Predictive modeling and advanced analytics
Discussion of technologies and vendors in the overlapping areas of predictive analytics, predictive modeling, data mining, machine learning, Monte Carlo analysis, and other “advanced” analytics.
Other notes on Oracle data warehousing
Obviously, the big news this week is Exadata, and its parallelization or lack thereof. But let’s not forget the rest of Oracle’s data warehousing technology.
- Frankly, I’ve come to think that disk-based OLAP cubes and materialized views are both cop-outs, indicative of a relational data warehouse architecture that can’t answer queries quickly enough straight-up. But if you disagree, then you might like Oracle’s new OLAP cube materialized views, which sound like a worthy competitor to Microsoft Analysis Services. (Further confusing things, I’ve seen reports that Oracle is increasing its commitment to Essbase, a separate MOLAP engine. I hope those are incorrect.)
- A few weeks ago, I came to realize that Oracle’s data mining database features actually mattered — perhaps not quite as much as Charlie Berger might think, but to say that is to praise with faint damns. 😉 SPSS seems to be getting large performance gains from leveraging the scoring part, and perhaps the transformation part as well. I haven’t focused on getting my details right yet, so I haven’t been writing about it. But heck, with all the other Oracle data warehousing discussion, it seems right to at least mention this part too.
SAS gets close to the database
One of the big announcements at the Teradata user conference this week (confusingly named “Partners”) is SAS integration. Now, SAS is integrating with other MPP data warehouse appliance vendors as well, but it’s likely that the Teradata integration is indeed the most advanced. For example, one customer proofpoint offered was an insurer who used this capability to reevaluate its risk profile at high speed after Hurricane Katrina. I doubt any of the other SAS/DBMS integrations I know of were in customer hands a year ago.
Three still-open questions I hope to address over the next couple of days are: Read more
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, Predictive modeling and advanced analytics, SAS Institute, Teradata | Leave a Comment |
Data mining is driving much of data warehousing
Until I did all this recent research on data warehousing, I didn’t realize just how big a role data mining plays in driving the whole thing. Basically, there are three things you can do with a data warehouse – classical BI, “operational” BI, and data mining. If we’re talking about long-running queries, that’s not operational BI, and it’s not all of classical BI either. The rest is data mining. Indeed, if you think back to what you know of the customer bases at data warehouse appliance vendors Netezza and DATallegro, there are a lot of credit-reporting-data types of users – i.e., data miners. And it’s hard to talk about uses for those appliances very long without SAS extracts and the like coming up.
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