How to: Distributed Computing
_Frameworks and libraries for Distributed Computing._

- Batch Processing
  - [dask](https://github.com/dask/dask) - A flexible parallel computing library for analytic computing.
  - [luigi](https://github.com/spotify/luigi) - A module that helps you build complex pipelines of batch jobs.
  - [mpi4py](https://github.com/mpi4py/mpi4py) - Python bindings for MPI.
  - [PySpark](https://github.com/apache/spark) - [Apache Spark](https://spark.apache.org/) Python API.
  - [Ray](https://github.com/ray-project/ray/) - A system for parallel and distributed Python that unifies the machine learning ecosystem.
- Stream Processing
  - [faust](https://github.com/robinhood/faust) - A stream processing library, porting the ideas from [Kafka Streams](https://kafka.apache.org/documentation/streams/) to Python.
  - [streamparse](https://github.com/Parsely/streamparse) - Run Python code against real-time streams of data via [Apache Storm](http://storm.apache.org/).