Investment research and trading

Discussion of how data management and analytic technologies are used in trading and investment research. (As opposed to a discussion of the services we ourselves provide to investors.) Related subjects include:

October 18, 2012

Notes on Hadoop adoption and trends

With Strata/Hadoop World being next week, there is much Hadoop discussion. One theme of the season is BI over Hadoop. I have at least 5 clients claiming they’re uniquely positioned to support that (most of whom partner with a 6th client, Tableau); the first 2 whose offerings I’ve actually written about are Teradata Aster and Hadapt. More generally, I’m hearing “Using Hadoop is hard; we’re here to make it easier for you.”

If enterprises aren’t yet happily running business intelligence against Hadoop, what are they doing with it instead? I took the opportunity to ask Cloudera, whose answers didn’t contradict anything I’m hearing elsewhere. As Cloudera tells it (approximately — this part of the conversation* was rushed):   Read more

September 24, 2012

Notes on Hadoop adoption

I successfully resisted telephone consulting while on vacation, but I did do some by email. One was on the oft-recurring subject of Hadoop adoption. I think it’s OK to adapt some of that into a post.

Notes on past and current Hadoop adoption include:

Thoughts on how Hadoop adoption will look going forward include: Read more

July 28, 2012

Some Vertica 6 features

Vertica 6 was recently announced, and so it seemed like a good time to catch up on Vertica features. The main topics I want to address are:

Also:

In general, the main themes of Vertica 6 appear to be:

Let’s do the analytic functionality first. Notes on that include:

I’ll also take this opportunity to expand on something I wrote about a few vendors — including Vertica — at the end of my post on approximate query results. When I probed how customers of Vertica and other RDBMS-based analytic platform vendors used vendor-proprietary advanced analytic SQL and other analytic capabilities, answers included: Read more

July 15, 2012

Issues in regulatory compliance

From time to time, I hear of regulatory requirements to retain, analyze, and/or protect data in various ways. It’s hard to get a comprehensive picture of these, as they vary both by industry and jurisdiction; so I generally let such compliance issues slide. Still, perhaps I should use one post to pull together what is surely a very partial list.

Most such compliance requirements have one of two emphases: Either you need to keep your customers’ data safe against misuse, or else you’re supposed to supply information to government authorities. From a data management and analysis standpoint, the former area mainly boils down to:

The latter, however, has numerous aspects.

First, there are many purposes for the data retention and analysis, including but by no means limited to: Read more

July 2, 2012

Introduction to Yarcdata

Cray’s strategy these days seems to be:

At the moment, the main diversifications are:

The last of the three is what Cray subsidiary Yarcdata is all about. Read more

June 18, 2012

Introduction to MemSQL

I talked with MemSQL shortly before today’s launch. MemSQL technology basics are:

MemSQL’s performance claims include:

MemSQL company basics include: Read more

May 28, 2012

Quick-turnaround predictive modeling

Last November, I wrote two posts on agile predictive analytics. It’s time to return to the subject. I’m used to KXEN talking about the ability to do predictive modeling, very quickly, perhaps without professional statisticians; that the core of what KXEN does. But I was surprised when Revolution Analytics told me a similar story, based on a different approach, because ordinarily that’s not how R is used at all.

Ultimately, there seem to be three reasons why you’d want quick turnaround on your predictive modeling: Read more

May 21, 2012

Cool analytic stories

There are several reasons it’s hard to confirm great analytic user stories. First, there aren’t as many jaw-dropping use cases as one might think. For as I wrote about performance, new technology tends to make things better, but not radically so. After all, if its applications are …

… all that bloody important, then probably people have already been making do to get it done as best they can, even in an inferior way.

Further, some of the best stories are hard to confirm; even the famed beer/diapers story isn’t really true. Many application areas are hard to nail down due to confidentiality, especially but not only in such “adversarial” domains as anti-terrorism, anti-spam, or anti-fraud.

Even so, I have two questions in my inbox that boil down to “What are the coolest or most significant analytics stories out there?” So let’s round up some of what I know. Read more

May 1, 2012

Thinking about market segments

It is a reasonable (over)simplification to say that my business boils down to:

One complication that commonly creeps in is that different groups of users have different buying practices and technology needs. Usually, I nod to that point in passing, perhaps by listing different application areas for a company or product. But now let’s address it head on. Whether or not you care about the particulars, I hope the sheer length of this post reminds you that there are many different market segments out there.

Last June I wrote:

In almost any IT decision, there are a number of environmental constraints that need to be acknowledged. Organizations may have standard vendors, favored vendors, or simply vendors who give them particularly deep discounts. Legacy systems are in place, application and system alike, and may or may not be open to replacement. Enterprises may have on-premise or off-premise preferences; SaaS (Software as a Service) vendors probably have multitenancy concerns. Your organization can determine which aspects of your system you’d ideally like to see be tightly integrated with each other, and which you’d prefer to keep only loosely coupled. You may have biases for or against open-source software. You may be pro- or anti-appliance. Some applications have a substantial need for elastic scaling. And some kinds of issues cut across multiple areas, such as budget, timeframe, security, or trained personnel.

I’d further say that it matters whether the buyer:

Now let’s map those considerations (and others) to some specific market segments. Read more

February 26, 2012

SAP HANA today

SAP HANA has gotten much attention, mainly for its potential. I finally got briefed on HANA a few weeks ago. While we didn’t have time for all that much detail, it still might be interesting to talk about where SAP HANA stands today.

The HANA section of SAP’s website is a confusing and sometimes inaccurate mess. But an IBM whitepaper on SAP HANA gives some helpful background.

SAP HANA is positioned as an “appliance”. So far as I can tell, that really means it’s a software product for which there are a variety of emphatically-recommended hardware configurations — Intel-only, from what right now are eight usual-suspect hardware partners. Anyhow, the core of SAP HANA is an in-memory DBMS. Particulars include:

SAP says that the row-store part is based both on P*Time, an acquisition from Korea some time ago, and also on SAP’s own MaxDB. The IBM white paper mentions only the MaxDB aspect. (Edit: Actually, see the comment thread below.) Based on a variety of clues, I conjecture that this was an aspect of SAP HANA development that did not go entirely smoothly.

Other SAP HANA components include:  Read more

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