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
Hadoop notes
I visited California recently, and chatted with numerous companies involved in Hadoop — Cloudera, Hortonworks, MapR, DataStax, Datameer, and more. I’ll defer further Hadoop technical discussions for now — my target to restart them is later this month — but that still leaves some other issues to discuss, namely adoption and partnering.
The total number of enterprises in the world paying subscription and license fees that they would regard as being for “Hadoop or something Hadoop-related” probably is not much over 100 right now, but I’d expect to see pretty rapid growth. Beyond that, let’s divide customers into three groups:
- Internet businesses.
- Traditional enterprises ‘ internet operations.
- Traditional enterprises’ other operations.
Hadoop vendors, in different mixes, claim to be doing well in all three segments. Even so, almost all use cases involve some kind of machine-generated data, with one exception being a credit card vendor crunching a large database of transaction details. Multiple kinds of machine-generated data come into play — web/network/mobile device logs, financial trade data, scientific/experimental data, and more. In particular, pharmaceutical research got some mentions, which makes sense, in that it’s one area of scientific research that actually enjoys fat for-profit research budgets.
| Categories: Cloudera, Hadoop, Health care, Hortonworks, Investment research and trading, Log analysis, MapR, MapReduce, Market share and customer counts, Scientific research, Web analytics | 5 Comments |
Petabyte-scale Hadoop clusters (dozens of them)
I recently learned that there are 7 Vertica clusters with a petabyte (or more) each of user data. So I asked around about other petabyte-scale clusters. It turns out that there are several dozen such clusters (at least) running Hadoop.
Cloudera can identify 22 CDH (Cloudera Distribution [of] Hadoop) clusters holding one petabyte or more of user data each, at 16 different organizations. This does not count Facebook or Yahoo, who are huge Hadoop users but not, I gather, running CDH. Meanwhile, Eric Baldeschwieler of Hortonworks tells me that Yahoo’s latest stated figures are:
- 42,000 Hadoop nodes …
- … holding 180-200 petabytes of data.
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
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
Temporal data, time series, and imprecise predicates
I’ve been confused about temporal data management for a while, because there are several different things going on.
- Date arithmetic. This of course has been around for a very long — er, for a very long time.
- Time-series-aware compression. This has been around for quite a while too.
- “Time travel”/snapshotting — preserving the state of the database at previous points in time. This is a matter of exposing (and not throwing away) the information you capture via MVCC (Multi-Version Concurrency Control) and/or append-only updates (as opposed to update-in-place). Those update strategies are increasingly popular for pretty much anything except update-intensive OLTP (OnLine Transaction Processing) DBMS, so time-travel/snapshotting is an achievable feature for most vendors.
- Bitemporal data access. This occurs when a fact has both a transaction timestamp and a separate validity duration. A Wikipedia article seems to cover the subject pretty well, and I touched on Teradata’s bitemporal plans back in 2009.
- Time series SQL extensions. Vertica explained its version of these to me a few days ago. I imagine Sybase IQ and other serious financial-trading market players have similar features.
In essence, the point of time series/event series SQL functionality is to do SQL against incomplete, imprecise, or derived data.* Read more
| Categories: Analytic technologies, Data types, Investment research and trading, Log analysis, Sybase, Telecommunications, Theory and architecture, Vertica Systems | 1 Comment |
Columnar DBMS vendor customer metrics
Last April, I asked some columnar DBMS vendors to share customer metrics. They answered, but it took until now to iron out a couple of details. Overall, the answers are pretty impressive. Read more
Infobright 4.0
Infobright is announcing its 4.0 release, with imminent availability. In marketing and product alike, Infobright is betting the farm on machine-generated data. This hasn’t been Infobright’s strategy from the getgo, but it is these days, with pretty good focus and commitment. While some fraction of Infobright’s customer base is in the Sybase-IQ-like data mart market — and indeed Infobright put out a customer-win press release in that market a few days ago — Infobright’s current customer targets seem to be mainly:
- Web companies, many of which are already MySQL users.
- Telecommunication and similar log data, especially in OEM relationships.
- Trading/financial services, especially at mid-tier companies.
Key aspects of Infobright 4.0 include: Read more
| Categories: Data warehousing, Database compression, Infobright, Investment research and trading, Log analysis, Open source, Telecommunications, Web analytics | 7 Comments |
Dirty data, stored dirt cheap
A major driver of Hadoop adoption is the “big bit bucket” use case. Users take a whole lot of data, often machine-generated data in logs of different kinds, and dump it into one place, managed by Hadoop, at open-source pricing. Hadoop hardware doesn’t need to be that costly either. And once you get that data into Hadoop, there are a whole lot of things you can do with it.
Of course, there are various outfits who’d like to sell you not-so-cheap bit buckets. Contending technologies include Hadoop appliances (which I don’t believe in), Splunk (which in many use cases I do), and MarkLogic (ditto, but often the cases are different from Splunk’s). Cloudera and IBM, among other vendors, would also like to sell you some proprietary software to go with your standard Apache Hadoop code.
So the question arises — why would you want to spend serious money to look after your low-value data? The answer, of course, is that maybe your log data isn’t so low-value. Read more
| Categories: Hadoop, Investment research and trading, Log analysis, Splunk | 7 Comments |
Notes on short-request scale-out MySQL
A press person recently asked about:
… start-ups that are building technologies to enable MySQL and other SQL databases to get over some of the problems they have in scaling past a certain size. … I’d like to get a sense as to whether or not the problems are as severe and wide spread as these companies are telling me? If so, why wouldn’t a customer just move to a new database?
While that sounds as if he was asking about scale-out relational DBMS in general, MySQL or otherwise, short-request or analytic, it turned out that he was asking just about short-request scale-out MySQL. My thoughts and comments on that narrower subject include(d) but are not limited to: Read more
Revolution Analytics update
I wasn’t too impressed when I spoke with Revolution Analytics at the time of its relaunch last year. But a conversation Thursday evening was much clearer. And I even learned some cool stuff about general predictive modeling trends (see the bottom of this post).
Revolution Analytics business and business model highlights include:
- Revolution Analytics is an open-core vendor built around the R language. That is, Revolution Analytics offers proprietary code and support, with subscription pricing, that help in the use of open source software.
- Unlike most open-core vendors I can think of, Revolution Analytics takes little responsibility for the actual open source part. Some “grants” for developing certain open source R pieces seem to be the main exception. While this has caused some hard feelings, I don’t have an accurate sense for their scope or severity.
- Revolution Analytics also sells a single-user/workstation version of its product, freely admitting that this is mainly a lead generation strategy or, in my lingo, a “break-even leader.”
- Revolution Analytics boasts around 100 customers, split about 70-30 between the workstation seeding stuff and the real server product.
- Revolution Analytics has “about” 37 employees. Headquarters are at 101 University Avenue (do I have to say in what city?
). There are also a development office in Seattle and a sales office in New York. - Revolution Analytics’ pricing is by size of server. “Small” servers — i.e. up to 12 cores — start at $25K/year.
- Unsurprisingly, adoption is more alongside SAS et al. than rip-and-replace.