Data Analysis with Python and PySpark
- Paperback: 456 pages
- Publisher: WOW! eBook (March 15, 2022)
- Language: English
- ISBN-10: 1617297208
- ISBN-13: 978-1617297205
Data Analysis with Python and PySpark: 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.
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.
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 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.
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