Distributed Processing

Distributed processing is a phrase used to refer to a variety of computer systems that use more than one computer (or processor) to run an application. This includes parallel processing in which a single computer uses more than one CPU to execute programs.

Distributed Processing

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Apache Giraph

Apache Giraph is an iterative graph processing system built for high scalability. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale.

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Apache Spark

Apache Spark is an open-source cluster computing framework. Spark uses in-memory primitives which makes performance up to 100 times faster in contrast to Hadoop's two-stage disk-based MapReduce paradigm.Spark is a real time large data processing system that can use primary memory very effectively.

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Hadoop Map Reduce

Hadoop MapReduce is a software framework for distributed processing of large data sets on compute clusters of commodity hardware. MapReduce takes care of scheduling tasks, monitoring them and re-executing any failed tasks.Main feature of MapReduce is the Batch processing of large volumes of data on secondary storage.

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Akka Actors

The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It frees developers from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems.