Data warehousing
Analysis of issues in data warehousing, with extensive coverage of database management systems and data warehouse appliances that are optimized to query large volumes of data. Related subjects include:
Xtreme Data readies a different kind of FPGA-based data warehouse appliance
Xtreme Data called me to talk about its plans in the data warehouse appliance business, almost all details of which are currently embargoed. Still, a few points may be worth noting ahead of more precise information, namely:
- Xtreme Data’s basic idea is to take a custom board and build a data warehouse appliance around it.
- An Xtreme Data board looks a lot like a conventional two-socket board, but has only one four-core CPU. In addition, it sports some FPGAs (Field-Programmable Gate Arrays).
- In the Xtreme Data appliance, the FPGAs will be used for core SQL processing, after the data is ingested via conventional I/O. This is different from Netezza’s approach to FPGA-based data warehouse appliances, in which the FPGA sits in the place of a disk controller and touches the data first, before passing it off to a more or less conventional CPU.
- While preparing entry into the data warehouse appliance business, Xtreme Data has sold its board to 150 other outfits, many quite impressive. Buyers seem to be FPGA users who previously had to craft their own custom boards. According to Xtreme Data, major uses by these customers include:
- Military/intelligence/digital signal processing.
- Military/intelligence/cybersecurity (a newish area for Xtreme Data)
- Bioinformatics/high-throughput gene sequencing (a “handful” of customers)
- Medical imaging
- More or less pure university research of various sorts (around 50 customers)
- … but not database management.
- Xtreme Data’s website has a non-obvious URL. 🙂
So far as I can tell, Xtreme Data’s 1.0 product will — like most other 1.0 analytic database management products — be focused on price/performance, without little or no positive differentiation in the way of features.
| Categories: Data warehouse appliances, Data warehousing, Netezza, Theory and architecture, XtremeData | 6 Comments |
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 | 4 Comments |
ParAccel pricing
As I noted in connection with ParAccel’s recent TPC-H filing, I think the whole exercise is basically an expensive joke. But one slightly useful spin-off is that ParAccel disclosed pricing. Specifically, ParAccel’s stated price in the disclosure document is:
- $100,000/TB license fee (user data). That’s like Vertica, although I don’t know whether ParAccel emulates Vertica’s policy of making test and development licenses free.
- 57% quantity discount at 30 terabytes. That’s not surprising.
- 1% annual maintenance fee (applied to the discounted price). That’s astounding.
Last year ParAccel quoted prices of $100,000/TB or $50,000/server. The latter figure would seem to have led to lower numbers on the benchmark configuration, so perhaps it’s no longer an option on ParAccel’s price list.
| Categories: Benchmarks and POCs, Data warehousing, ParAccel, Pricing | 3 Comments |
The TPC-H benchmark is a blight upon the industry
ParAccel has released a 30,000-gigabtye TPC-H benchmark, and no less a sage than Merv Adrian paid attention. Now, the TPCs may have had some use in the 1990s. Indeed, Merv was my analyst relations contact for a visit to my clients at Sybase around the time — 1996 or so — I was advising Sybase on how to market against its poor benchmark results. But TPCs are worthless today.
It’s not just that TPCs are highly tuned (ParAccel’s claim of “load-and-go” is laughable Edit: Looking at Appendix A of the full disclosure report, maybe it’s more justified than I thought.). It’s also not just that different analytic database management products perform very differently on different workloads, making the TPC-H not much of an indicator of anything real-life. The biggest problem is: Most TPC benchmarks are run on absurdly unrealistic hardware configurations.
For example, if you look at some details, the ParAccel 30-terabyte benchmark ran on 43 nodes, each with 64 gigabytes of RAM and 24 terabytes of disk. That’s 961,124.9 gigabytes of disk, officially, for a 32:1 disk/data ratio. By way of contrast, real-life analytic DBMS with good compression often have disk/data ratios of well under 1:1.
