Azure MapReduce


                  Azure MapReduce


MapReduce is a programming model and data processing technique commonly used to generate large datasets in parallel across a distributed cluster. In the context of Azure, MapReduce can be implemented using Azure HDInsight. This fully managed cloud service makes it easy to process big data using open-source frameworks such as Hadoop, Spark, and HBase.

By utilizing Azure HDInsight, you can deploy and manage Hadoop clusters suitable for running MapReduce jobs. These jobs are typically written in languages like Java or Python, and they are divided into two main phases:

  1. Map Phase: In this phase, the input data is split into smaller chunks, and a separate mapper node processes each piece. Mappers generate a set of key-value pairs based on the input data.
  2. Reduce Phase: In this phase, the key-value pairs emitted by the mappers are grouped by key and passed to reducer nodes. The reducers then process the data and produce the final output.

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