February 8, 2012
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include:
- The Gartner Magic Quadrant’s comments on Teradata seem pretty fair. I don’t think I’m much in disagreement when I say:
- Teradata has the richest, most mature analytic DBMS offering.
- Teradata has an outstanding track record both for managing large data volumes and for high-concurrency mixed workloads.
- Aster Data was a cool Teradata acquisition, even if Teradata/Aster synergies or integration have been nominal to date.
- Teradata still needs to get out of its own way in marketing, positioning, packaging, and/or defining its premium-priced system vs. its more moderately-priced alternatives. Indeed, as necessary as this approach may have been to fending off encroachments by Netezza and others, what Teradata really needs to do is evolve to a more pick-your-own-node-combination mix-match kind of offering.
- Gartner has talked with a lot of Oracle Exadata users who say that the product works; Gartner also has stopped beating Oracle up for its previous policy of almost never doing onsite POCs (Proofs of Concept); both parts of that ring true with me. But Gartner also rightly dings Oracle for various issues in cost and cumbersomeness. Overall, while I agree there are organizations for which Oracle should indeed be a top-ranked choice, there are many others who shouldn’t put Oracle on their short list.
- Third in the Gartner MQ rankings is IBM.
- Gartner gets so caught up in reciting the names of various IBM product offerings that it neglects to say much good about DB2 itself. (I tend to have a similar problem.)
- But Gartner does mention concurrency as a strength. I agree, especially if we presume that that was a reference to DB2 rather than Netezza.
- Gartner cites Netezza’s post-acquisition annual growth rate as 30%. Gartner seems to think this is a good number. I disagree, but in Netezza’s defense, it has had to endure IBM’s post-acquisition on-boarding process.
- Arguably fourth in the Gartner Data Warehouse Magic Quadrant rankings is EMC/Greenplum.
- In general, Gartner likes the taste of Greenplum Kool-Aid.
- Gartner neglects to ding Greenplum for concurrency challenges, which I view as an oversight given Gartner’s general stress on that area.
- Gartner does ding Greenplum for support challenges.
- Gartner neglects to praise Greenplum for true hybrid row/columnar data management, a feature shared by Teradata and Vertica, among others, but not by Oracle, DB2, or Netezza.
- Gartner located a half-petabyte Greenplum database. This doesn’t surprise me, even though Greenplum has frequently made exaggerated claims about large-size database successes in the past.
- Gartner reports a >400 figure for Greenplum customers, which is plausible.
- In its first deviation from strict one-dimensional rank ordering, the Gartner Magic Quadrant ranks Sybase ahead of Greenplum in completeness of vision but behind in “ability to execute”.
- If that were the other way around, it might make more sense. Greenplum promises anything and everything you might ever want for analytic data management or the associated analysis; but Sybase has vastly more analytic DBMS users than Greenplum does, running a variety of demanding workloads.
- Gartner appears to think that Sybase IQ requires less database administration than I do.
- Gartner seems concerned that SAP will position HANA and Sybase ASE as, between them, the only DBMS you’ll ever need, casting doubt on Sybase IQ’s future. I wouldn’t worry about that if you have a problem you want to solve today.
- The Gartner Magic Quadrant for Data Warehouse Database Management Systems ranks Microsoft sixth overall, despite noting that there isn’t a single production reference for Microsoft’s Parallel Data Warehouse. In support of this ranking, it for example cites the compression feature, which distinguishes Microsoft SQL Server from no other product on the list except Kognitio. If you have such an undemanding data warehousing problem that many different analytic DBMS could meet your needs, there’s a good chance Microsoft SQL Server can also do the job; and if you’ve bought into the Microsoft technology stack, you might as well keep going down that path. Otherwise, I don’t know why somebody should adopt Microsoft’s offering at this time.
- Seventh along the main diagonal path in the Gartner Magic Quadrant is HP Vertica. I’d rank Vertica higher than that, but in fairness I note two execution concerns. First, HP has a lousy track record, both in acquisitions and in data warehousing/analytics. Second, Vertica is bad about answering my email. 🙂 Anyhow, Gartner doesn’t seem to have given Vertica credit either for its full customer count or for the multiple petabyte-scale databases Vertica runs.
- 1010data is an outlier, with Gartner noting that it only partly fits in with other “Data Warehousing Database Management” companies, and hence kind of confessing that 1010data’s specific location on the Magic Quadrant is somewhat arbitrary. Stuff like that is bound to happen, given the inherent difficulties of defining market categories. Anyhow, my thoughts on 1010data include:
- I’m nervous about the fact that 1010data doesn’t actually control its own DBMS technology, but rather relies on old code from the small private company KX Systems.
- There are three main reasons to consider 1010data:
- You want to enter the data mart outsourcing business in a casual way, and you like its SaaS offering.
- You want to engage in stakeholder-facing analytics in a casual way, and you like its SaaS offering.
- You love 1010data’s particular set of interactive analytic features and performance.
- Back to the main path winding along the Gartner Magic Quadrant main diagonal — next up is ParAccel. While I question some of the peripheral comments, I agree with Gartner’s core messages that:
- ParAccel, the product, is blazingly fast in certain use cases.
- ParAccel, the company, is dangerously small.
- Eighth on the Gartner MQ’s main path is Kognitio. This is too high. Kognitio positions itself as offering in-memory DBMS, yet stubbornly refuses to do any kind of data compression. That’s an awful combination of choices. As for using Kognitio’s data warehousing SaaS offering — why would you do that, when more modern products are available on a SaaS/cloud basis as well?
- Ninth in the Gartner Magic Quadrant main rankings is SAND.
- The SAND section is not a triumph of Gartner accuracy. For example:
- Gartner completely missed the errors in SAND’s reported customer counts.
