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    3. Data Analysis with Python and PySpark

    Data Analysis with Python and PySpark

    Posted By: yoyoloit
    Date: 16 Mar 2022 15:29:53
    Data Analysis with Python and PySpark

    Data Analysis with Python and PySpark
    by Jonathan Rioux
    English | 2022 | ISBN: ‎ 1617297208 | 456 pages | True EPUB, MOBI | 17.97 MB

    Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.

    In Data Analysis with Python and PySpark you will learn how to:

    Manage your data as it scales across multiple machines
    Scale up your data programs with full confidence
    Read and write data to and from a variety of sources and formats
    Deal with messy data with PySpark’s data manipulation functionality
    Discover new data sets and perform exploratory data analysis
    Build automated data pipelines that transform, summarize, and get insights from data
    Troubleshoot common PySpark errors
    Creating reliable long-running jobs

    Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required.

    Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

    About the technology
    The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem.

    About the book
    Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code.

    What's inside

    Organizing your PySpark code
    Managing your data, no matter the size
    Scale up your data programs with full confidence
    Troubleshooting common data pipeline problems
    Creating reliable long-running jobs
    UPWARDS