DBMS product categories
Analysis of database management technology in specific product categories. Related subjects include:
Data warehouse appliances – fact and fiction
Borrowing the “Fact or fiction?” meme from the sports world:
- Data warehouse appliances have to have specialized hardware. Fiction. Indeed, most contenders except Teradata and Netezza — for example, DATAllegro, Vertica, ParAccel, Greenplum, and Infobright — offer Type 2 appliances. (Dataupia is another exception.)
- Specialized hardware is a dead-end for data warehouse appliances. Fiction. If it were easy for Teradata to replace its specialized switch technology, it would have done so a decade ago. And Netezza’s strategy has a lot of appeal.
- Data warehouse appliances are nothing new, and failed long ago. Fiction, but only because of Teradata. 1980s appliance pioneer Britton-Lee didn’t do so well (it was actually bought by Teradata). IBM and ICL (Britain’s national-champion hardware company) had content-addressable data store technology that went nowhere.
- Since data warehouse appliances failed long ago, they’ll fail now too. Fiction. Shared-nothing MPP is a fundamental advantage of appliances. So are various index-light strategies. Data warehouse appliances are here to stay.
- Data warehouse appliances only make sense if your main database management system can’t handle the job. Fiction. There are dozens of data warehouse appliances managing under 5 terabytes of user data, if not under 1 terabyte. True, some of them are legacy installations, dating back to when Oracle couldn’t handle that much data well itself. But new ones are still going in. Even if Oracle or Microsoft SQL Server can do the job, a data warehouse appliance is often a far superior — cheaper, easier to deploy and keep running, and/or better performing — alternative.
- Data warehouse appliances are just for data marts. For your full enterprise data warehouse, use a conventional DBMS. Part fact, part fiction. It depends on the appliance, and on the complexity of your needs. Teradata systems can do pretty much everything. Netezza and DATAllegro, two of the oldest data warehouse appliance startups, have worked hard on their concurrency issues and now can support fairly large user or reporting loads. They also can handle reasonable volumes of transactional or trickle-feed updates, and probably can support full EDW requirements for decent-sized organizations. Even so, there are some warehouse use cases for which they’re ill-suited. Newer appliance vendors are more limited yet.
- Analytic appliances are just renamed data warehouse appliances. Fact, even if misleading. Netezza is using the term “analytic appliance” to highlight additional things one can do on its boxes beyond answering queries. But those are still operations on a data mart or data warehouse.
- Teradata is the leading data warehouse appliance vendor. More fact than fiction. Some observers say that Teradata systems aren’t data warehouse appliances. But I think they are. Competitors may be superior to Teradata in one or the other characteristic trait of appliances – e.g., speed of installation – but it’s hard to define “appliances” in an objective way that excludes Teradata.
If you liked this post, you might also like one on text mining fact and fiction.
Amazon Dynamo — when primary key access is enough
Amazon has a very decentralized technical operation. But even the individual pieces have interestingly huge scale. Thus, various different things they’re doing are of interest.
They recently presented a research paper on a high-performance transactional system called Dynamo. (Hat tip to Dare Obasanjo.) A key point is the following:
There are many services on Amazon’s platform that only need primary-key access to a data store. For many services, such as those that provide best seller lists, shopping carts, customer preferences, session management, sales rank, and product catalog, the common pattern of using a relational database would lead to inefficiencies and limit scale and availability. Dynamo provides a simple primary-key only interface to meet the requirements of these applications.
Now, I don’t think too many organizations past Amazon are going to decide that they can’t afford the overhead of an RDBMS for such OLTP-like applications. But I do think it will become increasingly common to find other reasons to eschew traditional OLTP relational architectures. Maybe you’ll want the schema flexibility of XML. Or perhaps you’ll be happy with a fixed relational schema, but will want to optimize for analytic performance.
| Categories: Amazon and its cloud, Cloud computing, Data models and architecture, Database diversity, NoSQL, OLTP, Theory and architecture | 1 Comment |
Netezza has another big October quarter
Netezza reported a big October quarter, ahead of expectations. And official guidance for next quarter is essentially flat quarter-over-quarter, suggesting Q3 was indeed surprisingly big. However, Netezza’s year-over-year growth for Q3 was a little under 50%, suggesting the quarter wasn’t so remarkable after all. (Netezza has a January fiscal year.)
