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:
After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.
- My claim that Spark will replace Hadoop MapReduce got much Twitter attention — including some high-profile endorsements — and also some responses here.
- My MemSQL post led to a vigorous comparison of MemSQL vs. VoltDB.
- My post on hardware and storage spawned a lively discussion of Hadoop hardware pricing; even Cloudera wound up disagreeing with what I reported Cloudera as having said. Sadly, there was less response to the part about the partial (!) end of Moore’s Law.
- My Cloudera/SQL/Impala/Hive apparently was well-balanced, in that it got attacked from multiple sides via Twitter & email. Apparently, I was too hard on Impala, I was too hard on Hive, and I was too hard on boxes full of cardboard file cards as well.
- My post on the Intel/Cloudera deal garnered a comment reminding us Dell had pushed the Intel distro.
- My CitusDB post picked up a few clarifying comments.
Here is a catch-all post to complete the set. Read more
The name of this blog comes from an August, 2005 column. 8 1/2 years later, that analysis holds up pretty well. Indeed, I’d keep the first two precepts exactly as I proposed back then:
- Task-appropriate data managers. Much of this blog is about task-appropriate data stores, so I won’t say more about them in this post.
- Drastic limitations on relational schema complexity. I think I’ve been vindicated on that one by, for example:
- NoSQL and dynamic schemas.
- Schema-on-read, and its smarter younger brother schema-on-need.
- Limitations on the performance and/or allowed functionality of joins in scale-out short-request RDBMS, and the relative lack of complaints about same.
- Funky database design from major Software as a Service (SaaS) vendors such as Workday and Salesforce.com.
- A whole lot of logs.
I’d also keep the general sense of the third precept, namely appropriately-capable data integration, but for that one the specifics do need some serious rework.
For starters, let me say: Read more
|Categories: About this blog, Business intelligence, Database diversity, EAI, EII, ETL, ELT, ETLT, Investment research and trading, NoSQL, Schema on need||2 Comments|
From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:
- Hadoop (always, and please see below).
- Analytic RDBMS (ditto).
- NoSQL and NewSQL.
- Specifically, SQL-on-Hadoop
- Spark and other memory-centric technology, including streaming.
- Public policy, mainly but not only in the area of surveillance/privacy.
- General strategic advice for all sizes of tech company.
Other stuff on my mind includes but is not limited to:
1. Certain categories of buying organizations are inherently leading-edge.
- Internet companies have adopted Hadoop, NoSQL, NewSQL and all that en masse. Often, they won’t even look at things that are conventional or expensive.
- US telecom companies have been buying 1 each of every DBMS on the market since pre-relational days.
- Financial services firms — specifically algorithmic traders and broker-dealers — have been in their own technical world for decades …
- … as have national-security agencies …
- … as have pharmaceutical research departments.
Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.
I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.
1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.
Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.
Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:
- There are vast numbers of companies offering data-management-related technology. They need ways to differentiate.
- Doing analytics at short-request speeds is an obvious data-management-related challenge, and not yet comprehensively addressed.
2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:
- Customer interaction
- Network and sensor monitoring
- Game and mobile application back-ends
Also arising fairly frequently are:
- Algorithmic trading
- Risk measurement
- Law enforcement/national security
- Stakeholder-facing analytics
I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.
The third of my three MySQL-oriented clients I alluded to yesterday is MemSQL. When I wrote about MemSQL last June, the product was an in-memory single-server MySQL workalike. Now scale-out has been added, with general availability today.
MemSQL’s flagship reference is Zynga, across 100s of servers. Beyond that, the company claims (to quote a late draft of the press release):
Enterprises are already using distributed MemSQL in production for operational analytics, network security, real-time recommendations, and risk management.
All four of those use cases fit MemSQL’s positioning in “real-time analytics”. Besides Zynga, MemSQL cites penetration into traditional low-latency markets — financial services (various subsectors) and ad-tech.
