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This video gives a general overview of the Dask project. What is Dask? Dask is a flexible library for parallel computing in Python. Dask is composed of two parts: 1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. “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 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 laptop in a single process Responsive: Designed with interactive computing in mind, it provides rapid feedback and diagnostics to aid humans Share your feedback with us in the comments and let us know: Did you find the video helpful? Have you used Dask before? Learn more at dask.org KEY MOMENTS 00:00 - Intro 00:08 - What does Dask do? 01:08 - Dask Array 01:43 - Where is Dask used? 02:58 - Examples of application 05:46 - How Does Dask Work? 06:15 - Where is Dask run? 00:06:48 Dask Open Source Community