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.

• Ben says Greenplum wasn’t being shopped, by which he means Greenplum was out raising more capital and the fund-raising was going well.  Note: Half or so of Greenplum’s deals were subscription-priced, so it had weaker cash flow than it would have if it were doing equally well selling perpetual licenses.
• However, joint engineering was also going well with, e.g., Greenplum CTO Luke Lonergan spending time at EMC facilities in Cork, Ireland. And one thing led to another …
• Greenplum has ~ 140 customers, vs. ~65 five quarters ago, 100+ at year-end, and an acquisition rate of 12-15/quarter last fall.
• A typical “small” paying customer for Greenplum starts with 10-20 TB of data.
• Greenplum Chorus isn’t generally available yet, with rollout energy being focused on Greenplum 4.0. Note: As important as it is for overall industry direction, Greenplum Chorus is a product which won’t be a terribly big deal in Release 1 anyway.

Highlights looking forward include: 

• When I challenged him, Ben sounded quite optimistic that Pat Gelsinger will immunize Greenplum against and generally counteract some of EMC’s traditionally stifling bureaucracy. (My words, of course, not his.)
• The initial Greenplum/EMC product vision appears truly centered around “private cloud,” specifically including Greenplum, VMware, and EMC storage arrays.
• Some other areas of potential Greenplum/EMC technical synergy I think are cool obviously haven’t been seriously addressed yet.
• Based on what I heard from Ben about the aura around the deal and also on what I know of the individual executives at Greenplum, I think each of them is a good bet to stick around EMC for a while. (That’s on average. Of course, it would be surprising if 100% of them stayed around very long.) Basically, there’s at least a chance EMC/Greenplum will do some pretty cool stuff, and most of the guys will probably stick around to see if that actually starts to happen.*

*Also, when they do eventually leave, they’ll surely say things to the effect “The cool stuff is well underway; my work here is done.” That party line is almost guaranteed, no matter how things unfold in reality.

• Facebook, which has said it has 150 or so Cassandra nodes (but see below)
• Twitter, which has said it has 45 or so Cassandra nodes
• Rackspace, which used to be Jonathan Ellis’ employer, and now is backing Cassandra company Riptano
• Digg, which along with Twitter and Rackspace was one of the three major users helping advance the Cassandra project
• OpenX, Simple Geo, Digital Reasoning, who Jonathan cited as production users in March
• Cloudkick, as noted and linked in my other post
• Two customers Riptano named at launch (but I’ve forgotten who they were*)

Fetlife, Meebo, and others seem to at least have a healthy interest in Cassandra, based on their level of involvement in a forthcoming Cassandra Summit. That said, the @Fetlife tweetstream features numerous yelps of pain, and I don’t mean the recreational kind. 

*And I can’t easily find a launch press release, whether on the rather minimalist Riptano website or elsewhere.

Beyond that, when Riptano launched in May, the Riptano guys (mainly Jonathan Ellis) said:

• They were sure there were dozens of Cassandra user organizations, maybe even >100. But there weren’t 100s.
• Maybe 20-40% of those Cassandra sites were in production. (But I don’t think I’d multiply that out to suggest there were, say, 35-50 production Cassandra users.)
• 4000 people were going daily to the Apache Cassandra site.
• There were 250 Cassandra downloads daily.
• Lots of startups were using Cassandra.
• Lots of other companies were looking at switching over to Cassandra.
• Many potential Cassandra users had been waiting for a Cassandra company to be available to support it.
• The median number of Cassandra (production?) nodes is probably 8-10. 4 would be a low end figure.

That’s a lot of adoption for a not-even-Release-1 open source project. Even so, there’s a feeling going around that Cassandra has lost some momentum the past couple of months. Most notably, Facebook, which created Cassandra in the first place, isn’t using it for new projects. True, I’m hearing even less evidence that any one of Membase, Voldemort, VoltDB, Akiban, Clustrix, or Riak – for example – is setting the world on fire than I am for Cassandra. But the viable Cassandra alternatives are piling up. Cassandra isn’t the only or even primary game in town, and for that matter I haven’t heard any concise description of a niche in which Cassandra is the unquestioned leader.

Edit: A/the Facebook project that continues to run on Cassandra is Inbox search.

