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.

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.

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.

Two lessons from Dataupia’s troubles

I’ve been beating my head against the wall trying to convince startups of two well-established truisms:

• Experience consistently shows that the demand for transparency/emulation features isn’t as great as entrepreneurs hope.
• If a startup’s competitors sell directly to enterprises, an indirect sales strategy rarely succeeds.

Maybe one or the other will learn from Dataupia’s example.

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.

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.

The future of data marts

Greenplum is announcing today a long-term vision, under the name Enterprise Data Cloud (EDC). Key observations around the concept — mixing mine and Greenplum’s together — include:

• Data marts aren’t just for performance (or price/performance). They also exist to give individual analysts or small teams control of their analytic destiny.
• Thus, it would be really cool if business users could have their own analytic “sandboxes” — virtual or physical analytic databases that they can manipulate without breaking anything else.
• In any case, business users want to analyze data when they want to analyze it. It is often unwise to ask business users to postpone analysis until after an enterprise data model can be extended to fully incorporate the new data they want to look at.
• Whether or not you agree with that, it’s an empirical fact that enterprises have many legacy data marts (or even, especially due to M&A, multiple legacy data warehouses). Similarly, it’s an empirical fact that many business users have the clout to order up new data marts as well.
• Consolidating data marts onto one common technological platform has important benefits.

In essence, Greenplum is pitching the story:

• Thesis: Enterprise Data Warehouses (EDWs)
• Antithesis: Data Warehouse Appliances
• Synthesis: Greenplum’s Enterprise Data Cloud vision

When put that starkly, it’s overstated, not least because

Specialized Analytic DBMS != Data Warehouse Appliance

But basically it makes sense, for two main reasons:

• Analysis is performed on all sorts of novel data, from sources far beyond an enterprise’s core transactions. This data neither has to fit nor particularly benefits from being tightly fitted into the core enterprise data model. Requiring it to do so is just an unnecessary and painful bureaucratic delay.
• On the other hand, consolidation can be a good idea even when systems don’t particularly interoperate. Data marts, which commonly do in part interoperate with central data stores, have all the more reason to be consolidated onto a central technology platform/stack.

Read more

Daniel Abadi on Kickfire and related subjects

Daniel Abadi has a new blog, whose first post centers around Kickfire.  The money quote is (emphasis mine):

In order for me to get excited about Kickfire, I have to ignore Mike Stonebraker’s voice in my head telling me that DBMS hardware companies have been launched many times in the past are ALWAYS fail (the main reasoning is that Moore’s law allows for commodity hardware to catch up in performance, eventually making the proprietary hardware overpriced and irrelevant). But given that Moore’s law is transforming into increased parallelism rather than increased raw speed, maybe hardware DBMS companies can succeed now where they have failed in the past

Good point.

More generally, Abadi speculates about the market for MySQL-compatible data warehousing.  My responses include:

• OF COURSE there are many MySQL users who need to move to a serious analytic DBMS.
• What’s less clear is whether there’s any big advantage to those users in remaining MySQL-compatible when they do move.  I’m not sure what MySQL-specific syntax or optimizations they’d have that would be difficult to port to a non-MySQL system.
• It’s nice to see Abadi speaking well of Infobright and its technology.
• To say that Infobright went open source because it was “desperate” is overstated. That said, I don’t think Infobright was on track to prosper without going open source.
• While open source and MySQL go together, an appliance like Kickfire loses many (not all) of the benefits of open source.
• Calpont has indeed never disclosed a customer win.  Any year now … (Just kidding, Vogel!)
• In general, seeing Abadi be so favorable toward Vertica competitors adds credibiity to the recent Hadoop vs. DBMS paper.

Anyhow, as previously noted, I’m a big Daniel Abadi fan. I look forward to seeing what else he posts in his blog, and am optimistic he’ll live up to or exceed its stated goals.

Oracle’s hardware strategy

Larry Ellison stated clearly in an email interview with Reuters (links here and here) that Oracle intends to keep Sun’s hardware business and indeed intends to invest in the SPARC chip. Naturally, I have a few thoughts about this.

As Stephen O’Grady points out, Sun’s main strength lay in selling to the large enterprise market. Well, that’s Oracle’s overwhelming focus too. As I noted two years ago:

One Oracle response is to provide lots of add-on technologies for high-end customers, on the database and middle tiers alike. In app servers it’s done surprisingly well against BEA. It’s sold a lot of clustering. And it’s bought into and tried to popularize niche technologies like TimesTen and Tangosol’s.

