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Hadoop and Complex Data Processing Workflows with Cascading

Understanding the basic concepts behind MapReduce is not a very difficult task, but those using extensively MapReduce tasks inside Hadoop are already facing new challenges like:

  • how can you run multiple map and/or reduce phases in your data processing?
  • how can you better coordinate the data processing execution flow for more complex scenarios?
  • how can you perform additional work between map/reduce phases?

Addressing these new challenges is the goal of the ☞ Cascading project:

Cascading is a feature rich API for defining and executing complex, scale-free, and fault tolerant data processing workflows on a Hadoop cluster.

Christopher Curtin’s slides embedded below are offering a good overview of what can be achieved using Cascading (starting with slide 20).