As for Riptano itself:

• Riptano launched with two founders and immediately made an offer to a third guy. I don’t know how many folks they have now, two months later.
• Rackspace put some funding into Riptano.
• Riptano’s strategy sounds a lot like Cloudera’s, by which I mean:
  • Riptano’s business is all services, whether training, consulting, or support.
  • Riptano’s intended main business is obviously support.
  • Notwithstanding the above, Riptano intends to eventually offer proprietary software, bundled with its support services.
  • The first area of focus for that proprietary software is intended to be management tools.
  • I wouldn’t be surprised if, like Cloudera, Riptano tweaks its software focus from “stuff that lets us support you better” to “integration with stuff you pay for.” Those strategies are actually pretty similar.

Riptano seems to be starting out with support pricing around $1,000-$4,000/server/year, before quantity discounts.

• If you are or want to be a serious MapReduce user – and you’re past the “play around over the weekend” stage — you probably should have either:
  • A serious non-DBMS MapReduce distribution.
  • MapReduce integrated into your analytic DBMS.
  • Both.
• The obvious choice for non-DBMS MapReduce is Hadoop.
• The obvious choice for a Hadoop distribution is Cloudera Enterprise.
• Cloudera Enterprise has three main aspects, in an inseparable bundle:
  • Distributions for a double-digit number of open source projects. It’s nice having all that in one package – unless, of course, you like playing with Tinkertoys.
  • Proprietary Cloudera code.
  • Cloudera support.
• Cloudera says its proprietary code is and in the future is planned to be concentrated – at least in large part — on integrating open source technology with closed source products. This has the virtue of being targeted directly at that segment of the market which has proven it’s actually willing to pay money for software.
• Cloudera Enterprise areas of focus, now and in the presumed future, include:
  • Core Hadoop engine, which Cloudera says is quite predictably and appropriately evolving more slowly than the tools around it.
  • Development, management and administrative tools, including:
    • Pig and Hive. Cloudera says >70% of Facebook Hadoop jobs are initiated through Hive, and the same is true of Yahoo and Pig.
    • Connectivity to commercial tools.
    • The product formerly known as “Cloudera Desktop.”
  • Workflow, which in this context refers to letting you create a Hadoop application as a sequence of small steps, rather than forcing you to kluge it into being one unwieldy thing. At the moment, this is much less widely adopted than Pig and Hive, but Cloudera has high hopes for it, because of its obvious benefits in modularity and manageability.
  • Quasi-DBMS technology. Besides Hive and Pig, this includes HBase. Cloudera says there has been considerable demand for HBase, and it is pleased that project is now mature enough to ship. Cloudera stresses that it intends HBase not for OLTP, but as an adjunct to analytic processing. E.g., Cloudera suggests HBase would be a fine vehicle for replicating dimension tables across each node of a cluster.
  • Data connectivity, e.g. to MySQL or to sensor log files.
• Cloudera Enterprise pricing is well below DBMS prices – not by a full order of magnitude, if I’m right about everybody’s quantity discount policies, but even so by a lot. Details are NDA.

Cloudera sometimes sends confusing signals about its beliefs and strategies. For example, one can get different stories depending on whether one talks to:

• Somebody at Cloudera who comes primarily from the user and open source communities.
• Somebody at Cloudera who has actually worked at a software company before.

But I predict that Cloudera will now stick for a while with more or less the strategy outlined above.

Naturally, we also talked about Hadoop adoption. Highlights of that part – no doubt somewhat biased towards Cloudera’s own customer base — included:

• Notwithstanding eBay’s prior skepticism about MapReduce, it is quoted saying nice things in a Cloudera press release, and has apparently become quite a large Hadoop user, starting out with a search-quality use case.
• Typical Hadoop deployment sizes are 10 nodes or so when experimenting, 80-500+ in production.
• 10 terabytes/node – I’m pretty sure Cloudera meant of user data — is not inconceivable, so a cost-conscious 500-node user could have 5 petabytes of data managed by Hadoop.
• Cloudera has half a dozen customers at the 75+ node production level.
• Web and financial services are the two vertical markets moving most aggressively into Hadoop production. The government is also in significant Hadoop production, but the details of that are classified.
• Web uses for Hadoop include:
  • Clickstream – sessionization, etc. – that’s a super-mainstream use.
  • Search – analyzing search attempts in conjunction with structured data.
  • Machine learning (for ad serving, etc.).
• Financial services uses for Hadoop include:
  • Internal trading rule enforcement/fraud detection.
  • Complex ETL.
  • Portfolio risk assessment (typically overnight).

None of this is inconsistent with previous surveys of Hadoop use cases.

