Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — concepts
The 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems is out. I’ll split my comments into two posts — this one on concepts, and a companion on specific vendor evaluations.
Links:
- Maintaining working links to Gartner Magic Quadrants is an adventure. But as of early February, 2013, this link seems live.
- I also commented on the 2011, 2010, 2009, 2008, 2007, and 2006 Gartner Magic Quadrants for Data Warehouse DBMS.
Let’s start by again noting that I regard Gartner Magic Quadrants as a bad use of good research. On the facts:
- Gartner collects a lot of input from traditional enterprises. I envy that resource.
- Gartner also does a good job of rounding up vendor claims about user base sizes and the like. If nothing else, you should skim the MQ report for that reason.
- Gartner observations about product feature sets are usually correct, although not so consistently that they should be relied on.
When it comes to evaluations, however, the Gartner Data Warehouse DBMS Magic Quadrant doesn’t do as well. My concerns (which overlap) start:
- The Gartner MQ conflates many different use cases into one ranking (inevitable in this kind of work, but still regrettable).
- A number of the MQ vendor evaluations seem hard to defend. So do some of Gartner’s specific comments.
- Some of Gartner’s criteria seemingly amount to “parrots back our opinions to us”.
- As do I, Gartner thinks a vendor’s business and financial strength are important. But Gartner overdoes the matter, drilling down into picky issues it can’t hope to judge, such as assessing a vendor’s “ability to generate and develop leads.” *
- The 2012 Gartner Data Warehouse DBMS Magic Quadrant is closer to being a 1-dimensional ranking than 2-dimensional, in that entries are clustered along the line x=y. This suggests strong correlation among the results on various specific evaluation criteria.
*I may focus more on marketing communications strategy than the whole Gartner database research team combined — but the only way I’d know whether Teradata’s lead gen is better than HP Vertica’s or vice-versa would be if both vendors happened to raise the matter during consulting sessions.
Specific product feature areas Gartner seems to emphasize include:
- Alignment with a “logical data warehouse” strategy.
- Analytic platform features.
- Compression.
- Administrative tools, including workload management.
- “Self-tuning” performance.
- Scale-out capabilities.
Most of this makes sense. But Gartner has been talking about the “logical data warehouse” for a long time without ever seeming to firm up what it is, as evidenced for example by some dueling summaries of the concept. So let’s drill down on the LDW.
I think “logical data warehouse” will wind up like “master data management” — i.e., it will be a goal and a business process, aided but not subsumed by some characteristic software. Beyond that, I’d say that generic, functional, high-performance data federation* software is a pipedream — building it would be as hard as building the mythical single DBMS that gives great functionality and performance, in all use cases, for all kinds of data. Just as DBMS need to be at least somewhat specialized in purpose, data federation software needs to be as well.
*While I disapprove, data virtualization seems to be the term that will win for describing data federation.
When Gartner refers to the “logical data warehouse” capabilities of analytic RDBMS — and the first sentence of the MQ report indeed specifies that the subject is “relational database management systems” — it seems to be looking for two things:
- Built-in data federation/query routing capabilities; i.e., specific features that help the DBMS interoperate with other data stores. But there seems to be little reference to relational federation/ external tables (which many vendors support) or text federation (which vendors with built-in search support, although that would mainly be Oracle, and its search is slow). Rather, this part of LDW is currently all about Hadoop interoperability, with bonus points for mentioning HCatalog.
- Management of multi-structured data. But with limited exceptions, nobody’s doing that well in an analytic RDBMS. And even when they do, that’s pretty much the opposite of the federation that the rest of the logical data warehouse concept seems to be about.
For those and other reasons, referring to the “logical data warehouse” features of an analytic RDBMS is problematic. I imagine Gartner will keep working at the “logical data warehouse” concept until it is more successfully fleshed out. But little weight should be placed on Gartner’s LDW-feature-evaluations of analytic RDBMS at this time.
Comments
5 Responses to “Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — concepts”
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Thanks for the comments Curt. There’s that “data virtualization” taxonomy issue creeping up again…Regards, Al D.
Curt, I think the MQ is aged and does not represent the reality that the distribution of capability is widely varied and correlated to over all volume – not just feature function.
The simple example is what this chart would look like if the constraint was Data Warehousing at 1PB or greater of raw data. 2/3rds of these companies would fall off the chart all together, and the distribution on the graph – particularly for all the upper right vendors would be entirely different.
Any – ALL – implementors of Data Warehouses must make this simple volumetric test prior to even evaluation of competent vendor stacks – hence the MQ chart is largely invalid and cannot be used given the high likely hood of a general mistake in the guidance. At least from the lens of the 10PB+ implementor…
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