kdrakon

import java.util

import com.sksamuel.avro4s.RecordFormat
import org.apache.avro.generic.GenericRecord
import org.apache.kafka.common.serialization.{Deserializer, Serde, Serializer}

object Avro4s {
  implicit class CaseClassSerde(inner: Serde[GenericRecord]) {
    def forCaseClass[T](implicit recordFormat: RecordFormat[T]): Serde[T] = {
      val caseClassSerializer: Serializer[T] = new Serializer[T] {

corey

A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, with an eye towards real-world deployments. I figured I'd share the list. While it's biased and rather incomplete but maybe of interest to someone. While many are obvious choices (I've omitted several, like MapReduce), I think there are a few underappreciated gems.

###Dataflow Engines:

Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf

Spark--in memory dataflow
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf

mudge

# rvm_server/manifests/init.pp

class rvm_server($version='latest') {

  # Dependencies for RVM and Ruby.
  package { 'rvm-dependencies':
    ensure => installed,
    name   => ['bash', 'gawk', 'sed', 'grep', 'which', 'coreutils', 'tar',
               'curl', 'gzip', 'bzip2', 'gcc-c++', 'autoconf', 'patch',
               'readline', 'readline-devel', 'zlib', 'zlib-devel',

mbostock

license: gpl-3.0

andreyvit

require 'rake/clean'

HAML   = FileList['**/*.haml']
LESS   = FileList['**/*.less']
COFFEE = FileList['**/*.coffee']

HTML = HAML.ext('html')
CSS  = LESS.ext('css')
JS   = COFFEE.ext('js')