Various users talked at the Hadoop Summit this week. I wasn’t there, and won’t write about their stories for now. That said, Twitter’s slide deck from same has some interesting stuff, including:

• 7 TB/day ETLed from MySQL.
• Petabytes-being-stored accordingly coming soon.
• Open sourcing their ETL tool Crane.
• 3-4X LZO compression at little CPU cost.
• HBase is a more usable for them than HDFS, which isn’t mutable enough.
• Pig = 5% of code and coding effort vs. vanilla Hadoop at 30% or less performance hit.

Bruce also told me that Kickfire is now up to 10 paying customers, and that there are repeat deals.

• Slide 2 has some market success figures and so on. (>3100 copies at >1800 users, >200 sales last year)
• Slides 6-11 give more detail on Sybase’s indexing and data access methods than I put into my recent technical basics of Sybase IQ post.
• Slide 16 reminds us that in-database data mining is quite competitive with what SAS has actually delivered with its DBMS partners, even if it doesn’t have the nice architectural approach of Aster or Netezza. (I.e., Sybase IQ’s more-than-SQL advanced analytics story relies on C++ UDFs  — User Defined Functions — running in-process with the DBMS.) In particular, there’s a data mining/predictive analytics library — modeling and scoring both — licensed from a small third party.
• A number of the other later slides also have quite a bit of technical crunch. (More on some of those points below too.)

Sybase IQ may have a bit of a funky architecture (e.g., no MPP), but the age of the product and the substantial revenue it generates have allowed Sybase to put in a bunch of product features that newer vendors haven’t gotten around to yet.

More recently, Sybase volunteered permission for me to preannounce Sybase IQ Version 15.2 by a few days (it’s scheduled to come out this week). Sybase IQ seems to be focused on large part on the government/intelligent market, with three major features being:

• A kind of data federation, querying external databases, that makes sense mainly in the context of rigorous security rules. (I find that confusing, since Sybase IQ’s indexes tend to hold all the information in the database, but I didn’t push the point.)
• An upgrade to Sybase IQ’s built-in text indexing. I doubt anybody would confuse this with best-of-breed text search, but evidently that intelligence community is satisfied with less. But even before 15.2, Sybase IQ could do both LIKE and WHERE CONTAINS searching.
• Improved LOB (Large OBject) management.

One part of my Sybase IQ conversations I haven’t blogged yet in much details is scale-out, concurrency, and “multiplexing.”

• Sybase feels that Sybase IQ’s competitive sweet spot, especially in terms of performance, is reached when there are 20 or more concurrent queries.
• In general, Sybase asserts that a shared-everything architecture is great for concurrency — just run different queries on different boxes, all against the same data.
• The ability to use a bunch of boxes run Sybase IQ is called “multiplexing.”  This is a chargeable option, without which one is limited to a single SMP box.
• Just under 20% of the top 250 Sybase IQ customers have multi-node scale-out configuration (vs. single-node SMP scale-up). And around 8% have it overall.
• Sybase IQ nodes can be heterogeneous (e.g., in compute power).
• Sybase IQ nodes can be dedicated to be read-only, or can be read-write. Indeed, Sybase IQ nodes can change roles dynamically, for example becoming write-only during nightly batch load. (I didn’t clarify whether all this applies just to nodes-as-boxes, or if some parts apply to specific processors or cores within the same box.)
• Sybase noted that data mart outsourcers can offer differentiated SLAs (Service Level Agreements) depending upon which nodes they give which customers access to.
• Most Sybase IQ installations start at 8 cores or more. The Sybase IQ Small Business Edition, limited to 4 cores, is not a big seller.
• Sybase IQ has a straightforward round-robin load-balancing story via third-party technology.

Finally, along the way in the discussions I picked up various tidbits about the Sybase IQ user base. Unfortunately, Sybase is pretty vague in discussing database sizes — are they user data? Are they compressed? What do the numbers mean? With that huge caveat:

• By some metric or other, a couple of classified customers are approaching petabyte scale.
• The largest commercial Sybase IQ customer — a credit card company — has a couple hundred terabytes or so.
• The largest financial services Sybase IQ databases are 50-70 terabytes. This sounds low, frankly, so maybe those are compressed figures, with user data being 200+ terabytes. But I’m just speculating there.
• Sybase IQ has a little less than 100 customers in the “data aggregator” market, which is a lot like what I call “data mart outsourcer.”
• Sybase IQ’s ILM technology is a chargeable option, with Sybase being “cautious” about sales. Compliance is a big market driver for it.
• Sybase IQ’s #1 vertical market is financial services. Other biggies are government, telecom, marketing services, and to some extent retail.
• As of February, there were 40-45 production users of Sybase IQ 15.0 and 15.1.

