Bộ sách luyện thi ETS 2023 LC&RC
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Sách Data Analysis with Python and PySpark (Sách keo gáy, bìa mềm)
Categories:Computers - Programming
Year:2022
Language:english
Pages:425
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.
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
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.
