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

October 22, 2014

Is analytic data management finally headed for the cloud?

It seems reasonable to wonder whether analytic data management is headed for the cloud. In no particular order:

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November 24, 2013

Thoughts on SaaS

Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:

For smaller enterprises, the core outsourcing argument is compelling. How small? Well:

So except for special cases, an enterprise with less than $100 million or so in revenue may have trouble affording on-site data processing, at least at a mission-critical level of robustness. It may well be better to use NetSuite or something like that, assuming needed features are available in SaaS form.*

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November 19, 2013

How Revolution Analytics parallelizes R

I talked tonight with Lee Edlefsen, Chief Scientist of Revolution Analytics, and now think I understand Revolution’s parallel R much better than I did before.

There are four primary ways that people try to parallelize predictive modeling:

One confusing aspect of this discussion is that it could reference several heavily-overlapping but not identical categories of algorithms, including:

  1. External memory algorithms, which operates on datasets too big to fit in main memory, by — for starters — reading in and working on a part of the data at a time. Lee observes that these are almost always parallelizable.
  2. What Revolution markets as External Memory Algorithms, which are those external memory algorithms it has gotten around to implementing so far. These are all parallelized. They are also all in the category of …
  3. … algorithms that can be parallelized by:
    • Operating on data in parts.
    • Getting intermediate results.
    • Combining them in some way for a final result.
  4. Algorithms of the previous category, where the way of combining them specifically is in the form of summation, such as those discussed in the famous paper Map-Reduce for Machine Learning on Multicore. Not all of Revolution’s current parallel algorithms fall into this group.

To be clear, all Revolution’s parallel algorithms are in Category #2 by definition and Category #3 in practice. However, they aren’t all in Category #4.

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November 10, 2013

RDBMS and their bundle-mates

Relational DBMS used to be fairly straightforward product suites, which boiled down to:

Now, however, most RDBMS are sold as part of something bigger.

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July 31, 2013

“Disruption” in the software industry

I lampoon the word “disruptive” for being badly overused. On the other hand, I often refer to the concept myself. Perhaps I should clarify. :)

You probably know that the modern concept of disruption comes from Clayton Christensen, specifically in The Innovator’s Dilemma and its sequel, The Innovator’s Solution. The basic ideas are:

In response (this is the Innovator’s Solution part):

But not all cleverness is “disruption”.

Here are some of the examples that make me think of the whole subject. Read more

May 27, 2013

Data skipping

Way back in 2006, I wrote about a cool Netezza feature called the zone map, which in essence allows you to do partition elimination even in the absence of strict range partitioning.

Netezza’s substitute for range partitioning is very simple. Netezza features “zone maps,” which note the minimum and maximum of each column value (if such concepts are meaningful) in each extent. This can amount to effective range partitioning over dates; if data is added over time, there’s a good chance that the data in any particular date range is clustered, and a zone map lets you pick out which data falls in the desired data range.

I further wrote

… that seems to be the primary scenario in which zone maps confer a large benefit.

But I now think that part was too pessimistic. For example, in bulk load scenarios, it’s easy to imagine ways in which data can be clustered or skewed. And in such cases, zone maps can let you skip a large fraction of potential I/O.

Over the years I’ve said that other things were reminiscent of Netezza zone maps, e.g. features of Infobright, SenSage, InfiniDB and even Microsoft SQL Server. But truth be told, when I actually use the phrase “zone map”, people usually give me a blank look.

In a recent briefing about BLU, IBM introduced me to a better term — data skipping. I like it and, unless somebody comes up with a good reason not to, I plan to start using it myself. :)

March 18, 2013

DBMS development and other subjects

The cardinal rules of DBMS development

Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.

That’s if things go extremely well.

Rule 2: You aren’t an exception to Rule 1. 

In particular:

DBMS with Hadoop underpinnings …

… aren’t exceptions to the cardinal rules of DBMS development. That applies to Impala (Cloudera), Stinger (Hortonworks), and Hadapt, among others. Fortunately, the relevant vendors seem to be well aware of this fact. Read more

February 6, 2013

Key questions when selecting an analytic RDBMS

I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:

Let’s drill down. Read more

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

December 2, 2012

Are column stores really better at compression?

A consensus has evolved that:

Still somewhat controversial is the claim that:

A strong plausibility argument for the latter point is that new in-memory analytic data stores tend to be columnar — think HANA or Platfora; compression is commonly cited as a big reason for the choice. (Another reason is that I/O bandwidth matters even when the I/O is from RAM, and there are further reasons yet.)

One group that made the in-memory columnar choice is the Spark/Shark guys at UC Berkeley’s AMP Lab. So when I talked with them Thursday (more on that another time, but it sounds like cool stuff), I took some time to ask why columnar stores are better at compression. In essence, they gave two reasons — simplicity, and speed of decompression.

In each case, the main supporting argument seemed to be that finding the values in a column is easier when they’re all together in a column store. Read more

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