Database diversity

Discussion of choices and variety in database management system architecture. Related subjects include:

July 8, 2012

Database diversity revisited

From time to time, I try to step back and build a little taxonomy for the variety in database technology. One effort was 4 1/2 years ago, in a pre-planned exchange with Mike Stonebraker (his side, alas, has since been taken down). A year ago I spelled out eight kinds of analytic database.

The angle I’ll take this time is to say that every sufficiently large enterprise needs to be cognizant of at least 7 kinds of database challenge. General notes on that include:

The Big Seven database challenges that almost any enterprise faces are: Read more

June 14, 2012

Workday update

In August 2010, I wrote about Workday’s interesting technical architecture, highlights of which included:

I caught up with Workday recently, and things have naturally evolved. Most of what we talked about (by my choice) dealt with data management, business intelligence, and the overlap between the two.

It is now reasonable to say that Workday’s servers fall into at least seven tiers, although we talked mainly about five that work together as a kind of giant app/database server amalgamation. The three that do noteworthy data management can be described as:

Two other Workday server tiers may be described as: Read more

April 7, 2012

Many kinds of memory-centric data management

I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:

Getting more specific than that is hard, however, because:

Consider, for example, some of the in-memory data management ideas kicking around. Read more

March 16, 2012

Juggling analytic databases

I’d like to survey a few related ideas:

Here goes. Read more

July 15, 2011

Soundbites: the Facebook/MySQL/NoSQL/VoltDB/Stonebraker flap, continued

As a follow-up to the latest Stonebraker kerfuffle, Derrick Harris asked me a bunch of smart followup questions. My responses and afterthoughts include:

Continuing with that discussion of DBMS alternatives:

And while we’re at it — going schema-free often makes a whole lot of sense. I need to write much more about the point, but for now let’s just say that I look favorably on the Big Four schema-free/NoSQL options of MongoDB, Couchbase, HBase, and Cassandra.

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

May 29, 2011

When it’s still best to use a relational DBMS

There are plenty of viable alternatives to relational database management systems. For short-request processing, both document stores and fully object-oriented DBMS can make sense. Text search engines have an important role to play. E. F. “Ted” Codd himself once suggested that relational DBMS weren’t best for analytics.* Analysis of machine-generated log data doesn’t always have a naturally relational aspect. And I could go on with more examples yet.

*Actually, he didn’t admit that what he was advocating was a different kind of DBMS, namely a MOLAP one — but he was. And he was wrong anyway about the necessity for MOLAP. But let’s overlook those details. :)

Nonetheless, relational DBMS dominate the market. As I see it, the reasons for relational dominance cluster into four areas (which of course overlap):

Generally speaking, I find the reasons for sticking with relational technology compelling in cases such as:  Read more

October 11, 2010

NoSQL overview

My NoSQL article is finally posted; I hope it lives up to all the foreshadowing. It is being run online at Intelligent Enterprise/Information Week, as per the link above, where Doug Henschen edited it with an admirably light touch.

Below please find three excerpts* that convey the essence of my thinking on NoSQL. For much more detail, please see the article itself.

*Notwithstanding my admiration for Doug’s editing, the excerpts are taken from my final pre-editing submission, not from the published article itself.

My quasi-definition of “NoSQL” wound up being:  Read more

October 10, 2010

Partnering with Cloudera

After I criticized the marketing of the Aster/Cloudera partnership, my clients at Aster Data and Cloudera ganged up on me and tried to persuade me I was wrong. Be that as it may, that conversation and others were helpful to me in understanding the core thesis:  Read more

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