Typically, raw data cannot be used directly in data analytics applications. This may be because the application requires the input data to be in a certain format, or have numerical data type, or the data may contain noise and errors, and various other statistical constraints.
In a driver/executor distributed computing model, since the tasks are asynchronous and not communicating directly with each other, they are forced to use workarounds with shuffle files.
A protocol standard in parallel computing for efficiently passing messages between processes. MPI was finalized and recognized as a computing standard in 1994, based upon decades of parallel computing research and development.
Distributed computing: Each task runs on a single core, and all the tasks collectively are spread across many machines, to be executed in a concurrent manner via a driver/executor architecture.
Data is extracted from one or more sources, transformed into a homogenous format ready for analysis and then loaded into a target.
Python decorators allow modifying the behavior of functions and classes, without changing their code. Let’s start from a simple example and see where a decorator would fit.
A well-proven parallel computing communication technique used within MPI established in the 1980s whereby processes exchange information in an optimal way without introducing extra overhead and opportunities for failures
Per Wikipedia, "... a technique employed to achieve parallelism... Tasks are split up and run simultaneously on multiple processors with different input in order to obtain results faster.
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