Highlights of MemSQL’s new distributed architecture start: Read more
|Categories: Clustering, Database compression, Emulation, transparency, portability, Games and virtual worlds, Investment research and trading, Log analysis, MemSQL, MySQL, NewSQL, Transparent sharding, Zynga||6 Comments|
Hmm. I probably should have broken this out as three posts rather than one after all. Sorry about that.
Discussions of DBMS performance are always odd, for starters because:
- Workloads and use cases vary greatly.
- In particular, benchmarks such as the YCSB or TPC-H aren’t very helpful.
- It’s common for databases or at least working sets to be entirely in RAM — but it’s not always required.
- Consistency and durability models vary. What’s more, in some systems — e.g. MongoDB — there’s considerable flexibility as to which model you use.
- In particular, there’s an increasingly common choice in which data is written synchronously to RAM on 2 or more servers, then asynchronously to disk on each of them. Performance in these cases can be quite different from when all writes need to be committed to disk. Of course, you need sufficient disk I/O to keep up, so SSDs (Solid-State Drives) can come in handy.
- Many workloads are inherently single node (replication aside). Others are not.
MongoDB and 10gen
I caught up with Ron Avnur at 10gen. Technical highlights included: Read more
It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.
Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:
- “We get data into a form in which it can be analyzed.” This is the story behind, among others:
- Most of the data integration and ETL (Extract/Transform/Load) industries, software vendors and consulting firms alike.
- Many things that purport to be “analytic applications” or data warehouse “quick starts”.
- “Data reduction” use cases in event processing.*
- Text analytics tools.
- “Forget all that transformation foofarah — just load (or write) data into our thing and start analyzing it immediately.” This at various times has been much of the story behind:
- Relational DBMS, according to their inventor E. F. Codd.
- MOLAP (Multidimensional OnLine Analytic Processing), also according to RDBMS inventor E. F. Codd.
- Any kind of analytic DBMS, or general purpose DBMS used for data warehousing.
- Newer kinds of analytic DBMS that are faster than older kinds.
- The “data mart spin-out” feature of certain analytic DBMS.
- In-memory analytic data stores.
- NoSQL DBMS that have a few analytic features.
- TokuDB, similarly.
- Electronic spreadsheets, from VisiCalc to Datameer.
- “Our tools help you with specific kinds of analyses or analytic displays.” This is the story underlying, among others:
- The business intelligence industry.
- The predictive analytics industry.
- Algorithmic trading use cases in complex event processing.*
- Some analytic applications.
*Complex event/stream processing terminology is always problematic.
My thoughts on all this start: Read more
In typical debates, the extremists on both sides are wrong. “SQL vs. NoSQL” is an example of that rule. For many traditional categories of database or application, it is reasonable to say:
- Relational databases are usually still a good default assumption …
- … but increasingly often, the default should be overridden with a more useful alternative.
Reasons to abandon SQL in any given area usually start:
- Creating a traditional relational schema is possible …
- … but it’s tedious or difficult …
- … especially since schema design is supposed to be done before you start coding.
Some would further say that NoSQL is cheaper, scales better, is cooler or whatever, but given the range of NewSQL alternatives, those claims are often overstated.
Sectors where these reasons kick in include but are not limited to: Read more
|Categories: Health care, Investment research and trading, Log analysis, NewSQL, NoSQL, Web analytics||8 Comments|
Business intelligence dashboards are frequently bashed. I slammed them back in 2006 and 2007. Mark Smith dropped the hammer last August. EIS, the most dashboard-like pre-1990s analytic technology, was also the most reviled. There are reasons for this disdain, but even so dashboards shouldn’t be dismissed entirely.
In essence, I’d say:
- Dashboards are overrated and oversold.
- They are useful even so.
- Their usefulness is ebbing as technology advances.
In particular: Read more
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
|Categories: Business intelligence, Cloudera, EAI, EII, ETL, ELT, ETLT, Hadoop, HBase, Health care, Investment research and trading, MapR, Market share and customer counts, Telecommunications, Web analytics||4 Comments|