In this book, subtitled "Parallelize and Distribute Your Python Code", author J.T. Wolohan shows how to take a small project and scale it up using a functionally influenced approach to Python coding. The book explores methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The book explores tools like Hadoop and PySpark to efficiently process massive distributed datasets, speeding up decision-making with machine learning, and simplifying data storage with AWS S3

<ASIN:1617296236>

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Author: J.T. Wolohan
Publisher: Manning
Date: January 2020
Pages: 350
ISBN: 978-1617296239
Print: 1617296236
Audience: Python developers
Level: Intermedaite/Advanced
Category: Python

Topics include:

• An introduction to the map and reduce paradigm
• Parallelization with the multiprocessing module and pathos framework
• Hadoop and Spark for distributed computing
• Running AWS jobs to process large datasets

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