Mastering Large Datasets: Parallelize and Distribute Your Python Code, John T. Wolohan (

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Programming techniques that work well on laptop-sized data can slow to a crawl--or fail altogether--when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change.

About the book
Mastering Large Datasets with Python teaches you to write code that can handle datasets of any size. Youll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. Youll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, youll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3.

Whats inside

• An introduction to the map and reduce paradigm
• Parallelization with the multiprocessing module and pathos framework
• Hadoop and Spark for distributed computing
• Running AWS jobs to process large datasets

About the reader
For Python programmers who need to work faster with more data.

About the author
J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington.

Table of Contents:

PART 1

1 ] Introduction

2 ] Accelerating large dataset work: Map and parallel computing

3 ] Function pipelines for mapping complex transformations

4 ] Processing large datasets with lazy workflows

5 ] Accumulation operations with reduce

6 ] Speeding up map and reduce with advanced parallelization

PART 2

7 ] Processing truly big datasets with Hadoop and Spark

8 ] Best practices for large data with Apache Streaming and mrjob

9 ] PageRank with map and reduce in PySpark

10 ] Faster decision-making with machine learning and PySpark

PART 3

11 ] Large datasets in the cloud with Amazon Web Services and S3