This all makes perfect sense – it’s a great fit for Oracle’s best customers, and a way to get thousands of extra dollars per server from enterprises that may already have bought all-you-can-eat licenses to the Oracle DBMS. And being so sensible, it fits into the Clayton Christensen disruption story in two ways:

1. Oracle may be helpless against mid-tier competition, but it sure has the high-end core of its market locked up.

2. As one type of technology is commoditized, value is created in other parts of the technology stack.

Oracle’s ongoing acquisition spree in system software, application software, and now hardware just supports that story. MySQL, embedded Java, and so on may be welcome to Oracle as yet more opportunities to tap additional markets — but Oracle’s emphasis is and surely will remain on the large enterprise market.

The next notable point may be found in Larry’s key quote: Read more

eBay’s two enormous data warehouses

A few weeks ago, I had the chance to visit eBay, meet briefly with Oliver Ratzesberger and his team, and then catch up later with Oliver for dinner. I’ve already alluded to those discussions in a couple of posts, specifically on MapReduce (which eBay doesn’t like) and the astonishingly great difference between high- and low-end disk drives (to which eBay clued me in). Now I’m finally getting around to writing about the core of what we discussed, which is two of the very largest data warehouses in the world.

Metrics on eBay’s main Teradata data warehouse include:

• >2 petabytes of user data
• 10s of 1000s of users
• Millions of queries per day
• 72 nodes
• >140 GB/sec of I/O, or 2 GB/node/sec, or maybe that’s a peak when the workload is scan-heavy
• 100s of production databases being fed in

Metrics on eBay’s Greenplum data warehouse (or, if you like, data mart) include:

• 6 1/2 petabytes of user data
• 17 trillion records
• 150 billion new records/day, which seems to suggest an ingest rate well over 50 terabytes/day
• 96 nodes
• 200 MB/node/sec of I/O (that’s the order of magnitude difference that triggered my post on disk drives)
• 4.5 petabytes of storage
• 70% compression
• A small number of concurrent users

Read more

Data warehouse storage options — cheap, expensive, or solid-state disk drives

This is a long post, so I’m going to recap the highlights up front. In the opinion of somebody I have high regard for, namely Carson Schmidt of Teradata:

• There’s currently a huge — one order of magnitude — performance difference between cheap and expensive disks for data warehousing workloads.
• New disk generations coming soon will have best-of-both-worlds aspects, combining high-end performance with lower-end cost and power consumption.
• Solid-state drives will likely add one or two orders of magnitude to performance a few years down the road. Echoing the most famous logjam in VC history — namely the 60+ hard disk companies that got venture funding in the 1980s — 20+ companies are vying to cash in.

In other news, Carson likes 10 Gigabit Ethernet, dislikes Infiniband, and is “ecstatic” about Intel’s Nehalem, which will be the basis for Teradata’s next generation of servers.

Read more

Kickfire update

I talked recently with my clients at Kickfire, especially newish CEO Bruce Armstrong. I also visited the Kickfire blog, which among other virtues features a fairly clear overview of Kickfire technology. (I did my own Kickfire overview in October.) Highlights of the current Kickfire story include:

• Kickfire is initially focused on three heavily overlapping markets — network event analysis, the general Web 2.0/clickstream/online marketing analytics area, and MySQL/LAMP data warehousing.
• Kickfire has blogged about a few sales to unnamed customers in those markets.
• I think network management is a market that’s potentially friendly to five-figure-cost appliances. After all, networking equipment is generally sold in appliance form. Kickfire doesn’t dispute this analysis.
• Kickfire’s sales so far are to run databases in the sub-terabyte range, although both Kickfire and its customers intend to run bigger databases soon. (Kickfire describes the range as 300 GB - 1 TB.) Not coincidentally, Kickfire believes that MySQL doesn’t scale very well past 100 GB without a lot of partitioning effort (in the case of data warehouses) or sharding (in the case of OLTP).
• When Bruce became CEO, he let go some sales, marketing, and/or business development folks. He likes to call this a restructuring of Kickfire rather than a reduction-in-force, but anyhow — that’s what happened. There are now about 50 employees, and Kickfire still has most of the $20 million it raised last August in the bank.  Edit: The company clarifies that it actually wound up with more sales and marketing people than before.
• Kickfire has thankfully deemphasized various marketing themes I found annoying, such as ascribing great weight to TPC-H benchmarks or explaining why John von Neumann originally made bad choices in his principles of computer design.

