Kubernetes-native platform to run massively parallel data/streaming jobs

MapReduce-MPI Library

Comparative Big Data Processing with Hadoop and Spark on User Query Logs

K-means clustering algorithm using MapReduce.

Efficient transducers for Julia

Fast & furious GroupBy operations for dask.array

Terraform module to provision an Elastic MapReduce (EMR) cluster on AWS using a Spotinst AWS MrScaler resource

Web Application Message Async Server and WAMP/MQTT bridge

Parallelized Base functions

A Java Stochastic Dynamic Programming Library

Fast, efficient, and scalable distributed map/reduce system, DAG execution, in memory or on disk, written in pure Go, runs standalone or distributedly.

Slice and map utilities based on generics and additional collection implementations like ordered map, set

Iterable Java8 style Streams for Python

This project implements a distributed K-means clustering algorithm using a custom-built MapReduce framework. It is designed to handle potentially large datasets by distributing the clustering workload across multiple processes or machines. Uses gRPC for the communication between mapper, reducer, master

Perform a single-pass map-reduce operation against each element in an array while iterating from right to left and return the accumulated result.

Perform a single-pass map-reduce operation against each element in an array and return the accumulated result.

A small hobby implementation of MapReduce that I hacked together at 2am.

MIT Distributed Systems CS 6.824/CS 6.5840

I have 5 Million articles of Wikipedia Articles , i have preprocessed my data using pandas , after that i have used Hadoop map reduce technology to make a search engine.

Distributed computing for data science tasks, executed on a Ubuntu server.