High Performance Hadoop with Anaconda and Dask on Your Cluster | Anaconda
Dask is a flexible open source parallel computation framework that lets you comfortably scale up and scale out your analytics. If you’re running into memory issues, storage limitations, or CPU boundaries on a single machine when using Pandas, NumPy, or other computations with Python, Dask can help you scale up on all of the cores on a single machine, or scale out on all of the cores and memory acr
Introducing Dask distributed
This work is supported by Continuum Analytics and the XDATA Program as part of the Blaze Project tl;dr: We analyze JSON data on a cluster using pure Python projects. Dask, a Python library for parallel computing, now works on clusters. During the past few months I and others have extended dask with a new distributed memory scheduler. This enables dask’s existing parallel algorithms to scale across
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Fast Message Serialization
Scale up and Scale Out Anaconda and PyData