SAS Institute
Analysis of data mining powerhouse SAS, and the especially the relationship between SAS’s data mining products and various database management systems. Related subjects include:
- Business intelligence
- (in The Monash Report) Data mining
- (in Text Technologies) SAS’ offerings in text mining
TwinFin(i) – Netezza’s version of a parallel analytic platform
Much like Aster Data did in Aster 4.0 and now Aster 4.5, Netezza is announcing a general parallel big data analytic platform strategy. It is called Netezza TwinFin(i), it is a chargeable option for the Netezza TwinFin appliance, and many announced details are on the vague side, with Netezza promising more clarity at or before its Enzee Universe conference in June. At a high level, the Aster and Netezza approaches compare/contrast as follows: Read more
| Categories: Analytic technologies, Aster Data, Data warehouse appliances, Data warehousing, Hadoop, MapReduce, Netezza, SAS Institute, Teradata | 2 Comments |
Aster Data nCluster 4.5
Like Vertica, Netezza, and Teradata, Aster is using this week to pre-announce a forthcoming product release, Aster Data nCluster 4.5. Aster is really hanging its identity on “Big Data Analytics” or some variant of that concept, and so the two major named parts of Aster nCluster 4.5 are:
- Aster Data Analytic Foundation, a set of analytic packages prebuilt in Aster’s SQL-MapReduce
- Aster Data Developer Express, an Eclipse-based IDE (Integrated Development Environment) for developing and testing applications built on Aster nCluster, Aster SQL-MapReduce, and Aster Data Analytic Foundation
And in other Aster news:
- Along with the development GUI in Aster nCluster 4.5, there is also a new administrative GUI.
- Aster has certified that nCluster works with Fusion I/O boards, because at least one retail industry prospect cares. However, that in no way means that arm’s-length Fusion I/O certification is Aster’s ultimate solid-state memory strategy.
- I had the wrong impression about how far Aster/SAS integration has gotten. So far, it’s just at the connector level.
Aster Data Developer Express evidently does some cool stuff, like providing some sort of parallelism testing right on your desktop. It also generates lots of stub code, saving humans from the tedium of doing that. Useful, obviously.
But mainly, I want to write about the analytic packages. Read more
| Categories: Analytic technologies, Aster Data, Data warehousing, Investment research and trading, RDF and graphs, SAS Institute, Teradata | 1 Comment |
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.
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, Netezza, SAS Institute | 4 Comments |
Teradata 13 focuses on advanced analytic performance
Last October I wrote about the Teradata 13 release of Teradata’s database management software. Teradata 13, which will be used across the various Teradata product lines, has now been announced for GCA (General Customer Availability)*. So far as I can tell, there were two main points of emphasis for Teradata 13:
- Performance (of course, performance is a point of emphasis for almost any release of any analytic DBMS product), especially but not only in the areas of aggregates, ETL (Extract/Transform/Load), and UDFs.
- UDFs (User Defined Functions), especially but not only in the areas of data mining and geospatial analysis.
To put it even more concisely, the focus of Teradata 13 is on advanced analytic performance, although there of course are some enhancements in simple query performance and in analytic functionality as well. Read more
Initial reactions to IBM acquiring SPSS
IBM is acquiring SPSS. My initial thoughts (questions by Eric Lai of Computerworld) include:
1) good buy for IBM? why or why not?
Yes. The integration of predictive analytics with other analytic or operational technologies is still ahead of us, so there was a lot of value to be gained from SPSS beyond what it had standalone. (That said, I haven’t actually looked at the numbers, so I have no comment on the price.)
By the way, SPSS coined the phrase “predictive analytics”, with the rest of the industry then coming around to use it. As with all successful marketing phrases, it’s somewhat misleading, in that it’s not wholly focused on prediction.
2) how does it position IBM vs. competitors?
IBM’s ownership immediately makes SPSS a stronger competitor to SAS. Any advantage to the rest of IBM depends on the integration roadmap and execution.
3) How does this particularly affect SAP and SAS and Oracle, IBM’s closest competitors by revenue according to IDC’s figures?
If one of Oracle or SAP had bought SPSS, it would have given them a competitive advantage against the other, in the integration of predictive analytics with packaged operational apps. That’s a missed opportunity for each.
One notable point is that SPSS is more SQL-oriented than SAS. Thus, SPSS has gotten performance benefits from Oracle’s in-database data mining technology that SAS apparently hasn’t.
IBM’s done a good job of keeping its acquired products working well with Oracle and other competitive DBMS in the past, and SPSS will surely be no exception.
