Summer Finance Tech Coding – Neo4j, Clojure/Om and Spark

I’m playing with a number of different technologies at the moment.  Mostly extensions of ideas I’ve previously blogged about.

  • Timelines and Neo4j
    • Specifically I’m interested in modelling the trade lifecycle in Neo4j (as discussed here) and also Skill Cloud (here) and avoiding the “current” view graphs that most applications/samples use.  Primarily leveraging ES6 code with Seraph as I’m not concerned about performance for this prototype.
    • There are a few interesting references documents that are worth a read:
      • Representing time dependent graphs in Neo4j
      • Time-Based Versioned Graphs – Ian’s solutions is quite clear in that it primarily leverages properties on relationships:

Each relationship has a from property with a long millisecond value representing 1 January 2014, and a to property with a very large long value (End-Of-Time, or EOT, which is a magic number – in this case Long.MAX_VALUE) that effectively means there is no current upper bound to the period associated with the relationship.

  • Spark
  • Clojure
    • Om Next – David Nolen presentation has rekindled my interested in ClojureScript.  Push Technologies offer a simple but FX bias demo of ClojureScript streaming prices – although not overly complex the demo does offer food for though with regards to Clojure in the electronic trading space. “Brandon Bloom – Building CircleCI’s Front end With Om” provide some insight into the challenges of an Om project.  Deployment to production is offered by lein-ring, IDE of choice is Cursive.  Clojure compiling to the JVM should avoid a degree of issues in the corporate space.

~ by mdavey on July 15, 2015.

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