Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code
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Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code
Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code
Elementas #: 52844593

Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code

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Elementas #: 52844593

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Learn how to parallelize and distribute Python code for large datasets. Get the first edition of Mastering Large Datasets with Python at Ubuy Lithuania.
  • SummaryModern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You'll explore 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 abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyProgramming techniques that work well on laptop-sized data can slow to a crawl--or fail altogether--when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change. About the bookMastering Large Datasets with Python teaches you to write code that can handle datasets of any size. You'll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. You'll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, you'll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3. What's inside An introduction to the map and reduce paradigmParallelization with the multiprocessing module and pathos frameworkHadoop and Spark for distributed computingRunning AWS jobs to process large datasets
Publisher Manning
Publication date January 21, 2020
Edition First Edition
Language English
Print length 312 pages
ISBN-10 1617296236
ISBN-13 978-1617296239
Item Weight 1.15 pounds (520 grams)
Dimensions 7.38 x 0.7 x 9.25 inches (18.7 x 1.8 x 23.5 cm)

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Mastering Large Datasets with Python: Parallelize and Distribute Your Python Code

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