Disco is an implementation of mapreduce for distributed computing. Disco supports parallel computations over large data sets, stored on an unreliable cluster of computers, as in the original framework created by Google. This makes it a perfect tool for analyzing and processing large data sets, without having to worry about difficult technicalities related to distribution such as communication protocols, load balancing, locking, job scheduling, and fault tolerance, which are handled by Disco.
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Apache Hadoop is a open source software framework that supports data-intensive distributed applications licensed under the Apache v2 license. It enables applications to...
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Apache Spark™ is a fast and general engine for large-scale data processing. Speed Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on...
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Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams.
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