Tentative conclusion: Netezza just tends to have big October quarters, perhaps by timing sales cycles to finish soon after the late September user conference. If Netezza’s user conference ever moves to later in the fall, expect Q3 to be weak that year.
Netezza reported 18 new customers, double last year’s figure. Read more
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, Greenplum, Kognitio, Netezza | 3 Comments |
The key problem with dashboard functionality
I keep hinting – or saying outright 🙂 — that I think dashboards need to be revolutionized. It’s probably time to spell that point out a little further.
The key issue, in my opinion, it that dashboards need to be much more personalizable than they are now. This isn’t just me talking. I’ve raised the subject with a lot of users recently, and am getting close to 100% agreement with my viewpoint.
One part of the problem is personalizing what to see, how to visualize it, and how all that’s arranged on the screen. No one product yet fully combines best-of-breed ideas from mainstream BI, specialized visualization tools, and flexible personalized web portals. But that’s not my biggest concern, as I think the BI industry is on a pretty good path in those respects.
Rather, the real issue is that dashboards don’t adequately reflect personal opinions as to what is important. Indeed, that lack is often portrayed as virtue, because supposedly top management can dictate through a few simple metrics what a whole company of subordinates will think and think about. (Balanced scorecard theology is a particularly silly form of this.) But actually that lack is a serious impediment to dashboard success, or indeed to a general analytic/numerate enterprise culture overall.
“One version of the truth” can be a gross oversimplification. Read more
| Categories: Analytic technologies, Business intelligence, OLTP | 6 Comments |
Vertica update – HP appliance deal, customer information, and more
Vertica quietly announced an appliance bundling deal with HP and Red Hat today. That got me quickly onto the phone with Vertica’s Andy Ellicott, to discuss a few different subjects. Most interesting was the part about Vertica’s customer base, highlights of which included:
- Vertica’s claim to have “50” customers includes a bunch of unpaid licenses, many of them in academia.
- Vertica has about 15 paying customers.
- Based on conversations with mutual prospects, Vertica believes that’s more customers than DATAllegro has. (Of course, each DATAllegro sale is bigger than one of Vertica’s. Even so, I hope Vertica is wrong in its estimate, since DATAllegro told me its customer count was “double digit” quite a while ago.)
- Most Vertica customers manage over 1 terabyte of user data. A couple have bought licenses showing they intend to manage 20 terabytes or so.
- Vertica’s biggest customer/application category – existing customers and sales pipelines alike – is call detail records for telecommunications companies. (Other data warehouse specialists also have activity in the CDR area.). Major applications are billing assurance (getting the inter-carrier charges right) and marketing analysis. Call center uses are still in the future.
- Vertica’s other big market to date is investment research/tick history. Surely not coincidentally, this is a big area of focus for Mike Stonebraker, evidently at both companies for which he’s CTO. (The other, of course, is StreamBase.)
- Runners-up in market activity are clickstream analysis and general consumer analytics. These seem to be present in Vertica’s pipeline more than in the actual customer base.
| Categories: Analytic technologies, Business Objects, Data warehouse appliances, Data warehousing, DATAllegro, HP and Neoview, RDF and graphs, Vertica Systems | 5 Comments |
Netezza cites three warehouses over 50 terabytes
Netezza is finally making it clear that they run some largish warehouses. Their latest press release cites Catalina Marketing, Epsilon, and NYSE Euronext as having 50+ terabytes each. I checked with Netezza’s Marketing VP Ellen Rubin, and she confirmed that those are clean figures — user data, single warehouses, etc. Ellen further tells me that Netezza’s total count of warehouses that big is “significantly more” than the 3 named in the release.
Of course, this makes sense, given that Netezza’s largest box, the NPS 10800, runs 100 terabytes. And Catalina was named as having bought a 10800 in a press release back in December, 2006. Read more
ParAccel opens the kimono slightly
Please do not rely on the parts of this post that draw a distinction between in-memory and disk-based operation. See our February 18, 2008 post about ParAccel instead. It turns out that communication with ParAccel was yet worse than I had realized.
