Aster Data
Analysis of data warehouse DBMS vendor Aster Data. Related subjects include:
Correction to a recent quote
I’m quoted in a recent article around Aster’s appliance announcement as saying data warehouse appliances are more suitable for small workgroups of analysts crunching small amounts of data than they are for other uses.
But that’s not what I think at all.
I do think the ease-of-administration pitch for appliances makes them particularly well suited for users who want to scrape by without doing much database adminstration. This is especially appealing to departments or smaller enterprises. And the first/best scenario that comes to mind is indeed a small team of analysts, with good SQL skills but lightweight DBA experience, although Netezza has proved that many other kinds of users can find appliances appealing as well.
But that small team of analysts may maintain the largest database in the firm.
And by the way — notwithstanding the MySpace counterexample, most of Aster’s initial customers had <10 terabyte databases, and I think indeed <5 terabyte. The “frontline” pitch succeeded for Aster before (MySpace again aside) any better-big-data-crunching story did.
| Categories: Analytic technologies, Aster Data, Data warehouse appliances, Data warehousing, Theory and architecture | Leave a Comment |
Aster Data enters the appliance game
Aster Data is rolling out a line of nCluster appliances today. Highlights include:
- Configurations ranging from 9 6.25 terabytes to 1 petabyte of user data. (Edit: Here’s the up-to-date data sheet.)
- A $50K “Express Edition” price for <1 terabyte of user data. Unfortunately, that’s the only stated price.
- The option of bundled MicroStrategy.
- “MapReduce” in the name, which suggests something about the positioning — i.e., enterprise decision support, rather than Aster’s usual web/”frontline” emphasis. (Edit: That also fits with Aster’s recent MapReduce-for-.NET announcement.) (Edit: Actual name is Aster MapReduce Data Warehouse Appliance.)
- Claims that because Aster runs effectively on cheaper, more truly “commodity” hardware than competitors, you get more hardware bang for the buck if you buy from Aster.
I don’t have a lot more to add right now, mainly because I wrote at some length about Aster’s non-appliance-specific, non-MapReduce technology and positioning a couple of weeks ago.
| Categories: Analytic technologies, Aster Data, Business intelligence, Data warehouse appliances, Data warehousing, Database compression, MapReduce, Pricing | 16 Comments |
My current customer list among the analytic DBMS specialists
(This is an updated version of an August, 2008 post.)
One of my favorite pages on the Monash Research website is the list of many current and a few notable past customers. (Another favorite page is the one for testimonials.) For a variety of reasons, I won’t undertake to be more precise about my current customer list than that. But I don’t think it would hurt anything to list the analytic/data warehouse DBMS/appliance specialists in the group. They are:
- Aster Data
- Greenplum
- Infobright
- Kickfire
- Kognitio
- Microsoft
- Netezza (my biggest client this year, probably, because of all the Enzee Universe appearances)
- Sybase
- Teradata
- Vertica
- Attivio, which may or may not be construed as being in the analytic DBMS business
- Clearpace, ditto
All of those are Monash Advantage members.
If you care about all this, you may also be interested in the rest of my standards and disclosures.
| Categories: About this blog, Aster Data, Data warehousing, Greenplum, Infobright, Kickfire, Microsoft and SQL*Server, Netezza, Sybase, Teradata, Vertica Systems | 2 Comments |
Aster Data on parallelism
Aster Data’s core claim boils down to “We do parallelism better.” Aster has shied away from saying that for marketing purposes, for fear of the response “Yeah, right, everybody says that.” But when I talked with Mayank Bawa, Steve Wooledge, et al. yesterday, I focused discussions on just that point. Based on that chat and others before, here are some highlights (as I understand them) of what Aster claims, believes, or believes to be differentiated about its nCluster technology:
| Categories: Analytic technologies, Aster Data, Data warehousing, MapReduce, Parallelization, Theory and architecture | 3 Comments |
Aster Data sticks by its SQL/MapReduce guns
Aster Data continues to think that MapReduce, integrated with SQL, is an important technology. For example:
- Aster announced today that it’s providing .NET support for SQL/MapReduce. Perhaps not coincidentally, Aster’s biggest customer is MySpace, which is apparently a big Microsoft shop. (And MySpace parent Fox Interactive Media is a SQL/MapReduce fan, albeit running on Greenplum.)
