Niko's Project Corner

JVM at other sites

Analyzing NYC Taxi dataset with Elasticsearch and Kibana

(19th March 2017)

The NYC taxi­cab dataset has seen lots of love from many data sci­en­tists such as Todd W. Schei­der and Mark Litwintschik. I de­cided to give it a go while learn­ing Clo­jure, as I sus­pected that it might be a good lan­guage for ETL jobs. This ar­ti­cle de­scribes how I loaded the dataset, nor­mal­ized its con­ven­tions and columns, con­verted from CSV to JSON and stored them to Elas­tic­search.

Languages: Clojure
Tags: GitHub JVM Elasticsearch Databases Business Intelligence Kibana
GitHub: nikonyrh/nyc-taxi-data

Mustache templates in Clojure

(25th January 2017)

Mus­tache is a well-known tem­plate sys­tem with im­ple­men­ta­tions in most pop­ular lan­guages. At its core it is log­icless same tem­plates can be di­rectly used on other pro­jects. For ex­am­ple I am plan­ning to port this blgo en­gine from PHP to Clo­jure but I only need to re­place La­TeX pars­ing and HTML gen­er­ation parts, I should be able to use ex­ist­ing Mus­tache tem­plates with­out any mod­ifi­ca­tions. To learn Clo­jure pro­gram­ming I de­cided not to use the rec­om­mended li­brary but in­stead im­ple­ment my own.

Languages: Clojure
Tags: Blog GitHub JVM
GitHub: nikonyrh/mustache-clj

English hyphenation algorithm in Clojure

(17th August 2016)

This is noth­ing that spec­tac­ular (as if any­thing on my blog is), but I still wanted to de­scribe the out­line of the pro­ject of port­ing the hy­phen­ation al­go­rithm from PHP to Clo­jure. The im­ple­men­ta­tion is only about 80 lines of code + com­ments + 20 lines of unit tests. For com­par­ison the orig­inal PHP abom­ina­tion is about is about 160 LoCs, al­though it is a bit bloated by im­ple­ment­ing the pat­terns search via a trie data struc­ture in­stead of us­ing the str­pos func­tion.

Languages: Clojure
Tags: Hyphenation Blog GitHub JVM
GitHub: nikonyrh/hyphenator-clj

Scalable analytics with Docker, Spark and Python

(23rd December 2015)

Tra­di­tion­ally data sci­en­tists in­stalled soft­ware pack­ages di­rectly to their ma­chi­nes, wrote code, trained mod­els, saved re­sults to lo­cal files and ap­plied mod­els to new data in batch pro­cess­ing style. New data-driven prod­ucts re­quire rapid de­vel­op­ment of new mod­els, scal­able train­ing and easy in­te­gra­tion to other as­pects of the busi­ness. Here I am propos­ing one (per­haps al­ready well-known) cloud-ready ar­chi­tec­ture to meet these re­quire­ments.

Languages: Bash Python
Tags: Architecture Docker Spark Nginx GitHub JVM
GitHub: nikonyrh/docker-scripts