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. Read more
It’s been a while since I penetrated Oracle’s tight message control and actually talked with them about Exadata. But Doug Henschen wrote a good article about Exadata based on an Andy Mendelsohn webcast. I agree with almost all of it. At first I was a little surprised that Exadata’s emphasis shift from data warehousing to OLTP/generic consolidation hasn’t gone more quickly, but on the other hand:
- On the data warehouse side Exadata can alleviate screaming pain points.
- In OLTP consolidation, Exadata mainly can save money. (Yes, I just said a product from Oracle can save customers money, and I meant it. You may stop laughing at any time.)
Doug did overstate when he said that columnar architectures give 100X or more compression. That doesn’t happen. Yes, columnar compression can be >10X in a variety of use cases, while pre-Exadata Oracle index bloat can approach 10X at times; but even if you’re counting that way I doubt there are many instances in which it actually multiplies out to >100.
In other Exadata news, the long-standing observation that Oracle doesn’t like to do on-site Exadata POCs still holds true. A couple of existing Oracle users — one rather well-known — recently told me that Oracle won’t let them text Exadata except on Oracle premises. In one case, this is a deal-breaker keeping Exadata from being considered for a purchase, and Oracle still won’t budge.
Finally, I’m pretty sure that this “new” Softbank Teradata replacement Oracle has been touting since September as competitive evidence — which Doug’s article also references — isn’t quite what it sounds like. I believe Teradata’s version of the story, which somewhat edited goes like this: Read more
|Categories: Benchmarks and POCs, Columnar database management, Data warehouse appliances, Database compression, Exadata, Oracle, Teradata||26 Comments|
Mike Stonebraker recently kicked off some discussion about desirable architectural features of a columnar analytic DBMS. Let’s expand the conversation to cover desirable architectural characteristics of analytic DBMS in general. Read more
|Categories: Analytic technologies, Aster Data, Benchmarks and POCs, Columnar database management, Data pipelining, Data warehousing, Database compression, Exadata, Michael Stonebraker, Oracle, Solid-state memory, Theory and architecture||5 Comments|
Until recently, I was extremely critical of ParAccel’s marketing. But there was an almost-clean sweep of the relevant ParAccel executives, and the specific worst practices I was calling out have for the most part been eliminated. So I was open to talking and working with ParAccel again, and that’s now happening. On my recent California trip, I chatted with three ParAccel folks for a few hours. Based on that and other conversation, here’s the current ParAccel story as I understand it.
|Categories: Benchmarks and POCs, Columnar database management, Database compression, Investment research and trading, Memory-centric data management, ParAccel, Solid-state memory, Storage, Vertica Systems||10 Comments|
When you are selecting an analytic DBMS or appliance, most of the evaluation boils down to two questions:
- How quickly and cost-effectively does it execute SQL?
- What analytic functionality, SQL or otherwise, does it do a good job of executing?
And so, in undertaking such a selection, you need to start by addressing three issues:
- What does “speed” mean to you?
- What does “cost” mean to you?
- What analytic functionality do you need anyway?
After working through problems w/ travel, cell phones, and so on, Peter Boncz of VectorWise finally caught up with me for a regrettably brief call. Peter gave me the strong impression that what I’d written in the past about VectorWise had been and remained accurate, so I focused on filling in the gaps. Highlights included: Read more
|Categories: Actian and Ingres, Analytic technologies, Benchmarks and POCs, Columnar database management, Data warehousing, Database compression, Open source, VectorWise||2 Comments|
One of our readers was kind enough to walk me through his analytic DBMS evaluation process. The story is:
- The X Company (XCo) has a <1 TB database.
- 100s of XCo’s customers log in at once to run reports. 50-200 concurrent queries is a good target number.
- XCo had been “suffering” with Oracle and wanted to upgrade.
- XCo didn’t have a lot of money to spend. Netezza pulled out of the sales cycle early due to budget (and this was recently enough that Netezza Skimmer could have been bid).
- Greenplum didn’t offer any references that approached the desired number of concurrent users.
- Ultimately the evaluation came down to Vertica and ParAccel.
- Vertica won.
Notes on the Vertica vs. ParAccel selection include: Read more
|Categories: Analytic technologies, Benchmarks and POCs, Buying processes, Data warehousing, Greenplum, Netezza, Oracle, ParAccel, Vertica Systems||7 Comments|
Greenplum is making two product announcements this morning. Greenplum 4.0 is a revision of the core Greenplum database technology. In addition, Greenplum is announcing Greenplum Chorus, which is the first product release instantiating last year’s EDC (Enterprise Data Cloud) vision statement and marketing campaign.
Greenplum 4.0 highlights and related observations include: Read more
I talked with Geno Valente of XtremeData tonight. Highlights included:
- XtremeData still hasn’t sold any dbX stuff (they’ve had a side business in generic FPGA-based boards paying the bills for years). Well, there may have been some paid POCs (proofs of concept) or something, but real sales haven’t come through yet.
- XtremeData does have three prospects who have said “Yes”, and expects one order to come through this month.
- XtremeData continues to believe it shines when:
- Data models are complex
- In particular, there are complex joins
- In particular, two large tables have to be joined with each other, under circumstances where no product can avoid doing vast data redistribution
- XtremeData insists that all the nice things Bill Inmon – including in webinars — has said about it has not been for pay or other similar business compensation. That’s quite unusual.
- XtremeData is coming out with a new product, codenamed the Personal Data Warehouse (PDW), which:
- Is ready to go into beta test
- Should be launched in a month and a half or so
- Will have a different name when it is launched
Naming aside, Read more
|Categories: Analytic technologies, Benchmarks and POCs, Data warehouse appliances, Data warehousing, Database compression, Kickfire, Market share and customer counts, Netezza, Pricing, XtremeData||5 Comments|
An enterprise user wrote in with a question that boils down to:
What are reasonable MDX performance expectations?
MDX doesn’t come up in my life very much, and I don’t have much intuition about it. E.g., I don’t know whether one can slap an MDX-to-SQL converter on top of a fast analytic RDBMS and go to town. What’s more, I’m heading off on vacation and don’t feel like researching the matter myself in the immediate future.
So here’s the long form of the question. Any thoughts?
I have a general question on assessing the performance of an OLAP technology using a set of MDX queries. I would be interested to know if there are any benchmark MDX performance tests/results comparing different OLAP technologies (which may be based on different underlying DBMS’s if appropriate) on similar hardware setup, or even comparisons of complete appliance solutions. More generally, I want to determine what performance limits I could reasonably expect on what I think are fairly standard servers.
In my own work, I have set up a star schema model centered on a Fact table of 100 million rows (approx 60 columns), with dimensions ranging in cardinality from 5 to 10,000. In ad hoc analytics, is it expected that any query against such a dataset should return a result within a minute or two (i.e. before a user gets impatient), regardless of whether that query returns 100 cells or 50,000 cells (without relying on any aggregate table or caching mechanism)? Or is that level of performance only expected with a high end massively parallel software/hardware solution? The server specs I’m testing with are: 32-bit 4 core, 4GB RAM, 7.2k RPM SATA drive, running Windows Server 2003; 64-bit 8 core, 32GB RAM, 3 Gb/s SAS drive, running Windows Server 2003 (x64).
I realise that caching of query results and pre-aggregation mechanisms can significantly improve performance, but I’m coming from the viewpoint that in purely exploratory analytics, it is not possible to have all combinations of dimensions calculated in advance, in addition to being maintained.