The conclusion to this series covers Combiner-like aggregation functionality, counters, partitioning, and serialization. Apache Spark is rising in popularity as an alternative to MapReduce, in a large part due to its expressive API for complex data processing. A few months ago, my colleague, Sean Owen wrote a post describing how to translate functionality from MapReduce into Spark, and in this post, I'll extend that conversation to cover additional functionality. Read more The post How-to: Translate from MapReduce to Apache Spark (Part 2) appeared first on Cloudera Engineering Blog.
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