Aster Data
Analysis of data warehouse DBMS vendor Aster Data. Related subjects include:
Aster Data nCluster Version 4.6
The main thing in Aster Data nCluster Version 4.6 is Aster’s version of hybrid row-column store technology. Technical highlights include:
- Aster Data is simply taking the number of storage options in nCluster up from 1 to 2 – you now can store a table either in the Aster Data nCluster row store or column store.
- In fact, you can store parts of a table in the Aster Data nCluster row store and other parts in the Aster Data nCluster column store. I‘m a bit foggy on the details of that – Aster makes discussions of partitioning more complicated than they need to be — but it definitely sounds pretty flexible. Edit: See comment thread below.
- Anything you can do with the Aster Data nCluster row store you can also do with the Aster Data nCluster column store. In particular, that includes all of Aster Data’s analytic functionality.
- The same is true vice-versa. There is no columnar-oriented kind of compression in Aster Data nCluster at this time.
So Aster Data has now joined Greenplum/EMC among row-based analytic DBMS vendors with hybrid row-column stores. Oracle will join them some day, and the same probably applies to other row-based vendors as well. Similarly, Aster Data will probably join Oracle some day in having columnar compression. And so this all fits the model:
- Aster Data has an impressively competitive analytic relational DBMS, considering the youth and size of the company.
- Aster Data is a leader in extending its analytic relational DBMS by integrating in other analytic processing capabilities.
| Categories: Analytic technologies, Aster Data, Columnar database management, Data warehousing, Database compression | 4 Comments |
Big Data is Watching You!
There’s a boom in large-scale analytics. The subjects of this analysis may be categorized as:
- People
- Financial trades
- Electronic networks
- Everything else
The most varied, interesting, and valuable of those four categories is the first one.
Links and observations
I’m back from a trip to the SF Bay area, with a lot of writing ahead of me. I’ll dive in with some quick comments here, then write at greater length about some of these points when I can. From my trip: Read more
| Categories: Analytic technologies, Aster Data, Calpont, Cassandra, Couchbase, Data warehouse appliances, Data warehousing, EMC, Exadata, Facebook, Greenplum, HP and Neoview, Kickfire, NoSQL, OLTP, ParAccel, Sybase, XtremeData | 1 Comment |
Breakthrough: Exadata now has as many reference accounts as Aster Data!
According to Bob Evans of Information Week, there now are 15 disclosed Exadata reference accounts. Coincidentally, there are exactly 15 logos on Aster Data’s customer page. So on its own, that’s not a particularly impressive piece of information.
But other highlights of his column include:
- Some of those accounts are rather big-name. However, I’m not at all sure whether they’re actual production references.
- Andy Mendelsohn characterizes the sweet spot of Exadata’s market as “virtual private cloud.” That matches what Juan Loaiza told me six months ago.
- Oracle claims numerous competitive wins for Exadata. Let me hasten to note that one vendor’s “competitive win” is another vendor’s “our salesman read the deal as an unfavorable one and chose not to compete,” or even sometimes “Huh? We never heard about that deal.” That said, what I’m hearing is that Exadata is indeed a much stronger competitor than it used to be.
- Oracle claims a near $1 billion sales run rate for Exadata. No doubt, a large majority of those are hardware upgrades for existing Oracle database customers, often from non-Sun/Oracle hardware. Even so, some of those are surely deals that would have migrated away from Oracle in the pre-Exadata past.
| Categories: Aster Data, Data warehousing, Exadata, Market share and customer counts, Oracle | 1 Comment |
Lots of Aster Data analytic packages
A number of vendors had announcements last week, notably:
- Netezza (user conference)
- Aster Data (to steal some of Netezza’s thunder)
- Infobright (so far as I can tell, just because it was time for a product release, and also to get ahead of the summer doldrums)
- Northscale (ditto)
Time to play some catchup.