Obviously, if IBM does a good job of Cognos/SPSS integration, that’s bad for competitors, starting with Oracle and SAP/Business Objects. So far business intelligence/predictive analytics integration has been pretty minor, because nobody’s figured out how to do it right, but some day that will change. Hmm — I feel another “Future of … ” post coming on.
4) Do you predict further M&A?
Always. 
Related links
- Official word from SPSS and IBM
- Blog posts from Larry Dignan and James Taylor
- James Kobelius’s post, which includes the obvious point that Oracle — unlike SAP — has pretty decent data mining of its own
- Eric Lai’s actual article
| Categories: Analytic technologies, Cognos, IBM and DB2, Oracle, SAP AG, SAS Institute | 7 Comments |
SAS in its own cloud
The Register has a fairly detailed article about SAS expanding its cloud/SaaS offerings. I disagree with one part, namely:
SAS may not have a choice but to build its own cloud. Given the sensitive nature of the data its customers analyze, moving that data out to a public cloud such as the Amazon EC2 and S3 combo is just not going to happen.
And even if rugged security could make customers comfortable with that idea, moving large data sets into clouds (as Sun Microsystems discovered with the Sun Grid) is problematic. Even if you can parallelize the uploads of large data sets, it takes time.
But if you run the applications locally in the SAS cloud, then doing further analysis on that data is no big deal. It’s all on the same SAN anyway, locked down locally just as you would do in your own data center.
I fail to see why SAS’s campus would be better than leading hosting companies’ data centers for either of data privacy/security or data upload speed. Rather, I think major reasons for SAS building its own data center for cloud computing probably focus on: Read more
| Categories: SAS Institute, Software as a Service (SaaS) | 15 Comments |
Gartner’s 2009 Magic Quadrant for Business Intelligence
A few days ago I tore into the Gartner Magic Quadrant for Data Warehouse DBMS. Well, the 2009 Gartner Magic Quadrant for Business Intelligence Platforms is out too. (Link here. Last year’s here. Hat tip for both to Doug Henschen.) Unlike the data warehouse MQ, Gartner’s BI MQ clusters its “Leaders” together tightly. But while less bold, the Business Intelligence Magic Quadrant’s claims are just as questionable as those in data warehousing.
Of course, some parts do make sense. E.g.: Read more
High-performance analytics
For the past few months, I’ve collected a lot of data points to the effect that high-performance analytics – i.e., beyond straightforward query — is becoming increasingly important. And I’ve written about some of them at length. For example:
- MapReduce – controversial or in some cases even disappointing though it may be – has a lot of use cases.
- It’s early days, but Netezza and Teradata (and others) are beefing up their geospatial analytic capabilities.
- Memory-centric analytics is in the spotlight.
Ack. I can’t decide whether “analytics” should be a singular or plural noun. Thoughts?
Another area that’s come up which I haven‘t blogged about so much is data mining in the database. Data mining accounts for a large part of data warehouse use. The traditional way to do data mining is to extract data from the database and dump it into SAS. But there are problems with this scenario, including: Read more
| Categories: Analytic technologies, Aster Data, Data warehousing, EAI, EII, ETL, ELT, ETLT, Greenplum, MapReduce, Netezza, Oracle, Parallelization, SAS Institute, Teradata | 5 Comments |
MapReduce for data mining? Maybe for variable-schema analytics.
Rich Skrenta is quite a successful entrepreneur, so it’s likely that he doesn’t really mean the more ridiculous parts of this rant on the MapReduce debate. E.g., he cheerfully disregards the fact that the data warehouse appliance vendors have ALREADY disrupted the market he’s focusing on. Index-light row-based and columnar systems are both super fast at data mining extracts.
But let’s go straight to the one interesting thing he said, Read more
| Categories: Analytic technologies, MapReduce, Parallelization, SAS Institute | 2 Comments |
Intelligent Enterprise’s list of 12/36/48 vendors
I’m getting a flood of press releases today, because many of the companies I write about were selected to Intelligent Enterprise’s list of 12 most influential vendors plus 36 more to watch in the areas Intelligent Enterprise covers (which seems to be pretty much the analytics-related parts of what I write about here and on Text Technologies). It looks like a pretty reasonable list, although I think they forced the issue in some of the small analytics vendors they selected, and of course anybody can quibble with some of the omissions.
Among the companies they cited, you can find topical categories here for IBM (and Cognos), Informatica, Microsoft, Netezza, Oracle, SAP/Business Objects (both), SAS, and Teradata; QlikTech; Cast Iron, Coral8, DATAllegro, HP, ParAccel, and StreamBase; and Software AG. On Text Technologies you’ll find categories for some of the same vendors, plus Attensity, Clarabridge, and Google. There also are categories for some of these vendors on the Monash Report.