- Aster generally puts more emphasis on MapReduce than SQL/MapReduce rival Greenplum. That’s a non-trivial comparison, because Greenplum is making progress in SQL/MapReduce itself.
- When talking with Aster folks, I can’t get them to shut up hear a lot about SQL/MapReduce.
I was a big fan of SQL/MapReduce when it was first announced last August. Notwithstanding persuasive examples favoring pure DBMS or pure MapReduce over DBMS/MapReduce integration, I continue to think the SQL/MapReduce idea has great potential. But I do wish more successful production examples would become visible …
| Categories: Analytic technologies, Aster Data, Data warehousing, Fox and MySpace, Greenplum, MapReduce, Parallelization | 3 Comments |
There always seems to be a fire drill around MapReduce news
Last August I flew out to see my new clients at Greenplum. They told me they planned to roll out MapReduce in a few weeks, and asked for my help in publicizing it. From their offices I went to dinner with non-clients Aster Data, who told me they’d gotten wind of a Greenplum MapReduce announcement and planned to come out ahead of it. A couple of hours later, Aster signed up as a client. In something of a pickle — but not one of my own making — I knocked heads, and persuaded both vendors to announce MapReduce at the same time, namely the following Monday. Lots of publicity ensued for both vendors, and everybody was reasonably satisfied.
| Categories: Analytic technologies, Aster Data, Greenplum, MapReduce, Michael Stonebraker, Vertica Systems | Leave a Comment |
Lots of analytic DBMS vendors are hiring
After writing about a Twitter jobs page, it occurred to me to check out whether analytic DBMS vendors are still hiring. Based on the Careers pages on their websites, I determined that Aster, Greenplum, Kickfire, and ParAccel all evidently are, in various mixes of (mainly) technical and field positions. At that point I got bored and stopped.
I didn’t choose those vendors entirely at random. If I had to name three vendors who are said to have had small layoffs at some point over the past few quarters, it would be ParAccel, Greenplum, and Kickfire. So if even they are hiring, the analytic DBMS sector is still pretty healthy … or at least thinks it is. 
| Categories: Aster Data, Data warehousing, Greenplum, Kickfire, ParAccel | 5 Comments |
Fox Interactive Media’s multi-hundred terabyte database running on Greenplum
Greenplum’s largest named account is Fox Interactive Media — the parent organization of MySpace — which has a multi-hundred terabyte database that it uses for hardcore data mining/analytics. Greenplum has been engaging in regrettable business practices, claiming that it is in the process of supplanting Aster Data at Fox/MySpace. In fact, MySpace’s use of Aster is more mission-critical than Fox’s use of Greenplum, and is increasing significantly.
Still, as Greenplum’s gushing customer video with Fox Interactive Media* illustrates, the Fox/Greenplum database is impressive on its own merits.
| Categories: Analytic technologies, Aster Data, Data warehousing, Fox and MySpace, Greenplum, Specific users, Theory and architecture, Web analytics | 2 Comments |
MySpace’s multi-hundred terabyte database running on Aster Data
Aster Data has put up a blog post embedding and summarizing a video about its MySpace account. Basic metrics include:
The combined Aster deployment now has 200+ commodity hardware servers working together to manage 200+ TB of data that is growing at 2-3TB per day by collecting 7-10B events that happen on one of the world.
I’m pretty sure that’s counting correctly (i.e., user data).*
| Categories: Analytic technologies, Application areas, Aster Data, Data warehousing, Fox and MySpace, Specific users, Theory and architecture, Web analytics | 10 Comments |
MapReduce user eHarmony chose Netezza over Aster or Greenplum
Depending on which IDG reporter you believe, eHarmony has either 4 TB of data or more than 12 TB, stored in Oracle but now analyzed on Netezza. Interestingly, eHarmony is a Hadoop/MapReduce shop, but chose Netezza over Aster Data or Greenplum even so. Price was apparently an important aspect of the purchase decision. Netezza also seems to have had a very smooth POC. Read more
| Categories: Analytic technologies, Application areas, Aster Data, Benchmarks and POCs, Data warehousing, Greenplum, MapReduce, Netezza, Oracle, Pricing | 4 Comments |
An example of Aster Data’s nPath/MapReduce syntax
Perhaps in response to my prior post on Aster Data’s introduction of MapReduce-based nPath, Steve Wooledge of Aster offers a more detailed example. The particular case he works through is:
… the question: for SEO/SEM-driven traffic that stay on our site only for 5 or less pageviews and then leave our site and never return in the same session, what are the top referring search queries and what are the top path of navigated pages on our site?
