Analysis of Netezza and its data warehouse appliances. Related subjects include:

March 16, 2012

Juggling analytic databases

I’d like to survey a few related ideas:

Here goes. Read more

November 23, 2011

Hope for a new PostgreSQL era?

In a comedy of briefing errors, I’m not too clear on the details of my client’s new PostgreSQL-as-a-service offering, nor exactly on what my clients at VMware are bringing to the PostgreSQL virtualization/cloud party. That said:

So I think it would be cool if one or the other big company put significant wood behind the PostgreSQL arrow.

*While Vertica was originally released using little or no PostgreSQL code — reports varied — it featured high degrees of PostgreSQL compatibility.

November 21, 2011

Some big-vendor execution questions, and why they matter

When I drafted a list of key analytics-sector issues in honor of look-ahead season, the first item was “execution of various big vendors’ ambitious initiatives”. By “execute” I mean mainly:

Vendors mentioned here are Oracle, SAP, HP, and IBM. Anybody smaller got left out due to the length of this post. Among the bigger omissions were:

Read more

July 26, 2011

Remote machine-generated data

I refer often to machine-generated data, which is commonly generated inexpensively and in log-like formats, and is often best aggregated in a big bit bucket before you try to do much analysis on it. The term has caught on, to the point that perhaps it’s time to distinguish more carefully among different kinds of machine-generated data. In particular, I think it may be useful to distinguish between:

Here’s what I’m thinking of for the second category. I rather frequently hear of cases in which data is generated by large numbers of remote machines, which occasionally send messages home. For example:  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 21, 2011

It’s official — the grand central EDW will never happen

I pointed out last year that the grand central enterprise data warehouse couldn’t happen; the post started:

An enterprise data warehouse should:

  • Manage data to high standards of accuracy, consistency, cleanliness, clarity, and security.
  • Manage all the data in your organization.

Pick ONE.

IBM’s main theme at the Enzee Universe conference has been to say the same thing.

Merv Adrian’s talk at the same conference made it clear that Gartner feels the same way, as does he personally. Indeed, like me, he’s racked up multiple decades of industry experience without ever finding a single theoretically ideal grand central EDW.

Forrester Research has been a little less clear on the point, but generally seems to be on the correct side of the issue as well.

If somebody is still saying that one central enterprise data warehouse can hold all the information or data you need on which to base your business decisions, they’re probably not somebody you should be listening to very hard.

Is that clear, or should I hammer home the point even harder? :D

June 19, 2011

Investigative analytics and derived data: Enzee Universe 2011 talk

I’ll be speaking Monday, June 20 at IBM Netezza’s Enzee Universe conference. Thus, as is my custom:

The talk concept started out as “advanced analytics” (as opposed to fast query, a subject amply covered in the rest of any Netezza event), as a lunch break in what is otherwise a detailed “best practices” session. So I suggested we constrain the subject by focusing on a specific application area — customer acquisition and retention, something of importance to almost any enterprise, and which exploits most areas of analytic technology. Then I actually prepared the slides — and guess what? The mix of subjects will be skewed somewhat more toward generalities than I first intended, specifically in the areas of investigative analytics and derived data. And, as always when I speak, I’ll try to raise consciousness about the issues of liberty and privacy, our options as a society for addressing them, and the crucial role we play as an industry in helping policymakers deal with these technologically-intense subjects.

Slide 3 refers back to a post I made last December, saying there are six useful things you can do with analytic technology:

Slide 4 observes that investigative analytics:

Slide 5 gives my simplest overview of investigative analytics technology to date:  Read more

May 14, 2011

Alternatives for Hadoop/MapReduce data storage and management

There’s been a flurry of announcements recently in the Hadoop world. Much of it has been concentrated on Hadoop data storage and management. This is understandable, since HDFS (Hadoop Distributed File System) is quite a young (i.e. immature) system, with much strengthening and Bottleneck Whack-A-Mole remaining in its future.

Known HDFS and Hadoop data storage and management issues include but are not limited to:

Different entities have different ideas about how such deficiencies should be addressed.  Read more

April 21, 2011

In-memory, parallel, not-in-database SAS HPA does make sense after all

I talked with SAS about its new approach to parallel modeling. The two key points are:

The whole thing is called SAS HPA (High-Performance Analytics), in an obvious reference to HPC (High-Performance Computing). It will run initially on RAM-heavy appliances from Teradata and EMC Greenplum.

A lot of what’s going on here is that SAS found it annoyingly difficult to parallelize modeling within the framework of a massively parallel DBMS such as Teradata. Notes on that aspect include:

Read more

← Previous PageNext Page →

Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:


Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.