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:
- CEP (Complex Event Processing)
- (in Text Technologies) The use of text analytics in trading and investment research
Big Data is Watching You!
There’s a boom in large-scale analytics. The subjects of this analysis may be categorized as:
- People
- Financial trades
- Electronic networks
- Everything else
The most varied, interesting, and valuable of those four categories is the first one.
Why you should go to XLDB4
Scientific data commonly:
- Comes in large volumes
- Is machine-generated
- Is augmented by synthetic and/or derived data
- Has a spatial and/or temporal structure
In those respects, it is akin to some of the hottest areas for big data analytics, including:
- Investment trade data – big, partly machine generated, augmented (often), temporal
- Web/network log data – big, machine-generated, post-processed into derived form, temporal
- Marketing analytic data – big, post-processed into derived form
- Genomic data
So when Jacek Becla started the XLDB conferences on the premise that scientific and big data analytic challenges have a lot in common, he had a point. There are several tough database problems that the science-focused folks have taken the leading in thinking about, but which are soon going to matter to the commercial world as well. And that’s one of two big reasons why you should consider participating in XLDB4, October 6-7, at the SLAC facility in Menlo Park, CA, as an attendee, sponsor, or both.
The other big reason is that it is important for the world that XLDB succeed. Read more
| Categories: Investment research and trading, Log analysis, Scientific research, Web analytics | 2 Comments |
Cloudera Enterprise and Hadoop evolution
I talked with Cloudera a couple of weeks ago in connection with the impending release of Cloudera Enterprise. I’d say: Read more
VoltDB finally launches
VoltDB is finally launching today. As is common for companies in sectors I write about, VoltDB — or just “Volt” — has discovered the virtues of embargoes that end 12:01 am. Let’s go straight to the technical highlights:
- VoltDB is based on the H-Store technology, which I wrote about in February, 2009. Most of what I said about H-Store then applies to VoltDB today.
- VoltDB is a no-apologies ACID relational DBMS, which runs entirely in RAM.
- VoltDB has rather limited SQL. (One example: VoltDB can’t do SUMs in SQL.) However, VoltDB guy Tim Callaghan (Mark Callaghan’s lesser-known but nonetheless smart brother) asserts that if you code up the missing functionality, it’s almost as fast as if it were present in the DBMS to begin with, because there’s no added I/O from the handoff between the DBMS and the procedural code. (The data’s in RAM one way or the other.)
- VoltDB’s Big Conceptual Performance Story is that it does away with most locks, latches, logs, etc., and also most context switching.
- In particular, you’re supposed to partition your data and architect your application so that most transactions execute on a single core. When you can do that, you get VoltDB’s performance benefits. To the extent you can’t, you’re in two-phase-commit performance land. (More precisely, you’re doing 2PC for multi-core writes, which is surely a major reason that multi-core reads are a lot faster in VoltDB than multi-core writes.)
- VoltDB has a little less than one DBMS thread per core. When the data partitioning works as it should, you execute a complete transaction in that single thread. Poof. No context switching.
- A transaction in VoltDB is a Java stored procedure. (The early idea of Ruby on Rails in lieu of the Java/SQL combo didn’t hold up performance-wise.)
- Solid-state memory is not a viable alternative to RAM for VoltDB. Too slow.
- Instead, VoltDB lets you snapshot data to disk at tunable intervals. “Continuous” is one of the options, wherein a new snapshot starts being made as soon as the last one completes.
- In addition, VoltDB will also spool a kind of transaction log to the target of your choice. (Obvious choice: An analytic DBMS such as Vertica, but there’s no such connectivity partnership actually in place at this time.)
More on Sybase IQ, including Version 15.2
Back in March, Sybase was kind enough to give me permission to post a slide deck about Sybase IQ. Well, I’m finally getting around to doing so. Highlights include but are not limited to:
- Slide 2 has some market success figures and so on. (>3100 copies at >1800 users, >200 sales last year)
- Slides 6-11 give more detail on Sybase’s indexing and data access methods than I put into my recent technical basics of Sybase IQ post.
- Slide 16 reminds us that in-database data mining is quite competitive with what SAS has actually delivered with its DBMS partners, even if it doesn’t have the nice architectural approach of Aster or Netezza. (I.e., Sybase IQ’s more-than-SQL advanced analytics story relies on C++ UDFs — User Defined Functions — running in-process with the DBMS.) In particular, there’s a data mining/predictive analytics library — modeling and scoring both — licensed from a small third party.
- A number of the other later slides also have quite a bit of technical crunch. (More on some of those points below too.)
Sybase IQ may have a bit of a funky architecture (e.g., no MPP), but the age of the product and the substantial revenue it generates have allowed Sybase to put in a bunch of product features that newer vendors haven’t gotten around to yet.