Oracle introduces a half-rack version of Exadata

Oracle has introduced what amounts to a half-rack Exadata machine. My thoughts on this basically boil down to “makes sense” and “no big deal.” Specifically:

• The new Baby Exadata still holds 10 terabytes or more.
• Most specialty analytic DBMS purchases are still for databases of 10 terabytes or smaller.
• Large enterprise data warehouse projects are often being deferred or cut back due to the economic crunch, but smaller projects with credible, quick ROIs are doing fine.
• Exadata is evidently being sold overwhelmingly to Oracle loyalists. Other analytic DBMS vendors aren’t telling me of serious Exadata competition yet. If the market for Exadata is primarily “happy Oracle data warehouse users”, that’s mainly folks who have <5-10 terabytes of user data today.
• Oracle Exadata beta tests were done on a kind of half-rack configuration anyway.

DATAllegro sales price: $275 million

According to a press release announcing a venture capitalist’s job change,

Microsoft purchased DATAllegro for $275 million

Technically, that needn’t shut down the rumor mill altogether, since given the way deals are structured and reported, it’s unlikely that Microsoft actually cut checks to DATAllegro stockholders in the aggregate amount of $275 million promptly after the close of the acquisition.

Still, it’s a data point of some weight.

Hat tip to Mark Myers.

Closing the book on the DATAllegro customer base

I’m prepared to call an end to the “Guess DATAllegro’s customers” game.  Bottom line is that there are three in all, two of which are TEOCO and Dell, and the third of which is a semi-open secret.  I wrote last week:

The number of DATAllegro production references is expected to double imminently, from one to two. Few will be surprised at the identity of the second reference. I imagine the number will then stay at two, as DATAllegro technology is no longer being sold, and the third known production user has never been reputed to be particularly pleased with it.

Dell did indeed disclose at TDWI that it was a large DATAllegro user, notwithstanding that Dell is a huge Teradata user as well.  No doubt, Dell is gearing up to be a big user of Madison too.

Also at TDWI, I talked with some former DATAllegro employees who now work for rival vendors. None thinks DATAllegro has more than three customers.  Neither do I.

HP and Neoview update

I had lunch with some HP folks at TDWI. Highlights (burgers and jokes aside) included:

• HP’s BI consulting (especially the former Knightsbridge) and analytic product groups (including Neoview) are now tightly integrated.
• HP is trying to develop and pitch “solutions” where it has particular “intellectual property.” This IP can come from ordinary product engineering or internal use, because HP Labs serves both sides of the business. Specific examples offered included:
  • Telecom. Apparently, HP made specialized data warehouse devices for CDRs (Call Detail Records) long ago, and claims this has been area of particular expertise ever since.
  • Supply chain – based on HP’s internal experiences.
  • Customer relationship – ditto
• The main synergy suggested between consulting and Neoview is that HP’s experts work on talking buyers into such a complex view of their requirements that only Neoview (supposedly) can fit the bill.
• HP insists there are indeed new Neoview sales.
• Neoview sales seem to be concentrated in what Aster might call “frontline” applications — i.e., low latency, OLTP-like uptime requirements, etc.
• HP says it did an actual 80 TB POC. I asked whether this was for an 80 TB app or something a lot bigger, but didn’t get a clear answer.

Given the emphasis on trying to exploit HP’s other expertise in the data warehousing business, I suggested it was a pity that HP spun off Agilent (HP’s instrumentation division, aka HP Classic). Nobody much disagreed.

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.

Winter Corporation on Exadata

The most ridiculous analyst study I can recall — at least since Aberdeen pulled back from the “You pay; we say” business — is Winter Corporation’s list of large data warehouses. (Failings include that it only lists warehouses run by software from certain vendors; it doesn’t even list most of the largest warehouses from those vendors; and its size metrics are in my opinion fried.) So it was with some trepidation that I approached what appears to be an Oracle-sponsored Winter Corporation white paper about Exadata.* Read more

Oracle Exadata article — up at last

I’d been promising Intelligent Enterprise editor Doug Henschen an article on Oracle Exadata for months. It’s finally up.  For a variety of reasons, it was a lot more work than one might at first guess.  One such reason is that it spawned four related blog posts over the past few days.

As I post this, there are two glitches in the article. One is that em dashes are appearing as quote marks — and as you know, I use a lot of em dashes. The other is that one sentence on in-database data mining seems unclear to me, and I’ve asked for a small edit to make it clearer what I’m talking about.  No doubt both will be cleared up soon. Edit:  Doug indeed fixed all that within minutes.

