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# Dask

Dask is a flexible parallel computing library for analytic computing.
Dask is composed of two parts:

* Dynamic task scheduling optimized for computation. This is similar to 
  Airflow, Luigi, Celery, or Make, but optimized for interactive 
  computational workloads.
* “Big Data” collections like parallel arrays, dataframes, and lists that 
  extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

Dask emphasizes the following virtues:

* Familiar: Provides parallelized NumPy array and Pandas DataFrame objects
* Flexible: Provides a task scheduling interface for more custom workloads and integration with other projects.
* Native: Enables distributed computing in Pure Python with access to the PyData stack.
* Fast: Operates with low overhead, low latency, and minimal serialization necessary for fast numerical algorithms
* Scales up: Runs resiliently on clusters with 1000s of cores
* Scales down: Trivial to set up and run on a