Andrea Fiore afiore

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

	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] {

	# 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',

	require 'rake/clean'

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

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