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  • SAND Technology

    Analysis of SAND Technology and its products, such as SAND/DNA. Related subjects include:

    February 5, 2013

    Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — evaluations

    To my taste, the most glaring mis-rankings in the 2012/2013 Gartner Magic Quadrant for Data Warehouse Database Management are that it is too positive on Kognitio and too negative on Infobright. Secondarily, it is too negative on HP Vertica, and too positive on ParAccel and Actian/VectorWise. So let’s consider those vendors first.

    Gartner seems confused about Kognitio’s products and history alike.

    Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.

    * non-existent

    In the cases of HP Vertica, Infobright, ParAccel, and Actian/VectorWise, the 2012 Gartner Magic Quadrant for Data Warehouse Database Management’s facts are fairly accurate, but I dispute Gartner’s evaluation. When it comes to Vertica: Read more

    August 7, 2012

    Notes on some basic database terminology

    In a call Monday with a prominent company, I was told:

    That, to put it mildly, is not accurate. So I shall try, yet again, to set the record straight.

    In an industry where people often call a DBMS just a “database” — so that a database is something that manages a database! — one may wonder why I bother. Anyhow …

    1. The products commonly known as Oracle, Exadata, DB2, Sybase, SQL Server, Teradata, Sybase IQ, Netezza, Vertica, Greenplum, Aster, Infobright, SAND, ParAccel, Exasol, Kognitio et al. all either are or incorporate relational database management systems, aka RDBMS or relational DBMS.

    2. In principle, there can be difficulties in judging whether or not a DBMS is “relational”. In practice, those difficulties don’t arise — yet. Every significant DBMS still falls into one of two categories:

    *I expect the distinction to get more confusing soon, at which point I’ll adopt terms more precise than “relational things” and “relational stuff”.

    3. There are two chief kinds of relational DBMS: Read more

    March 31, 2012

    Our clients, and where they are located

    From time to time, I disclose our vendor client lists. Another iteration is below, the first since a little over a year ago. To be clear:

    For reasons explained below, I’ll group the clients geographically. Obviously, companies often have multiple locations, but this is approximately how it works from the standpoint of their interactions with me. Read more

    November 12, 2011

    Clarifying SAND’s customer metrics, positioning and technical story

    Talking with my clients at SAND can be confusing. That said:

    A few months ago, I wrote:

    SAND Technology reported >600 total customers, including >100 direct.

    Upon talking with the company, I need to revise that figure downward, from > 600 to 15.

    Read more

    September 25, 2011

    Workload management and RAM

    Closing out my recent round of Teradata-related posts, here’s a little anomaly:

    Read more

    July 5, 2011

    Eight kinds of analytic database (Part 2)

    In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear.? Read more

    July 5, 2011

    Eight kinds of analytic database (Part 1)

    Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.

    Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning.? Read more

    June 20, 2011

    Columnar DBMS vendor customer metrics

    Last April, I asked some columnar DBMS vendors to share customer metrics. They answered, but it took until now to iron out a couple of details. Overall, the answers are pretty impressive.? Read more

    February 28, 2011

    Updating our vendor client disclosures

    Edit: This disclosure has been superseded by a March, 2012 version.

    From time to time, I disclose our vendor client lists. Another iteration is below. To be clear:

    With that said, our vendor client disclosures at this time are:

    Read more

    February 5, 2011

    Comments on the Gartner 2010/2011 Data Warehouse Database Management Systems Magic Quadrant

    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

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