• Vertica plans to build a massive, world-conquering sales force.
• If Vertica dips back into negative cash flow to do that and has to raise more venture capital, so be it.
• “Triple-digit” revenue growth is expected for this year.

*I’ve since heard more both from Ralph and his former colleagues, and I’m comfortable taking the move more or less at face value — for some reasons he doesn’t want to spell out, Ralph really wanted to move back home to South Africa.

While they were at it, Vertica also put out a press release reporting very good success in the social gaming market. The biggest and best known of the bunch is Zynga. Three months ago, Wayne Eckerson had figures of 3 TB/day added to the database, 200 nodes, and >40 million users. Now Zynga is using a figure of >65 million daily users and 230 nodes. More precisely, at Zynga:

• There are two Vertica databases with identical data.
• Each Zynga Vertica database runs on 115 nodes.
• Zynga’s two Vertica database clusters are used for different applications.
• It’s undisclosed exactly what Zynga runs on what Vertica cluster. But best practice would be to put mission-critical, fast-response stuff on one cluster, and use the other for longer-running or less-critical queries — plus have it be available as hot standby — given that I don’t see much reason to put data geographically close to users around the world for reasons of latency or whatever.
• An undisclosed amount of data, amounting to all of what Wayne earlier estimated at 3 TB, is added to each of Zynga’s Vertica databases daily.

In other news, Vertica now states its customer count as being >130.

Greenplum 4.0 highlights and related observations include:

• For the most part, Greenplum 4.0 is focused on general robustness catch-up and Bottleneck Whack-A-Mole, much like the latest releases from fellow analytic DBMS vendors Vertica and Aster Data.
• Greenplum has switched its replication approach from logical (execute transactions against two different disks) to block-level (just ship over the blocks that were changed by the original transaction). This seems to increase a Greenplum database’s robustness/performance/uptime in the face of disk/node failure. It also provides Greenplum with an ongoing performance advantage in that data only has to be compressed once in total for both disk writes.
• The Greenplum DBMS now has something called “tablespaces,” which sounds as if it extends Greenplum’s “polymorphic storage” to accommodate different kinds of storage device. Everybody has to do and for the most part is doing this, e.g. Teradata and Sybase. At least for now, you need to have the same mix of storage technology at every Greenplum node. That said, while Greenplum’s customers will surely want solid-state storage in the future, that’s not quite yet a major current issue.
• The timetable on Greenplum 4.0 is a salami-thin-slicer’s delight:
  • Greenplum 4.0 has been used in POCs (Proofs of Concept) for a while.
  • Greenplum 4.0 has been in early access for a few weeks.
  • Greenplum 4.0 controlled availability is planned for the end of April.
  • Greenplum 4.0 general availability is planned around the end of May or early June.
  • (Note: Everything in Greenplum 4.0 has been built, and is undergoing QA).
• Greenplum has put together a nice list of big-name customers, including Fox/MySpace, eBay, Sears, and T-Mobile. While Fox/MySpace never got to the predicted 1-petabyte level of user data, T-Mobile is loosely projected to indeed get there. The same 1-petabyte projection is made more confidently about another Greenplum telecom customer (unnamed), which seems to be in the process of acquiring a 300-node Greenplum system.

The really interesting part of this announcement, however, is Greenplum Chorus. Greenplum agrees with my assertion that Greenplum Chorus is a new kind of data integration/ETL technology. In particular, Greenplum Chorus is designed around a stance I agree with, namely it’s unrealistic to put everything into a single enterprise data warehouse (EDW); you need to manage data marts as well, preferably in a coordinated way. Mainstream data integration/ETL (Extract/Integration/Load) vendors such as Informatica or Ab Initio would surely say “That’s often quite true, and our technology can handle such scenarios just as it handles single-EDW-data-sink environments.” But Greenplum Chorus offers three capabilities not generally found in traditional data integration products (and offers only those three capabilities), namely:

• Spin out data marts, whether by recopying the data or by creating a virtual data mart inside another data warehouse/mart.
• Find/discover data in databases across your enterprise.
• Do social networking around databases/data marts.

