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    <p><strong>Hadoop Alternatives</strong></p> <p>• <strong><a href="http://spark.incubator.apache.org/">Apache Spark</a></strong>- open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write.</p> <p>• <strong><a href="http://graphlab.org/">GraphLab</a></strong> - a redesigned fully distributed API, HDFS integration and a wide range of new machine learning toolkits.</p> <p>• <strong><a href="http://hpccsystems.com/">HPCC Systems</a></strong> - (High Performance Computing Cluster) is a massive parallel-processing computing platform that solves Big Data problems.</p> <p>• <strong><a href="http://research.microsoft.com/en-us/projects/Dryad/">Dryad</a></strong>- is investigating programming models for writing parallel and distributed programs to scale from a small cluster to a large data-center.</p> <p>• <strong><a href="http://flink.incubator.apache.org/">Apache Flink</a></strong> – open source distributed data processing platform. Distributed programs are represented as a DAG of operators (such as join, map, group, ..) </p> <p>• <strong><a href="http://storm-project.net/">Storm</a></strong> - is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language, and is a lot of fun to use!</p> <p>• <strong><a href="http://heynemann.github.io/r3/">R3</a></strong> - is a map reduce engine written in python using a redis backend.</p> <p>• <strong><a href="http://discoproject.org/">Disco</a></strong> - is a lightweight, open-source framework for distributed computing based on the MapReduce paradigm.</p> <p>• <strong><a href="http://mapreduce.stanford.edu/">Phoenix</a></strong> - is a shared-memory implementation of Google's MapReduce model for data-intensive processing tasks. </p> <p>• <strong><a href="http://plasma.camlcity.org/plasma/index.html">Plasma</a></strong> - PlasmaFS is a distributed filesystem for large files, implemented in user space. Plasma Map/Reduce runs the famous algorithm scheme for mapping and rearranging large files. Plasma KV is a key/value database on top of PlasmaFS</p> <p>• <strong><a href="http://peregrine_mapreduce.bitbucket.org/">Peregrine</a></strong> - is a map reduce framework designed for running iterative jobs across partitions of data. </p> <p>• <strong><a href="https://code.google.com/p/httpmr/">httpmr</a></strong> - A scalable data processing framework for people with web clusters.</p> <p>• <strong><a href="http://sector.sourceforge.net/">sector/sphere</a></strong> - sector is a high performance, scalable, and secure distributed file system. Sphere is a high performance parallel data processing engine that can process Sector data files on the storage nodes with very simple programming interfaces.</p> <p>• <strong><a href="http://mfisk.github.io/filemap/">Filemap</a></strong> - is a lightweight system for applying Unix-style file processing tools to large amounts of data stored in files.</p> <p>• <strong><a href="http://alumni.cs.ucr.edu/~jdou/misco/">misco</a></strong> - is a distributed computing framework designed for mobile devices</p> <p>• <strong><a href="http://mapreduce.sandia.gov/">MR-MPI</a></strong> – is a library, which is an open-source implementation of MapReduce written for distributed-memory parallel machines on top of standard MPI message passing</p> <p>• <strong><a href="http://www.gridgain.com/">GridGain</a></strong> – in-memory computing</p> <p>a comprehensive list - <strong><a href="http://azadparinda.wordpress.com/2013/10/11/projects-other-than-hadoop/">Projects other than Hadoop !</a></strong></p>
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    1. COVery nice and descriptive answer buddy ;)
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    2. COAFAIK, Phoenix works on top of HBase API and doesn't use MR.
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