Advice on how to design and build your Apache Spark application for testability
Map Reduce can be used in jobs such as pattern-based searching, web access log stats, document clustering, web link-graph reversal, inverted index construction, term-vector per host, statistical machine translation and machine learning. Text indexing, search, and tokenization can also be accomplished with the Map Reduce program.
Map Reduce can also be used in different environments such as desktop grids, dynamic cloud environments, volunteer computing environments and mobile environments. Those who want to apply for Map Reduce jobs can educate themselves with the many tutorials available in the internet. Focus should be put on studying the input reader, map function, partition function, comparison function, reduce function and output writer components of the program. Hire Map Reduce Developers
Could you code a program TF-IDF on Python 2.x using MapReduce (with MRjob) and then it has to run on Hadoop? Input for TF-IDF algorithm: some .txt docs with different text some words for which need to calculate TF-IDF Output: word1@doc (TF-IDF), word2@doc (TF-IDF) etc