Greenplum Chorus is heading into early access soon, with general availability slated around midyear. Also in the mix is a Greenplum “Hypervisor” that can somehow relate to an almost unlimited number of nodes or databases; however, I didn’t get a lot of details on the Greenplum Hypervisor technology or on the target dates for delivering and integrating the Hypervisor with other parts of Greenplum’s technology.

When Greenplum first talked about about the enterprise data cloud (EDC) idea, it emphasized the spinning out of physical data marts in an easy way, as opposed to the virtual data marts pushed by Oliver Ratzesberger and Teradata. Greenplum Chorus, however, supports both kinds (as, at least directionally, does Teradata), specifically letting you choose between:

• “Independent sandboxes” – physical copies of the data, in a separate Greenplum database instance.
• “Satellite sandboxes” – virtual data marts, of course managed by the same Greenplum database instance.

Actually, if you want to recopy data in the same Greenplum database instance, you can do that too, via something called “data sets,” but that’s not the main focus. Either option, I presume, can be configured to provide either or both of the two main benefits of spun-out data marts, namely:

• Control over the performance and SLAs (Service-Level Agreements) of your analytic workload
• Ability to mix in new raw data and/or new aggregations

in either case without messing up the performance, SLAs, security, or “one truth-ness” of the existing database.

To provide those capabilities in an analytic DBMS, you need sufficiently robust parallel data movement (for the physical sandboxes) and workload management (for the virtual ones). Greenplum obviously believes it has both. Teradata makes the same claim. Other vendors would make similar assertions, and presumably will offer similar capabilities soon. You also want some kind of ability to ingest data from foreign databases, but that can be pretty routine stuff; e.g., in Release 1 of Chorus, Greenplum is content to offer ODBC access to Oracle, SQL Server, et al.

The “data discovery” and “social networking” aspects of Greenplum Chorus seem to be quite Release 1 as well. Basically, Greenplum lets people post discussion threads about databases and data marts, discussing what value can be derived from them. I guess somebody could include links to web-technology reports based on those databases, but otherwise there’s no integration with business intelligence tools and their collaboration capabilities. Even so, Greenplum reports that business executives liked this capability in early access testing.

Greenplum Chorus is ETL without a lot of T, and without a lot of performance optimizations either. That may not be much of a problem in its paradigmatic use case, spinning out a data mart quickly for some analysis to see if valuable conclusions can be drawn. Presumably, in the most successful cases, business and technical processes would emerge after the fact to pipe up-to-date versions of that analysis into operational systems, mooting any ETL deficiencies in the initial exploration moot. In a world where “data exploration” is becoming an increasingly important concept, something like Greenplum Chorus may suffice to provide significant customer value. But whether Greenplum Chorus’s capabilities are eventually co-opted by more fully-featured data integration suites remains an open question for the future.

Nonetheless, OLTP/general-purpose data management startup activity has recently picked up, targeting what I see as some very real opportunities and needs. So as a jumping-off point for further writing, I thought it might be interesting to collect a few observations about the market in one place.  These include:

• Big-brand OLTP/general-purpose DBMS have more “stickiness” than analytic DBMS.
• By number, most of an enterprise’s OLTP/general-purpose databases are low-volume and low-value.
• Most interesting new OLTP/general-purpose data management products are either MySQL-based or NoSQL.
• It’s not yet clear whether MySQL will prevail over MySQL forks, or vice-versa, or whether they will co-exist.
• The era of silicon-centric relational DBMS is coming.
• The emphasis on scale-out and reducing the cost of joins spans the NoSQL and SQL-based worlds.
• Users’ instance on “free” could be a major problem for OLTP DBMS innovation.

I shall explain.

Big-brand OLTP/general-purpose DBMS have more “stickiness” than analytic DBMS.

• OLTP applications are more complex than analytic ones, and hence more tightly wired into particular brands of DBMS. For example, third-party packaged OLTP applications are typically portable among only a few brands of DBMS. But third-party business intelligence tools, and the BI “applications” built in them, are more easily and widely portable.
• Specific technical observations such as “OLTP apps tend to use stored procedures, which are DBMS-specific” or “OLTP apps tend to have lots and lots of tables” serve to underscore the first point.
• An enterprise’s highest-value data is commonly the financial stuff handled by its core OLTP systems, so those are the last things they want to mess around with just to get some cost savings. Security, high availability, and so on are major considerations that can outweigh cost.

