Data Analysis with Python and Pyspark, Rioux Jonathan

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 PySparks 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 youve 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 Sparks core engine with a Python-based API. It helps simplify Sparks 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. Youll learn how to scale your processing capabilities across multiple machines while ingesting data from any source--whether thats Hadoop clusters, cloud data storage, or local data files. Once youve covered the fundamentals, youll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code.

Whats 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

About the reader
Written for data scientists and data engineers comfortable with Python.

About the author
As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts.

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