I manage a small team of developers and at any given time we have several on going (one-off) data projects that could be considered "Embarrassingly parallel" - These generally involve running a single script on a single computer for several days, a classic example would be processing several thousand PDF files to extract some key text and place into a CSV file for later insertion into a database.
We are now doing enough of these type of tasks that I started to investigate developing a simple job queue system using RabbitMQ with a few spare servers (with an eye to use Amazon SQS/S3/EC2 for projects that needed larger scaling)
In searching for examples of others doing this I keep coming across the classic Hadoop New York Times example:
The New York Times used 100 Amazon EC2 instances and a Hadoop application to process 4 TB of raw image TIFF data (stored in S3) into 11 million finished PDFs in the space of 24 hours at a computation cost of about $240 (not including bandwidth)
Which sounds perfect? So I researched Hadoop and Map/Reduce.
But what I can't work out is how they did it? Or why they did it?
Converting TIFF's in PDF's is not a Map/Reduce problem surely? Wouldn't a simple Job Queue have been better?
The other classic Hadoop example is the "wordcount" from the Yahoo Hadoop Tutorial seems a perfect fit for Map/Reduce, and I can see why it is such a powerful tool for Big Data.
I don't understand how these "Embarrassingly parallel" tasks are put into the Map/Reduce pattern?
TL;DR
This is very much a conceptual question, basically I want to know how would I fit a task of "processing several thousand PDF files to extract some key text and place into a CSV file" into a Map/Reduce pattern?
If you know of any examples that woul