Nielsen Company indeed has 45 terabytes or whatever of data on Oracle in its European (Customer) Information Factory. This is not particularly growing. Nielsen’s Oracle data warehouse has been built up over the past 9 years. It’s not new. It’s certainly not on Exadata, nor planned to move to Exadata.
- These are not single-instance databases. Nielsen’s biggest single Netezza database is 20 terabytes or so of user data, and its biggest single Oracle database is 10 terabytes or so.
- Much (most?) of the rest of the installations are customer data marts and the like, based in each case on the “big” central database. (That’s actually a classic data mart use case.) Greg said that Netezza’s capabilities to spin out those databases seemed pretty good.
- That 10 terabyte Oracle data warehouse instance requires a lot of partitioning effort and so on in the usual way.
- Nielsen has no immediate plans to replace Oracle with Netezza.
- Nielsen actually has 800 terabytes or so of Netezza equipment. Some of that is kept more lightly loaded, for performance.