Edit: Comments on the February, 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems — and on the companies reviewed in it — are now up.
The Gartner 2010 Data Warehouse Database Management Systems Magic Quadrant is out. I shall now comment, just as I did to varying degrees on the 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants.
Note: Links to Gartner Magic Quadrants tend to be unstable. Please alert me if any problems arise; I’ll edit accordingly.
In my comments on the 2008 Gartner Data Warehouse Database Management Systems Magic Quadrant, I observed that Gartner’s “completeness of vision” scores were generally pretty reasonable, but their “ability to execute” rankings were somewhat bizarre; the same remains true this year. For example, Gartner ranks Ingres higher by that metric than Vertica, Aster Data, ParAccel, or Infobright. Yet each of those companies is growing nicely and delivering products that meet serious cutting-edge analytic DBMS needs, neither of which has been true of Ingres since about 1987.
The general list of “market forces, end-user expectations and vendors’ resulting solution approaches” at the top of the 2010 Gartner Data Warehouse Database Management System Magic Quadrant article is a mixed bag. Following Gartner’s order, I’ll address those first, and particular companies cited afterwards. Specific items and comments include:
- “Increased demand for optimization techniques and performance enhancement.“ Gartner seems to be saying that data warehouse DBMS buyers want lists of specific, esoteric performance features. Well, buyers always want their DBMS to run fast, and they’d like the products to be mature enough to have been through a few rounds of Bottleneck Whack-A-Mole, but otherwise I’m not sure I’d put that at the top of my list.
- “The argument made by purchasing departments that buying power increases when dealing with a single, incumbent vendor.“ I agree that vendor consolidation and account control are a huge part of the Oracle, Microsoft, IBM and even Teradata stories. (Vertica can prove it’s 10X more price-performant than Oracle and still not get the business.) But it’s not just about price negotiations; once annual maintenance is included, one has to squint pretty hard to see Oracle as a low-cost alternative. Also important is reducing the number of total product-specific skill-sets needed on the IT staff.
- “Prepackaged, prebalanced warehouse environments delivered using data warehouse appliances.“ Yep. To varying extents, Oracle, Microsoft, Teradata, and IBM are all committed to designed-hardware strategies.
- “Expectations for the delivery of on-site POCs.“ Honestly, not as many buyers insist on on-site Proofs of Concept as should. Still, Oracle is shameful in its reluctance to do them. (Teradata tries to avoid them too, for obvious reasons of expense, but is much more gracious about capitulating when the buyer insists.)
- “Cost controls and data warehouse performance management.“ See next comment.
- “Demands for delivering a fully mixed workload.“ I’d have phrased the workload management and administrative tools points rather differently than this, but so be it.
- “Demands for departmental analytics delivered quickly via data marts.“ Agreed. Data-mart-only installations are a huge part of the market of the analytic DBMS market. Data mart spin-out is also important.
- “Wider indexing and fast performance within clusters of data, delivered via column-based solutions.“ This bizarrely seems to conflate column stores and parallel processing (both of which are of course highly important).
- “A wave of new data warehouse implementers seeking fast-track, low-risk delivery.“ Well, yes. Netezza noticed that quite some years ago. And by now the long-gestation EDW (Enterprise Data Warehouse) is widely disliked.
- “Global organizations seeking distributed solutions as potential architecture.“ If this is the MPP point, it’s oddly phrased. If this is a suggestion that data warehouses should be partitioned across wide-area networks, it’s just plain odd. If it’s a reiteration that departments like to control their own data marts, I agree. And if it’s a comment on keep-data-in-the-country privacy laws, it could be the most prescient thing Donald Feinberg has said in many years.
Long though it is, that list of general items and issues for the 2010 Gartner Data Warehouse Database Management System Magic Quadrant has some gaps. Most glaringly, I don’t see any references to advanced analytics in general, or even to the specific case of integrated predictive analytics. There’s also nothing about solid-state memory or other storage-technology considerations, although in fairness it’s still early days for much of what vendors conceive of as competitive differentiation in those respects.
Here are some vendor-specific comments on the 2010 Gartner Data Warehouse Database Management System Magic Quadrant:
- It’s pretty bizarre to compare 1010data to database.com or Microsoft Azure. Kognitio would be a better choice. So would cloud-hosted instances of Vertica, Aster Data nCluster, or others.
- Gartner’s comments on Aster Data and nCluster are actually pretty reasonable.
- Gartner’s comments on EMC/Greenplum are a bit Kool-Aid-drinky, and don’t account for the inevitable flailing that occurs right after an acquisition. But otherwise they’re pretty reasonable.
- I don’t take IBM’s super-comprehensive-all-inclusive architectural stories as seriously as Gartner does.
- I don’t take Netezza’s small stable of OEM partners as seriously as Gartner does. I also don’t share Gartner’s optimism for the continuation of Netezza’s NEC partnership in the face of IBM’s Netezza ownership.
- I’m even more skeptical about illuminate than Gartner is.
- I’m delighted that Gartner has adopted my phrase machine-generated data (Infobright is one of several firms pushing that one).
- “Only open-source column-store DBMS” is a bit exaggerated, but Infobright is indeed the only one with serious traction, or offered by a serious analytic DBMS vendor.
- What Gartner said in connection with Ingres is too inaccurate to deserve detailed attention.
- While Gartner’s write-up of Kognitio is a bit confused, that’s excusable. Kognitio’s strategy changes often.
- I’m not persuaded by the claim of low Microsoft TCO. The days when Microsoft’s tools were vastly better than the competition’s are long gone. And using an OLTP DBMS for data warehousing generally takes more people effort than using something more purpose-built.
- Gartner is right to ding Oracle for high prices, high people costs, and unwillingness to do onsite POCs.
- Gartner is right that Exadata is a huge improvement over non-Exadata Oracle data warehousing.
- Gartner is right to suggest that Exadata can easily handle data warehouses over 20 terabytes in size, but wrong to suggest that software-only Oracle also can. Just because the pain is less than it was with earlier releases of Oracle doesn’t mean it isn’t still bad.
- Gartner’s comments on ParAccel are pretty reasonable.
- Gartner’s comments on compression in connection with SAND make no technical sense (tokenization is a key form of columnar compression, not an alternative to it). Also, SAP’s acquisition of Sybase is a business challenge for SAND, not a technical one.
- Unless I’m forgetting something, Sybase IQ has no more in-database data mining than any other Fuzzy Logix partner does.
- Gartner failed to note that, like other DBMS dating back to the 1990s and before, Sybase IQ is more complex to administer than some newer products are.
- Gartner’s take on Teradata is pretty reasonable.
- Gartner’s take on Vertica, while sloppy, is basically sensible. However, Gartner failed to note that Vertica is a laggard in non-query analytics. (I am sure those deficiencies are being addressed, but Vertica’s competitors are moving ahead as well.)