Meanwhile, the RAM:data ratio is around 1:11 It’s clear that ParAccel’s early TPC-H benchmarks ran entirely in RAM; indeed, ParAccel even admits that. And so I conjecture that ParAccel’s latest TPC-H benchmark ran (almost) entirely in RAM as well. Once again, this would illustrate that the TPC-H is irrelevant to judging an analytic DBMS’ real world performance.
More generally — I would not advise anybody to consider ParAccel’s product, for any use, except after a proof-of-concept in which ParAccel was not given the time and opportunity to perform extensive off-site tuning. I tend to feel that way about all analytic DBMS, but it’s a particular concern in the case of ParAccel.
| Categories: Analytic technologies, Benchmarks and POCs, Buying processes, Columnar database management, Data warehousing, Database compression, ParAccel | 96 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: Read more
| Categories: Analytic technologies, Aster Data, Data warehousing, MapReduce, Parallelization, Theory and architecture | 3 Comments |
Dataupia’s troubles are now confirmed
Todd Fin pointed me yesterday to an article by Wade Roush that confirmed in detail layoffs and other troubles at Dataupia. The article quotes Dataupia marketing VP Samantha Stone as saying Dataupia is down to 23 employees, and that some of the layoffs were in engineering. This is consistent with what I’d been hearing for a while, namely that other analytic DBMS vendors were seeing a flood of Dataupia resumes, especially technical ones.
The article goes on to discuss difficulties Dataupia has had in raising another round of financing. During Dataupia’s very long CEO search — which I kept hearing about from people who’d been approached for the job — it was obvious money wouldn’t come in until a CEO was found. But it seems that even with a new CEO, existing investors are reluctant to re-up without a new investor as well, and that new investment is slow in happening.
On the plus side, the article quotes Samantha as saying founder Foster Hinshaw is recovering well from his heart surgery.
| Categories: Data warehouse appliances, Data warehousing, Dataupia, Emulation, transparency, portability | 3 Comments |
Netezza Q1 earning call transcript
I finally read the Netezza Q1 earnings call transcript, put out by Seeking Alpha. Highlights included:
- Netezza got 14 new-name accounts and 21 follow-on deals. Average sale in both groups was right around $1 million.
- The economy is tough, deals are slipping, and nobody knows for sure what will happen.
- Netezza’s main head-to-head competitors are Oracle and Teradata. Netezza claims good but not perfect win rates against each, but concedes that those vendors (especially Oracle) of course get other deals Netezza never sees.
- Netezza characterizes Teradata as offering its multiple product lines, trying to upsell many customers from cheaper to more expensive product lines, and being selectively aggressive about pricing. None of this is surprising to me.
- 80% of Netezza’s Q1 revenue, and perhaps even a higher fraction of new-name accounts, was in four vertical markets: “Digital media,” telecom, government, and financial services.
- Some time over the next few months, Netezza will give at least some more clarity about future products.
One tip for the Netezza folks, by the way, from this former stock analyst — you should never use the word “certainly” about a deal you haven’t closed yet. “Almost surely” could be OK, but “certainly” — well, it certainly was not the thing to say.
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 | 4 Comments |
Per-terabyte pricing
Software-only DBMS vendors sometimes price per terabyte of user data. Vertica’s list price is $100K/TB. Greenplum’s list price is $70K/TB. In practice, both offer substantial discounts, especially at higher volumes. In both cases, this means raw data, uncompressed, without counting indexes or temp space.
Client experience teaches me that this definition is easy to forget, so let me reemphasize the key point:
Per-terabyte pricing is based on a calculated figure. Per-terabyte pricing is not based on the current disk space used by your database when managed by the DBMS you are replacing.
There’s at least one important difference in how Vertica and Greenplum calculate database size. No matter how many times you copy the data, Vertica only charges you for it once.* But if you spin out data marts and recopy data into it — as Greenplum rightly encourages you to do — Greenplum wants to be paid for each copy. Similarly, Vertica charges only for deployment, and not for test or development; I didn’t remember to ask what Greenplum’s policies are in those regards. (Edit: Greenplum says in a comment below that it doesn’t charge for test or development data either.)
*That policy is a great fit with Vertica’s performance recommendation that you should store columns in different sort orders, perhaps an average of two copies per column.