Officially launched today at the TDWI conference, ParAccel is out to compete with Netezza. Right out of the chute, ParAccel may have surpassed Netezza in at least one area: pointlessly annoying secrecy. (In other regards I love them dearly, but that paranoia can be a real pain.) As best I can remember, here are some things about ParAccel that I both am allowed to say and find interesting:
- ParAccel offers a columnar, MPP data warehouse DBMS, called the ParAccel Analytic Database.
- ParAccel’s product runs in two main modes. “Maverick” is normal, stand-alone mode. “Amigo” mode amounts to a plug-compatible accelerator for Oracle or Microsoft SQL*Server. Early sales and marketing were concentrated on SQL*Server Amigo mode.
- ParAccel’s product also runs in another pair of modes – in-memory and disk-based. Early sales and marketing were concentrated on in-memory mode. Hybrid memory-centric processing sounds like something for a future release.
- Sun has a reseller partnership with ParAccel, focused on in-memory mode.
- Sun and ParAccel published record-shattering 100 gigabyte, 300 gigabyte, and 1 terabyte TPC-H benchmarks today, based on in-memory mode. (If you’d like to throw 13 terabytes of disk at 1 terabyte of user data, running simple and repetitive queries, that benchmark might be a useful guide to your own experience. But hey – that’s a big improvement on the prior champion, who used 40 terabytes of disk. To ParAccel’s credit, they’re not pretending that this is a bigger deal than it is.)
DATAllegro discloses a few numbers
Privately held DATAllegro just announced a few tidbits about financial results and suchlike for the fiscal year ended June, 2007. I sent over a few clarifying questions yesterday. Responses included:
- Yes, the company experienced 330% year-over-year annual revenue growth.
- The majority of DATAllegro customers have bought systems in the 25-100 terabyte range.
- One system over 250 terabytes has been in production for months (surely the one I previously wrote about); a second is being installed.
- DATAllegro has “about 100” employees. By way of comparison, Netezza reported 225 full-time employees for the year ended January, 2007 – which probably means as of January 31, 2007.
All told, it sounds as if DATAllegro is more than 1/3 the size of Netezza, although given its higher system size and price points I’d guess it has well under 1/3 as many customers.
Here’s a link. I’ll likely edit that to something more permament-seeming later, and generally spruce this up when I’m not so rushed.
| Categories: Analytic technologies, Data warehouse appliances, Data warehousing, DATAllegro | 8 Comments |
Either there’s enormous interest in EnterpriseDB and/or mid-range relational DBMS …
… or else I’m one heck of a webinar draw.
We had 364 attendees for today’s webcast with EnterpriseDB, which is a huge number for that sort of thing.
| Categories: EnterpriseDB and Postgres Plus, Mid-range, Open source | 1 Comment |
Infobright BrightHouse — columnar, VERY compressed, simple, and related to MySQL
To a first approximation, Infobright – maker of BrightHouse — is yet another data warehouse DBMS specialist with a columnar architecture, boasting great compression and running on commodity hardware, emphasizing easy set-up, simple administration, great price-performance, and hence generally low TCO. BrightHouse isn’t actually MPP yet, but Infobright confidently promises a generally available MPP version by the end of 2008. The company says that experience shows >10:1 compression of user data is realistic – i.e., an expansion ratio that’s fractional, and indeed better than 1/10:1. Accordingly, despite the lack of shared-nothing parallelism, Infobright claims a sweet spot of 1-10 terabyte warehouses, and makes occasional references to figures up to 30 terabytes or so of user data.
BrightHouse is essentially a MySQL storage engine, and hence gets a lot of connectivity and BI tool support features from MySQL for “free.” Beyond that, Infobright’s core technical idea is to chop columns of data into 64K chunks, called data packs, and then store concise information about what’s in the packs. The more basic information is stored in data pack nodes,* one per data pack. If you’re familiar with Netezza zone maps, data pack nodes sound like zone maps on steroids. They store maximum values, minimum values, and (where meaningful) aggregates, and also encode information as to which intervals between the min and max values do or don’t contain actual data values. Read more