| Categories: Analytic technologies, Aster Data, Data warehousing, MapReduce, Web analytics | Leave a Comment |
Aster Data nPath
At the same time as it rolled out its cloud story, Aster Data told of nPath, a MapReduce-based feature in nCluster. As best I understand it, the core idea of nPath is that it preprocesses sequential data via MapReduce so that you can then do ordinary SQL on it. (Steve Wooledge’s blog post about nPath outlines why that might be needed. Point 1 in Mayank Bawa’s August, 2008 post is much more concise. ) Now, that might seem to contradict the syntax, which is all about MapReduce being invoked via SQL — still, it’s what’s really going on.
That leads to two obvious questions: What is nPath used (or useful) for? and How is the preprocessing done anyway? Read more
| Categories: Analytic technologies, Aster Data, Data warehousing, MapReduce, Web analytics | 2 Comments |
Aster Data in the cloud
Aster Data is in the news, bragging about a cloud version of nCluster, and providing both a press release and a blog post on the subject. It seems there are three actual customers, two of which have been publicly named. One of them, ShareThis, is in production. (2 terabytes of data on 9 nodes, planning to scale to 10-18 TB on 24 or so nodes by year-end.) All seem to be doing something in the area of internet marketing, web analytics or otherwise — which makes sense, as the same could be said of almost all Aster customers overall. That said, it seems that these customers are doing their primary analytic processing remotely, which makes Aster’s experience in that regard more akin to Kognitio’s than to Vertica’s. Read more
| Categories: Analytic technologies, Application areas, Aster Data, Cloud computing, Data warehousing, MapReduce, Software as a Service (SaaS), Web analytics | 1 Comment |
Draft slides on how to select an analytic DBMS
I need to finalize an already-too-long slide deck on how to select an analytic DBMS by late Thursday night. Anybody see something I’m overlooking, or just plain got wrong?
Edit: The slides have now been finalized.
One vendor’s trash is another’s treasure
A few months ago, CEO Mayank Bawa of Aster Data commented to me on his surprise at how “profound” the relationship was between design choices in one aspect of a data warehouse DBMS and choices in other parts. The word choice in that was all Mayank, but the underlying thought is one I’ve long shared, and that I’m certain architects of many analytic DBMS share as well.
For that matter, the observation is no doubt true in many other product categories as well. But in the analytic database management arena, where there are literally 10-20+ competitors with different, non-stupid approaches, it seems most particularly valid. Here are some examples of what I mean.
| Categories: Aster Data, Data warehousing, Exadata, Kognitio, Oracle, Theory and architecture, Vertica Systems | 22 Comments |
Gartner’s 2008 data warehouse database management system Magic Quadrant is out
Gartner’s annual Magic Quadrant for data warehouse DBMS is out. Thankfully, vendors don’t seem to be taking it as seriously as usual, so I didn’t immediately hear about. (I finally noticed it in a Greenplum pay-per-click ad.) Links to Gartner MQs tend to come and go, but as of now here are two working links to the 2008 Gartner Data Warehouse Database Management System MQ. My posts on the 2007 and 2006 MQs have also been updated with working links. 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:
| Categories: Analytic technologies, Aster Data, Data warehousing, EAI, EII, ETL, ELT, ETLT, Greenplum, MapReduce, Netezza, Oracle, Parallelization, SAS Institute, Teradata | 5 Comments |
Big scientific databases need to be stored somehow
A year ago, Mike Stonebraker observed that conventional DBMS don’t necessarily do a great job on scientific data, and further pointed out that different kinds of science might call for different data access methods. Even so, some of the largest databases around are scientific ones, and they have to be managed somehow. For example:
- Microsoft just put out an overwrought press release. The substance seems to be that Pan-STARRS — a Jim Gray legacy also discussed in an August, 2008 Computerworld article — is adding 1.4 terabytes of image data per night, and one not so new database adds 15 terabytes per year of some kind of computer simulation output used to analyze protein folding. Both run on SQL Server, of course.
- Kognitio has an astronomical database too, at Cambridge University, adding 1/2 a terabyte of data per night.
- Oracle is used for a McGill University proteonomics database called CellMapBase. A figure of 50 terabytes of “mass storage” is included, which doesn’t include tape backup and so on.
