Data Analysis with Python and PySpark
Author: Jonathan Rioux(Author)
Publisher finelybook 出版社: Manning (March 15, 2022)
Language 语言: English
Print Length 页数: 425 pages
ISBN-10: 1617297208
ISBN-13: 9781617297205
Book Description
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.
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.
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 Author: 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
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