More recently, Sybase volunteered permission for me to preannounce Sybase IQ Version 15.2 by a few days (it’s scheduled to come out this week). Read more
Revolution Analytics seems very confused
Revolution Analytics is a relaunch of a company previously known as REvolution Computing, built around the open source R language. Last week they sent around email claiming they were a new company (false), and asking for briefings in connection with an embargo this morning. I talked to Revolution Analytics yesterday, and they told me the embargo had been moved to Thursday.* However, Revolution apparently neglected to tell the press the same thing, and there’s an article out today — quoting me, because I’d given quotes in line with the original embargo, before I’d had the briefing myself. And what’s all this botched timing about? Mainly, it seems to be for a “statement of direction” about software Revolution Analytics hasn’t actually developed yet.
*More precisely, they spoke as if the embargo had been Thursday all along.
| Categories: Investment research and trading, Parallelization, Predictive modeling and advanced analytics, Revolution Analytics, SAS Institute | 13 Comments |
Examples of machine-generated data
Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Read more
| Categories: Analytic technologies, Data warehousing, Games and virtual worlds, Investment research and trading, Log analysis, Oracle, Telecommunications, Web analytics | 28 Comments |
Vertica update
I caught up with Jerry Held (Chairman) and Dave Menninger (VP Marketing) of Vertica for a chat yesterday. The immediate reason for the call was that a competitor had tipped me off to the departure of Vertica CEO Ralph Breslauer, which of course raises a host of questions. Highlights of the call included:
- Vertica had a “killer” Q4 and is doing very well in Q1 again.
- Vertica burned hardly any cash last year; i.e., it was close to cash-flow neutral in 2009.
- Vertica is hiring aggressively, e.g., in sales.
- Vertica is well down the path with several CEO candidates who Jerry regards as outstanding. He is hopeful there will be a new CEO in April. (But I bet that would be late April, given what Jerry mentioned about his own travel plans.)
- Absent a full-time CEO, Jerry and Andy Palmer are spending a lot more time with Vertica.
- One Vertica customer is approaching a petabyte of user data. The last time Vertica had checked, that customer had been more in the ¼ petabyte range.
- Other multi-hundred terabyte Vertica databases were mentioned, including one where Vertica claims to have beaten Teradata and perhaps other competitors in a head-to-head competition (it sounds like that one’s too recent to be deployed yet).
- Vertica sees Aster and Greenplum competitively more often than it sees ParAccel.
- Vertica sees Sybase IQ competitively a lot in financial services (in new-name accounts for Sybase as well as where some kind of Sybase DBMS is an incumbent), and more occasionally in other sectors.
NDA parts of the conversation also gave me the impression that Vertica is moving forward just as eagerly as its peers. I.e., I didn’t uncover any reason to think that Ralph’s departure is a sign of trouble, of the company being shopped, etc. Read more
| Categories: Analytic technologies, Data warehousing, Investment research and trading, Market share and customer counts, ParAccel, Petabyte-scale data management, Sybase, Vertica Systems | 6 Comments |
Aster Data nCluster 4.5
Like Vertica, Netezza, and Teradata, Aster is using this week to pre-announce a forthcoming product release, Aster Data nCluster 4.5. Aster is really hanging its identity on “Big Data Analytics” or some variant of that concept, and so the two major named parts of Aster nCluster 4.5 are:
- Aster Data Analytic Foundation, a set of analytic packages prebuilt in Aster’s SQL-MapReduce
- Aster Data Developer Express, an Eclipse-based IDE (Integrated Development Environment) for developing and testing applications built on Aster nCluster, Aster SQL-MapReduce, and Aster Data Analytic Foundation
And in other Aster news:
- Along with the development GUI in Aster nCluster 4.5, there is also a new administrative GUI.
- Aster has certified that nCluster works with Fusion I/O boards, because at least one retail industry prospect cares. However, that in no way means that arm’s-length Fusion I/O certification is Aster’s ultimate solid-state memory strategy.
- I had the wrong impression about how far Aster/SAS integration has gotten. So far, it’s just at the connector level.
Aster Data Developer Express evidently does some cool stuff, like providing some sort of parallelism testing right on your desktop. It also generates lots of stub code, saving humans from the tedium of doing that. Useful, obviously.
But mainly, I want to write about the analytic packages. Read more
| Categories: Aster Data, Data warehousing, Investment research and trading, Predictive modeling and advanced analytics, RDF and graphs, SAS Institute, Teradata | 9 Comments |
Quick thoughts on the StreamBase Component Exchange
Streambase is announcing something called the StreamBase Component Exchange, for developers to exchange components to be used with the StreamBase engine, presumably on an open source basis. I simultaneously think:
- This is a good idea, and many software vendors should do it if they aren’t already.
- It’s no big deal.
For reasons why, let me quote an email I just sent to an inquiring reporter:
- StreamBase sells mainly to the financial services and intelligence community markets. Neither group will share much in the way of core algorithms.
- But both groups are pretty interested in open source software even so. (I think for both the price and customizability benefits.)
- Open source software commonly gets community contributions for connectors, adapters, and (national) language translations.
- But useful contributions in other areas are much rarer.
- Linden Labs is one of StreamBase’s few significant customers outside its two core markets.
- All of the above are consistent with the press release (which quotes only one StreamBase customer — guess who?).