- Gartner refers to SAND as being “in existence for approximately nine years”, which is too low by at least a factor of 2.
- Gartner says “SAND is a privately held company”, even though Merv knows better than that.
- Otherwise, Gartner’s opinion on SAND seems to boil down to “Interesting technology and ideas, but dangerously small company.” I agree.
- The SAND section is not a triumph of Gartner accuracy. For example:
- Tenth and too low in the Gartner MQ main rankings is Infobright.
- At least by some metrics (e.g. customer count), Infobright isn’t as dangerously small as ParAccel, SAND, Kognitio, et al.
- That said, Infobright is small and focused on machine-generated data. So I wouldn’t be confident in Infobright’s future technology path for human-generated data use cases.
- Infobright’s performance is uneven — blazing in cases where the Knowledge Grid helps, but not necessarily stellar by analytic DBMS standards when full table scans are called for.
- I agree with Gartner that the possibility of Oracle/MySQL future shenanigans is a concern. But while the energy behind MySQL forking efforts doesn’t seem too great right now, I’d expect them to revive and offer a successful escape path if it seemed Oracle was going to indeed play hardball.
- Also, given that it’s already an open source vendor, there are various kinds of assurances Infobright could give that would also help alleviate customer concerns.
- Actian, formerly Ingres, took a big tumble in Gartner’s rankings versus last year, when I simply wrote “What Gartner said in connection with Ingres is too inaccurate to deserve detailed attention.” I’m even a little harsher about Ingres/Actian’s DBMS products and prospects than Gartner is, but at least now we’re in the same ballpark.
- Along with Infobright, ParAccel, and SAND, Exasol appears to be another of the “good columnar technology/small company” crowd. As with other such products, one should be careful about fit-and-finish features that are missing today, as there is no assurance they’ll be added in a timely manner going forward.
- illuminate Solutions, which was on last year’s Gartner list, now appears to be an ex-company.
Categories: Columnar database management, Data mart outsourcing, Data warehouse appliances, Data warehousing, Database compression, EMC, Exadata, Exasol, Greenplum, illuminate Solutions, In-memory DBMS, Infobright, Kognitio, Market share and customer counts, Microsoft and SQL*Server, Open source, Oracle, ParAccel, Software as a Service (SaaS), Sybase, Teradata
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12 Responses to “Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same”
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Curt,
you didn’t include Microsoft. It’s in the challenger quadrant. Two things everyone overlooks about the low TOC for Microsoft (and I’ve been using MS products for well over 20 years) is first there is a ton of code you need to write. Second, is the Microsoft’s cavalier attitude to backward comparability. Code I wrote for Oracle 5-10 years ago still works (may need to update for better functionality but it works). Stuff I wrote 5-10 years ago for Microsoft is a crap shoot on whether it works or not.
Larry,
Look again; Microsoft is in there (it was sixth on Gartner’s “list”). 🙂
Interesting comment on Microsoft backward compatibility. Do you have some specific examples to point to?
You didn’t mention ParAccel. They seem to be still in the game and getting the job done w/o being acquired. Were they mentioned?
Bob,
Look again; I did mention ParAccel.
hi,
I was in London on the Gartner Bi summit where this was presented. I didn’t witness the end of this presentation but there is one very interesting point that was made there.
Gartner is currently promoting the idea of an LDW (logical datawarehouse) which is compromised of multiple blocks of
– a traditional structured DBMS (your old EDW)
– big data systems
– in memory DBMS
-…
all in one logical access point for your user. This LDW vision was not applied to this years MQ, but it will be influencing next years. So while this MQ is a non event since it’s mostly a copy of last year, next year might be a blast.
IMO the LDM thing is a good idea but it will be years before it actually works. It also remains to be seen whether it ends up being multiple blocks or just gets all sucked into the hadoop framework
I left the whole LDW idea out of this post for two reasons:
1. Length. Comments like those above — “You didn’t mention Subject X.” “Actually, I did.” — almost never happen on this blog; their presence in this case suggests the post overwhelmed people with length as it was.
2. It’s a concept I’ve been writing about for years under different names, and I frankly don’t know the details of Gartner’s spin on it.
That said, it’s certainly on my to-do list for a separate post.
unholyguy,
The moment something new comes up the first remarks are always ‘ this will replace the old X’ . This has never proven true. Hadoop will compliment our current landscape and will live side-by-side with technology we’ve been using for some years now. (Gartners vision and also mine)
Gartner also said that no current tech works for all the necessary cases here (LDW). But the moment they start using this as a measure in their MQ’s it put’s more focus there for some companies
Curt,
I can’t share the slides we recieved from Gartner, but in short it’s a virtual layer so users connect to one evironment. Behind this the right tech is choosen for the individual usecase.(in-memory,big-data,appliance,columnar,… whatever you need)
Remember hadoop is a framework not a software product. Multiple pieces of software can and do connect to data and eachother via the hadoop api tiers. It’s better to think of it as a big mainframe then a database
The advantage of making mpp databases speak hadoop and use hdfs as an underlying data store is pretty significant, as it eliminates the need to ship the data around. It doesn’t mean you don’t still have separate systems or that hadoop replaces anything, it’s just the level of interconnect, do they talk over the network and ship data or do they talk api and share data?
ikke,
On that level of generality, it’s obviousness. But hey — kudos to Gartner for teaching “obvious” stuff to folks who actually may not have grasped it yet.
[…] Like many folk out there, I am as interested in what the industry analysts make of the report as the report itself. Curt Monash’s comments are here: http://www.dbms2.com/2012/02/08/gartner-magic-quadrant-data-warehouse-2011-2012/ […]
[…] also commented on the 2011, 2010, 2009, 2008, 2007, and 2006 Gartner Magic Quadrants for Data Warehouse […]