By number, most of an enterprise’s OLTP/general-purpose databases are low-volume and low-value. Indeed, “OLTP” is often a misnomer, which is why I tend to go with “general-purpose” or some similarly wishy-washy phrase instead.

• In theory, this is a ripe area for what I’ve called mid-range DBMS.
• The big brand vendors try hard to keep as many of those databases for themselves as they can. Enterprise-wide license pricing helps. Going forward, so will virtualization/consolidation strategies, such as Oracle’s Exadata-centric approach.
• A variety of mid-range DBMS alternatives beyond the big brands have technical merit, at least in some cases and configurations – MySQL, PostgreSQL, Intersystems Cache’, and so on.
• The only such mid-range DBMS alternative with much large enterprise business momentum, however, appears to be MySQL.

“General-purpose” might be a better term than “OLTP” anyway.

• I don’t have a link, but it’s widely agreed that over half of the processing on an “OLTP” enterprise app is commonly reporting and so on.
• “Operational BI” is progressing by fits and starts, but it is progressing.
• Anything customer-facing — web-based, call center, or otherwise — is likely to include a heavy dose of “real-time” analytic optimization.

Most interesting new OLTP/general-purpose data management products are either MySQL-based or NoSQL.

• VoltDB is the main exception that jumps to mind.
• This isn’t true in the analytic DBMS area, where Netezza, Greenplum, Aster, Vertica and others started from PostgreSQL’s code, APIs, or both.

It’s not yet clear whether MySQL will prevail over MySQL forks, or vice-versa, or whether they will co-exist.

• MySQL is a limited product without all the third-party storage engines that are being developed.
• Oracle’s promise of MySQL good behavior has an expiration date.
• None of the MySQL front-end alternatives are remotely mature yet.

The era of silicon-centric relational DBMS is coming.

• I think “silicon” means “solid-state memory” as much as or more than it means “RAM,” but that’s not yet certain.
• What is pretty certain is that, thanks to Moore’s Law, some kind of silicon will increasingly replace disk.
• Oracle’s increasingly Flash-centric story is a challenge to everybody.
• RAM-centric VoltDB will launch fairly soon. (By the way, while VoltDB still has a lot in common with H-Store, they’re not exactly the same thing. And H-Store research is progressing too.)
• RethinkDB is being developed, focused directly on solid-state memory. Based on the sparse information available online, RethinkDB sounds somewhat like a dumbed-down H-Store.
• New disk-based vendors may never optimize their use of disk, instead targeting a solid-state future. (E.g., I think Akiban should and quite well might follow this path.)

The emphasis on scale-out and reducing the cost of joins spans the NoSQL and SQL-based worlds. We hear that from the NoSQL guys all the time. But I also just heard it from Akiban.

Users’ instance on “free” could be a major problem for OLTP DBMS innovation. Vendors of new OLTP data management technologies often feel obligated to open source their products, notwithstanding the historical lack of revenue in the open source OLTP DBMS market. As just one of many examples, Nova Spivack wrote:

I have recently seen some new graph data storage products that may provide the levels of scale and performance needed, but pricing has not been determined yet. In short, storage and retrieval of semantic graph datasets is a big unsolved challenge that is holding back the entire industry. We need federated database systems that can handle hundreds of billions to trillions of triples under high load conditions, in the cloud, on commodity hardware and open source software. Only then will it be affordable to make semantic applications and services at Web-scale.

I hear similar things from other startups, who evidently believe they need and/or are entitled to enjoy sophisticated, high-performance, zero-cost, specialized database management technology.

• Business is strong. Multiple vendors claim to be going gangbusters, with the happy sounds coming out of Vertica and Infobright being echoed by several competitors. Hearsay suggests some other companies in related businesses are doing well too. Depending on who you talk to, the business pickup dates back to Q4, give or take a quarter.
• Oracle Exadata has become a formidable competitor, on the strength of Exadata 2. Exadata 2’s positioning and perception among Oracle users seem to be pretty much in line with what Oracle portrayed to me.
• Teradata is portrayed as a weak competitor. Competitors don’t worry about Teradata nearly as much as they do about Oracle. That said, I suspect a bit of wishful thinking; Teradata is clearly still getting a lot of business the other vendors would dearly love to have.
• HP Neoview is reeling. (Almost) nobody sees Neoview competitively. The Walmart Neoview installation is said to have stayed small at best. JP Morgan Chase is said to have completely thrown Neoview out (and a bunch of HP engineers with it).
• (Almost) nobody mentions competing against DB2 either. This continues to baffle me.