Data Analysis with Python and PySpark, Jonathan Rioux

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.

Table of Contents

1 Introduction

PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK

2 Your first data program in PySpark

3 Submitting and scaling your first PySpark program

4 Analyzing tabular data with pyspark.sql

5 Data frame gymnastics: Joining and grouping

PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE

6 Multidimensional data frames: Using PySpark with JSON data

7 Bilingual PySpark: Blending Python and SQL code

8 Extending PySpark with Python: RDD and UDFs

9 Big data is just a lot of small data: Using pandas UDFs

10 Your data under a different lens: Window functions

11 Faster PySpark: Understanding Spark’s query planning

PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK

12 Setting the stage: Preparing features for machine learning

13 Robust machine learning with ML Pipelines

14 Building custom ML transformers and estimators

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