- The Large Hadron Collider, once it actually starts functioning, is projected to generate 15 petabytes of data annually, which will be initially stored on tape and then distributed to various computing centers around the world.
- Netezza is proud of its ability to serve images and the like quickly, although off the top of my head I’m not thinking of a major customer it has in that area. (But then, if you just sell software, your academic discount can approach 100%; but if like Netezza you have an actual cost of goods sold, that’s not as appealing an option.)
Long-term, I imagine that the most suitable DBMS for these purposes will be MPP systems with strong datatype extensibility — e.g., DB2, PostgreSQL-based Greenplum, PostgreSQL-based Aster nCluster, or maybe Oracle.
| Categories: Aster Data, Data types, Greenplum, IBM and DB2, Kognitio, Microsoft and SQL*Server, Netezza, Oracle, Parallelization, PostgreSQL, Scientific research | 1 Comment |
Update on Aster Data Systems and nCluster
I spent a few hours at Aster Data on my West Coast swing last week, which has now officially put out Version 3 of nCluster. Highlights included:
| Categories: Application areas, Aster Data, Data warehousing, Database compression, MapReduce, Market share, Parallelization, Specific users, Theory and architecture, Web analytics | 2 Comments |
Aster Data on online marketing data warehousing
Aster Data’s blog is getting to be like Vertica’s, in that I find myself recommending a large fraction of its posts.
The virtue of the latest one is that it strings together several customer examples in related areas of online marketing (which is pretty much the only sector Aster has so far sold into). I’ve tended to overgeneralize a bit, and use terms like “web analytics” or “clickstream analysis” even when they don’t wholly apply. The Aster post is a good antidote to that.
| Categories: Application areas, Aster Data, Data warehousing, Web analytics | 1 Comment |
Aster Data has a new release
Aster and I got our scheduling signals crossed, and I haven’t been briefed in detail yet. But Aster Data has a new release, and as usual is doing a great job telling their story in their own blog. The post summarizing nCluster 3.0 is here.
| Categories: Aster Data | Leave a Comment |
Web analytics — clickstream and network event data
It should surprise nobody that web analytics – and specifically clickstream data — is one of the biggest areas for high-end data warehousing. For example:
- I believe that both of the previously mentioned petabyte+ databases on Greenplum will feature clickstream data.
- Aster Data’s largest disclosed database, by almost two orders of magnitude, is at MySpace.
- Clickstream analytics is a big application area for Vertica Systems.
- Clickstream analytics is a big application area for Netezza.
- Infobright’s customer success stories appear to be concentrated in clickstream analytics.
- Coral8 tells me that CEP is also being used for clickstream data, although I suspect that a lot of Coral8’s evidence in that regard comes from a single flagship account. Edit: Actually, Coral8 has a bunch of clickstream customers.
| Categories: Aleri and Coral8, Aster Data, Complex event processing (CEP), Greenplum, Infobright, Netezza, Vertica Systems, Web analytics | 2 Comments |
Dividing the data warehousing work among MPP nodes
I talk with lots of vendors of MPP data warehouse DBMS. I’ve now heard enough different approaches to MPP architecture that I think it might be interesting to contrast some of the alternatives.
| Categories: Aster Data, Calpont, Exasol, Greenplum, Parallelization, Theory and architecture, Vertica Systems | 21 Comments |
Three different implementations of MapReduce
So far as I can see, there are three implementations of MapReduce that matter for enterprise analytic use – Hadoop, Greenplum’s, and Aster Data’s.* Hadoop has of course been available for a while, and used for a number of different things, while Greenplum’s and Aster Data’s versions of MapReduce – both in late-stage beta – have far fewer users.
*Perhaps Nokia’s Disco or another implementation will at some point join the list.
Earlier this evening I posted some Mike Stonebraker criticisms of MapReduce. It turns out that they aren’t all accurate across all MapReduce implementations. So this seems like a good time for me to stop stalling and put up a few notes about specific features of different MapReduce implementations. Here goes.
| Categories: Aster Data, Greenplum, MapReduce | 2 Comments |
Introduction to Aster Data and nCluster
I’ve been writing a lot about Greenplum since a recent visit. But on the same trip I met with Aster Data, and have talked with them further since. Let me now redress the balance and outline some highlights of the Aster Data story.
