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Hadoop MapReduce

Working with Hadoop's MapReduce framework for processing large datasets in parallel across a distributed cluster, including setting up jobs, coding mappers and reducers, handling input and output formats, implementing data shuffling and partitioning, and debugging MapReduce applications.

Setting up MapReduce Jobs Coding Mappers and Reducers Handling Output Formats and Input Formats Handling Serialization Shuffle Partitioner Shuffle Comparable Shuffle Combiner Implementing Join Operation Leveraging Distributed Cache in Jobs
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