Pydoop vs Mrjob for image processing on Hadoop
I would actually suggest pyspark because it natively supports binary files.
For image processing, you can try TensorFlowOnSpark
I want to process images(most probably in big sizes) on Hadoop platform but I am confused about which one to choose from the aforementioned 2 interfaces, especially for someone who is still a beginner in Hadoop. Considering the need to split the images into blocks to distribute processing among working machines and merge the blocks after processing is completed.
It's known that Pydoop has better access to the Hadoop API while mrjob has powerful utilities for executing the jobs, which one is suitable to be used with this kind of work?