I’ll start with Aster Data, which added to the list of analytic packages it previously announced, and kindly gave me permission to post a partial slide deck from the briefing on same. Highlights of Aster’s analytic packages story include: Read more
What kinds of data warehouse load latency are practical?
I took advantage of my recent conversations with Netezza and IBM to discuss what kinds of data warehouse load latency were practical. In both cases I got the impression:
- Subsecond load latency is substantially impossible. Doing that amounts to OLTP.
- 5 seconds or so is doable with aggressive investment and tuning.
- Several minute load latency is pretty easy.
- 10-15 minute latency or longer is now very routine.
There’s generally a throughput/latency tradeoff, so if you want very low latency with good throughput, you may have to throw a lot of hardware at the problem.
I’d expect to hear similar things from any other vendor with reasonably mature analytic DBMS technology. Low-latency load is a problem for columnar systems, but both Vertica and ParAccel designed in workarounds from the getgo. Aster Data probably didn’t meet these criteria until Version 4.0, its old “frontline” positioning notwithstanding, but I think it does now.
Related link
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Just what is your need for speed anyway?
| Categories: Analytic technologies, Aster Data, Columnar database management, Data warehousing, IBM and DB2, Netezza, ParAccel, Vertica Systems | 4 Comments |
Further clarifying in-database MPP SAS
My recent post about SAS’ MPP/in-database efforts was based on a discussion in a shared ride to the airport, and was correspondingly rough. SAS’ Shannon Heath was kind enough to write in with clarifications, and to allow me to post same. Read more
| Categories: Aster Data, Netezza, Parallelization, Predictive modeling and advanced analytics, SAS Institute | 4 Comments |
Notes and cautions about new analytic technology
As previously noted, I headlined Aster’s Big Data Summit in Washington, DC last Thursday. More than others, that talk did reuse material I’d presented before. I promised the audience that when I got back I’d put up a blog post linking to supporting material for the talk.
Part of the time, I talked about things I’ve written about before. For example: Read more
| Categories: Aster Data, Business intelligence, Data warehousing, Predictive modeling and advanced analytics, Presentations | 3 Comments |
Clarifying the state of MPP in-database SAS
I routinely am briefed way in advance of products’ introductions. For that reason and others, it can be hard for me to keep straight what’s been officially announced, introduced for test, introduced for general availability, vaguely planned for the indefinite future, and so on. Perhaps nothing has confused me more in that regard than the SAS Institute’s multi-year effort to get SAS integrated into various MPP DBMS, specifically Teradata, Netezza Twinfin(i), and Aster Data nCluster.
However, I chatted briefly Thursday with Michelle Wilkie, who is the SAS product manager overseeing all this (and also some other stuff, like SAS running on grids without being integrated into a DBMS). As best I understood, the story is: Read more
| Categories: Aster Data, Data warehouse appliances, MapReduce, Netezza, Parallelization, Predictive modeling and advanced analytics, SAS Institute, Specific users, Teradata | 11 Comments |
I’ll be speaking in Washington, DC on May 6
My clients at Aster Data are putting on a sequence of conferences called “Big Data Summit(s)”, and wanted me to keynote one. I agreed to the one in Washington, DC, on May 6, on the condition that I would be allowed to start with the same liberty and privacy themes I started my New England Database Summit keynote with. Since I already knew Aster to be one of the multiple companies in this industry that is responsibly concerned about the liberty and privacy threats we’re all helping cause, I expected them to agree to that condition immediately, and indeed they did.
On a rough-draft basis, my talk concept is:
Implications of New Analytic Technology in four areas:
- Liberty & privacy
- Data acquisition & retention
- Data exploration
- Operationalized analytics
I haven’t done any work yet on the talk besides coming up with that snippet, and probably won’t until the week before I give it. Suggestions are welcome.
If anybody actually has a link to a clear discussion of legislative and regulatory data retention requirements, that would be cool. I know they’ve exploded, but I don’t have the details.