This is an edited article.  Other than columns, it may be my first such since the Upside Magazine cover story on AOL over a decade ago. But it was edited with a light and skillful touch. Please don’t hold me responsible for every minor subtlety of emphasis or grammatical nuance.  But otherwise I stand behind the opinions, for they are indeed mine.

Oracle says they do onsite Exadata POCs after all

When I first asked Oracle about Netezza’s claim that Oracle doesn’t do onsite Exadata POCs, they blew off the question. Then I showed Oracle an article draft saying they don’t do onsite Exadata proofs-of-concept. At that point, Oracle denied Netezza’s claim, and told me there indeed have been onsite Exadata POCs.  Oracle has not yet been able to provide me with any actual examples of same, but perhaps that will change soon.  In the mean time, I continue with the assumption that Oracle is, at best, reluctant to do Exadata POCs at customer sites.

I do understand multiple reasons for vendors to prefer POCs be done on their own sites, both innocent (cost) and nefarious (excessive degrees of control). Read more

Netezza’s marketing goes retro again

Netezza loves retro images in its marketing, such as classic rock lyrics, or psychedelic paint jobs on its SPUs.  (Given the age demographics at, say, a Teradata or Netezza user conference, this isn’t as nutty as it first sounds.) Netezza’s latest is a creative peoples-liberation/revolution riff, under the name Data Liberators.  The ambience of that site and especially its first download should seem instinctively familiar to anybody who recalls the Symbionese Liberation Army when it was active, or who has ever participated in a chant of “The People, United, Will Never Be Defeated!”

The substance of the first “pamphlet”, so far as I can make out, is that you should only trust vendors who do short, onsite POCs, and Oracle may not do those for Exadata. Read more

Kickfire reports a few customer wins

Kickfire has the kind of blog I emphatically advise my clients to publish even when they don’t have management bandwidth to do something “sexier.”  If nothing else, at least they record their customer wins when they can.

The current list of cited customers is two application appliance OEM vendors (unnamed, but with some detail), plus one Web 2.0 company (ditto). They’ve also posted about a Sun partnership.

The “baseball bat” test for analytic DBMS and data warehouse appliances

More and more, I’m hearing about reliability, resilience, and uptime as criteria for choosing among data warehouse appliances and analytic DBMS. Possible reasons include:

• More data warehouses are mission-critical now, with strong requirements for uptime.
• Maybe reliability is a bit of a luxury, but the products are otherwise good enough now that users can afford to be a bit pickier.
• Vendor marketing departments are blowing the whole subject out of proportion.

The truth probably lies in a combination of all these factors.

Making the most fuss on the subject is probably Aster Data, who like to talk at length both about mission-critical data warehouse applications and Aster’s approach to making them robust. But I’m also hearing from multiple vendors that proofs-of-concept now regularly include stress tests against failure, in what can be – and indeed has been – called the “baseball bat” test. Prospects are encouraged to go on a rampage, pulling out boards, disk drives, switches, power cables, and almost anything else their devious minds can come up with to cause computer carnage.

Read more

How to tell Teradata’s product lines apart

Once Netezza hit the market, Teradata had a classic “disruptive” price problem – it offered a high end product, at a high price, sporting lots of features that not all customers needed or were willing to pay for. Teradata has at times slashed prices in competitive situations, but there are obvious risks to that, especially when a customer already has a number of other Teradata systems for which it paid closer to full price.

This year, Teradata has introduced a range of products that flesh out its competitive lineup. There now are three mainstream Teradata offerings, plus two with more specialized applicability. Teradata no longer has to sell Cadillacs to customers on Corolla budgets.

But how do we tell the five Teradata product lines apart? The names are confusing, both in their hardware-vendor product numbers and their data-warehousing-dogma product names, especially since in real life Teradata products’ capabilities overlap. Indeed, Teradata executives freely admit that the Teradata Data Mart Appliance 551 can run smaller data warehouses, while the Teradata Data Warehouse Appliance 2550 is positioned in large part at what Teradata quite reasonably calls data marts.

When one looks past the difficulties of naming, Teradata’s product lineup begins to make more sense. Let’s start by considering the three main Teradata products.

Read more

Introduction to Kickfire

I’ve spent a few hours visiting or otherwise talking with my new clients at Kickfire recently, so I think I have a better feel for their story. A few details are still missing, however, either because I didn’t get around to asking about them, or because an unexplained accident corrupted my notes (and I wasn’t even using Office 2007). Highlights include:

Read more

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