Aster Data

Analysis of data warehouse DBMS vendor Aster Data. Related subjects include:

February 5, 2011

Comments on the Gartner 2010/2011 Data Warehouse Database Management Systems Magic Quadrant

Edit: Comments on the February, 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems — and on the companies reviewed in it — are now up.

The Gartner 2010 Data Warehouse Database Management Systems Magic Quadrant is out. I shall now comment, just as I did to varying degrees on the 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants.

Note: Links to Gartner Magic Quadrants tend to be unstable. Please alert me if any problems arise; I’ll edit accordingly.

In my comments on the 2008 Gartner Data Warehouse Database Management Systems Magic Quadrant, I observed that Gartner’s “completeness of vision” scores were generally pretty reasonable, but their “ability to execute” rankings were somewhat bizarre; the same remains true this year. For example, Gartner ranks Ingres higher by that metric than Vertica, Aster Data, ParAccel, or Infobright. Yet each of those companies is growing nicely and delivering products that meet serious cutting-edge analytic DBMS needs, neither of which has been true of Ingres since about 1987.  Read more

January 24, 2011

Choices in analytic computing system design

When I posted a long list of architectural options for analytic DBMS, I left a couple of IOUs in for missing parts. One was in the area of what is sometimes called advanced-analytics functionality, which roughly speaking means aspects of analytic database management systems that are not directly related to conventional* SQL queries.

*Main examples of “conventional” = filtering, simple aggregrations.

The point of such functionality is generally twofold. First, it helps you execute analytic algorithms with high performance, due to reducing data movement and/or executing the analytics in parallel. Second, it helps you create and execute sophisticated analytic processes with (relatively) little effort.

For now, I’m going to refer to an analytic RDBMS that has been extended by advanced-analytics functionality as an analytic computing system, rather than as some kind of “platform,” although I suspect the latter term is more likely to wind up winning.  So far, there have been five major categories of subsystem or add-on module that contribute to making an analytic DBMS a more fully-fledged analytic computing system:

Read more

January 19, 2011

Sound bites on HP/Microsoft and Neoview

HP and Microsoft put out a press release.  Three new appliances are being announced, and we’re being reminded of at least one past announcement. I wasn’t briefed, and wouldn’t want to comment on, say, price/performance or feature particulars. That said:

January 18, 2011

Architectural options for analytic database management systems

Mike Stonebraker recently kicked off some discussion about desirable architectural features of a columnar analytic DBMS. Let’s expand the conversation to cover desirable architectural characteristics of analytic DBMS in general.  Read more

January 12, 2011

Mike Stonebraker on “real column stores”

Mike Stonebraker has a post up on Vertica’s blog trying to differentiate “real” from “pretend” column stores. (Edit: That post seems to have come back down, but as of 1/19 it can be found in Google Cache.) In essence, Mike argues that the One Right Way to design a column store is Vertica’s, a position that Daniel Abadi used to share but since has retreated from.

There are some good things about that post, and some not-so-good. The worst paragraph is probably

Several row-store vendors (including Oracle, Greenplum and Aster Data) now claim to be selling a column store.   Obviously, this would require a complete rewrite of a DBMS to move from Figure 1 to Figure 2.  Hence, none of the “pretenders” have actually done this.  Instead all have implemented some aspects of column stores, and then claim to be the real thing.  This blog defines what the “real enchilada” looks like, and how to tell it from the pretenders.

which I question on two levels. Read more

October 22, 2010

Notes and links October 22, 2010

A number of recent posts have had good comments. This time, I won’t call them out individually.

Evidently Mike Olson of Cloudera is still telling the machine-generated data story, exactly as he should be. The Information Arbitrage/IA Ventures folks said something similar, focusing specifically on “sensor data” …

… and, even better, went on to say:  Read more

October 18, 2010

More notes on Membase and memcached

As a companion to my post about Membase last week, the company has graciously allowed me to post a rather detailed Membase slide deck. (It even has pricing.) Also, I left one point out.

Membase announced a Cloudera partnership. I couldn’t detect anything technically exciting about that, but it serves to highlight what I do find to be an interesting usage trend. A couple of big Web players (AOL and ShareThis) are using Hadoop to crunch data and derive customer profile data, then feed that back into Membase. Why Membase? Because it can serve up the profile in a millisecond, as part of a bigger 40-millisecond-latency request.

And why Hadoop, rather than Aster Data nCluster, which ShareThis also uses? Umm, I didn’t ask.

When I mentioned this to Colin Mahony, he said Vertica had similar stories. However, I don’t recall whether they were about Membase or just memcached, and he hasn’t had a chance to get back to me with clarification.  (Edit: As per Colin’s comment below, it’s both.)

October 10, 2010

Partnering with Cloudera

After I criticized the marketing of the Aster/Cloudera partnership, my clients at Aster Data and Cloudera ganged up on me and tried to persuade me I was wrong. Be that as it may, that conversation and others were helpful to me in understanding the core thesis:  Read more

October 10, 2010

Notes and links October 10 2010

More quick-hit notes, links, and so on:  Read more

October 10, 2010

It can be hard to analyze analytics

When vendors talk about the integration of advanced analytics into database technology, confusion tends to ensue. For example: Read more

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