Gartner’s 2008 data warehouse database management system Magic Quadrant is out
February, 2011 edit: I’ve now commented on Gartner’s 2010 Data Warehouse Database Management System Magic Quadrant as well.
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 it. (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.
Highlights of this year’s data warehouse DBMS Magic Quadrant include:
- Teradata is #1, Oracle is #2, and IBM is #3, with the first two if anything slightly extending their leads. (in 2006, IBM was #2.)
- Netezza has been given a nice upwards (actually, more rightwards) bump and is now a clear #4.
- Microsoft is treading water at a clear #5.
- Greenplum and Sybase have slid back some, but depending on which dimension you weight more heavily are somewhere in the #6-8 range.
- HP joins newly, as the other #6-8 competitor, a little behind Sybase.
- Vertica joins as a first-timer, as a clear #9.
- Kognitio and SAND are next, with hefty gains in “ability to execute”, both leapfrogging Sun/MySQL.
- Ingres, iLLuminate, and 1010data straggle in at the bottom, all of them new (at least versus 2006-7).
I don’t really have a lot of quarrel with the “completeness of vision” rankings. As I see it, important attributes of a data warehouse DBMS “vision” would include:
- A performance story across at least a reasonable range of workloads.
- Either a clear hardware architecture story, or else a clear story as to why hardware architecture is relatively unimportant.
- SQL 2003 and further features in integrated analytics.
- Reasonable OLTP-like features, from the basics — ACID compliance! — to manageability, high availability and fast-enough update/load.
- Good compatibility with third-party products.
Gartner’s rankings are not ridiculous by those standards. Aster would surely have ranked high, but obviously they did not meet the confirmed-sale requirements for inclusion.
So what about Gartner’s “ability to execute” rankings? These are approximately:
- Teradata at #1
- Oracle and IBM tied at #2-3
- HP, Sybase, Microsoft, and Netezza tied at #4-7
- Greenplum at #8, Vertica at #9, and everybody else trailing after
That looks like it’s basically a measure of revenue, blending overall corporate and data-warehouse-DBMS-specific figures in some way, adjusted for who can deploy the most credible-sounding executive who appears to simultaneously have his — I use the male pronoun deliberately — finger on development and revenue-generation alike.
Frankly, I think it’s that dimension that makes Gartner Magic Quadrants well-nigh meaningless. If you asked me in which vendor’s execution-on-vision I had the most confidence, I’d stammer around unless I felt free to reframe the question and shoot back “Which PART of the vision?” If you want to deploy a 1 terabyte data warehouse with a highly diverse workload — well, Oracle, IBM, Teradata, and to a lesser extent Microsoft have been doing that for years, and they deserve to be atop the ability-to-execute charts, with Netezza perhaps not far behind. If you want to run fast queries on cheap hardware on 200 GB of data, Sybase IQ is a proven market leader. If you want a cheap 100 TB data warehouse that will soon scale to over a petabyte, Oracle’s great achievements in other areas of DBMS and its clever Exadata ideas suffice merely to put it on a par with those smaller vendors that have actually deployed a few such systems each, albeit behind Teradata.
When selecting a database management system for analytic processing, confine yourself to those vendors whose products can, today, do everything you’re likely to need for the next few years. Further require that they be on track to soon deliver most of what you seriously want over that time period. And throw the Gartner MQ into the nearest bit bucket, before it confuses your evaluation cycle irredeemably.
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12 Responses to “Gartner’s 2008 data warehouse database management system Magic Quadrant is out”
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Curt,
the full report is accessible from Microsoft’s AR pages:
http://www.microsoft.com/presspass/itanalyst/default.mspx
Thanks! You’re on the ball as usual.
But the sheer number of factual inaccuracies is disturbing. Vertica is not the newest market entrant, nor was it the first DBMS vendor to be in the cloud, nor is it lacking for terabyte-scale references. Kognitio/White Cross didn’t just start selling product (as opposed to SaaS) a couple of years ago. In general, both the Kognitio and Sybase write-ups could lead one to believe the products are only a few years old. And so on and so forth.
Why SAS System is not in the list?
Good question. Probably a marketing choice on their part. I think their DBMS is just as separable from the rest of their tools as 1010data’s is.
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As a Business Intelligence Architect I have a problem with MPP databases being ranked so high. OLAP servers do not run well against MPP databases in MOLAP storage modes. MOLAP storage requires transactional bulk data extracts to “process” the OLAP cubes. MPP database are slow because they have to unhash large amounts of detailed data. SMP database servers are much faster. MPP vendors suggest not using MOLAP and use the much slower query response and unscalable ROLAP storage or you need to setup a hub and spoke architecture. With a SMP server as the spoke just to process OLAP cubes.
“As a Business Intelligence Architect I have a problem with MPP databases being ranked so high. OLAP servers do not run well against MPP databases in MOLAP storage modes. MOLAP storage requires transactional bulk data extracts to “process” the OLAP cubes. MPP database are slow because they have to unhash large amounts of detailed data. SMP database servers are much faster. MPP vendors suggest not using MOLAP and use the much slower query response and unscalable ROLAP storage or you need to setup a hub and spoke architecture. With a SMP server as the spoke just to process OLAP cubes.”
Huh..?
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Hello,
I understand this is a revenue ranking mostly but have you evaluated other options? There are old guys on the block like the Italian Advanced Systems, owners of SADAS columnar database which I think you should check out. A very effective engine, running on standard off-the-shelf hardware( a US$10k machine can run billions of records) and one of the most manageable engine I have ever seen.Worth taking a look.
Sounds a bit like the Kx/Kdb+ story …
Anyhow, they’ve never contacted me.
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