35+ Hadoop Alternatives For Big Data

Disco

Disco can be broadly defined as an open-source and lightweight framework for distributed computing, which is based on the MapReduce paradigm. Due to Python, it is easy and powerful to use.

Misco

Misco can be broadly defined as a distributed computing framework, which has been specially designed for mobile devices. Highly portable, Misco is being 100% implemented in Python and should be able to run on any system, which supports Python.

Cloud MapReduce

Initially developed at Accenture Technology Labs, Cloud MapReduce can be broadly defined as MapReduce Implementation on Amazon Cloud OS. When compared with other open source implementation, it is a completely different architecture than others.

Bashreduce

Bashreduce offers the application of favorite UNIX tools across multiple cores or machines. There is no requirement for installation, administration or distributed file system. This is can a really good HDFS alternative.

Qizmt

MySpace Qizmt can be broadly defined as a map-reduce framework which is used for execution and development of distributed computation application of various Windows servers. This project also develops more open-source software for scalable, reliable and very easily distributed computation software.

HTTPMR

HTTPMR can be broadly defined as the implementation of Google’s famous Map or Reduce data processing models, which are done on a group of HTTP servers. This is primarily driven by the requirements of the Google AppEngine users.

Skynet

Skynet can be broadly defined as the open-source Ruby implementation of Google MapReduce framework. Skynet was created at Geni and anyone can use this

Sphere

Sphere can be broadly defined as the support for distributed storage of data, processing, and distribution over many clusters of commodity computers across multiple data centers or in a single data center. It can be further defined as scalable, high performance and secure distributed file system.

Riak

Riak can be defined as a distributed database which is open-source. It has been architected for storing data as well as serving the given requests quickly and predictably also in peak times.

Octopy

Octo.py can be broadly defined as fast-n-easy MapReduce implementation for Python, and it has been inspired by Starfish and Google MapReduce for Ruby. It offers a simple approach which is adaptable to a large proportion of the parallelizable tasks.

MapReduce

MPI-MR can be defined as a programming paradigm, which is used all over the world for processing large data sets in a parallel process. Popularized by Google, the best feature is that the user can perform processes in parallel without writing parallel code.

Filemap

Filemap is a file-based MapReduce system, which can perform parallel computation. It can be further defined as high=performance, lightweight and zero-install alternative.

Plasma MapReduce

Plasma MapReduce can be broadly defined as the distributed file system which used for large files and implemented in userspace. The main function of MapReduce is to run the famous algorithm scheme for rearranging as well as mapping large files.

Mapredus

Mapredus is a simple framework which assists you to download, install and use Ruby Software in the system. The software package is known as a gem and contains Ruby library or application.

Mincemeat

Mincemeat can be broadly defined as an open-source, secure, lightweight and tolerant framework, which contain the code in a singular Python file which depends upon Python standard library.

GPMR

GPMR can be broadly defined as the MapReduce library, which leverages the GPU cluster’s power for large scale computing.

Elastic Phoenix

Originally developed at Stanford University, Elastic Phoenix can be defined as the shared-memory implementation of any MapReduce parallel programming framework.

Preregrine

Preregrine can be broadly defined as the map-reduce framework, which has been specially designed for running iterative jobs across various data partitions. It has been specifically designed to be fast for the execution of the map reduces jobs.

R3

R3 can be broadly defined as the map reduce engine, which is written in Python by using a redis backend. The purpose of this software is to be very simple so that it is accessed and used easily by the programmer.

Ceph

Ceph offers seamless access to the objects which use native language’s binding or radosgw which is an REST interface and is compatible with the application written for Swift and S3.

QFS

QFS is a fault-tolerant, high-performance, distributed file system, which has been developed for effective support of MapReduce processing.

Cloud-Crowd

CloudCrowd has been especially used for making distributed processing very easy for Ruby programmers. There are many jobs, which can be easily done such as resizing or generation of images, the encoding of videos, migration of large database or file sets.

HPCC

HPCC or High High-Performance Computing cluster can be broadly defined as a massive open-source parallel-processing computing platform, which

Condor

This is an open-source program development platform whose goal is the development, implementation, deployment, and evaluation of the mechanisms and policies which support HTC on a large collection of computing resources.

Storm

Storm by Nathan Storm is a fully distributed real-time computation system which is similar to Hadoop and offers a set of general primitives for batch processing. It is very simple, which can be used with any programming language.

HaLoop

An offshoot of Hadoop to support iterative data HaLoop can be broadly defined as a modified version of the Hadoop MapReduce framework, which has been designed to serve various applications. HaLoop increases the efficiency of programming support by making the task scheduler loop aware.

MapRejuice

MapRejuice can be broadly defined as the distributed client-side computing implementation of the Map-Reduce which is built upon the top of Node.js.

GoCircuit

GoCircuit can be broadly defined as a programmable paradigm which offers support for development and sustenance of big data apps.

Spark

Apache Spark can be broadly defined as the general and fast engine for processing scale data. It has an advanced DAG execution engine which supports in-memory computing and cyclic data flow.

Stratosphere

This is a research system which offers the novel programming paradigm also known as PACT. This paradigm extends MapReduce with more functions as well as offers various methods designed to optimize complete parallel workflow.

Gridgain

GridGain In-Memory Data Fabric can be broadly defined as a proven software solution which delivers an unlimited scale as well as unprecedented speed for an acceleration of business and time.

MongoDB

MongoDB can be defined as a very popular tool which is used for cloud computing. This tool uses the map-reduce algorithm in a simple and elegant manner.

MARS

Mars can be broadly defined as an Nvidia MapReduce framework, which is very useful on GPUs or graphic processors.

Minceat

Minceat.py is the Python implementation of the MapReduce distributed computing framework. It offers a lightweight, fault-tolerant, secure and open-source solution to the customers.

Dato Core

Dato Core, can be broadly defined as the fast, scalable engine From GraphLabCreate which is open-source and secure Hadoop alternative.

HPCC

HPCC can be broadly defined as the High-Performance Computing cluster and a massive parallel-processing platform has been specially designed for a resolution of problems involving large data. The platform is open-source and can be used by anyone. The unique architecture, simple and powerful data programming languages make it a compelling solution.

MapReduce Lite

MapReduce Lite is also known as the C++ implementation of the MapReduce programming paradigm.

Gearman

Gearman offers a generic application framework which enables the user to farm out work to other processes or machines, which are suitable to do that particular work. It also allows the user to load the balance processing as well as to work in the parallel process.

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