July 2023
Intermediate to advanced
223 pages
5h 24m
English
Content preview from Scaling Python with Dask
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Chapter 1. What Is Dask?
Dask is a framework for parallelized computing with Python that scales from multiple cores on one machine to data centers with thousands of machines. It has both low-level task APIs and higher-level data-focused APIs. The low-level task APIs power Dask’s integration with a wide variety of Python libraries. Having public APIs has allowed an ecosystem of tools to grow around Dask for various use cases.
Continuum Analytics, now known as Anaconda Inc, started the open source, DARPA-funded Blaze project, which has evolved into Dask. Continuum has participated in developing many essential libraries and even conferences in the Python data analytics space. Dask remains an open source project, with much of its development now being supported by Coiled.
Dask is unique in the distributed computing ecosystem, because it integrates popular data science, parallel, and scientific computing libraries. Dask’s integration of different libraries allows developers to reuse much of their existing knowledge at scale. They can also frequently reuse some of their code with minimal changes.
Why Do You Need Dask?
Dask simplifies scaling analytics, ML, and other code written in Python,1 allowing you to handle larger and more complex data and problems. Dask aims to fill the space where your existing tools, like pandas DataFrames, or your scikit-learn machine learning pipelines start to become too slow (or do not succeed). While the term “big data” is perhaps less in vogue now than ...
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