The amount of data being generated today is staggering and growing.
Apache Spark has emerged as the de facto tool to analyze big data and is
now a critical part of the data science toolbox. Updated for Spark 3.0,
this practical guide brings together Spark, statistical methods, and
real-world datasets to teach you how to approach analytics problems
using PySpark, Spark's Python API, and other best practices in Spark
programming.
Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and
Josh Wills offer an introduction to the Spark ecosystem, then dive into
patterns that apply common techniques-including classification,
clustering, collaborative filtering, and anomaly detection, to fields
such as genomics, security, and finance. This updated edition also
covers NLP and image processing.
If you have a basic understanding of machine learning and statistics and
you program in Python, this book will get you started with large-scale
data analysis.
- Familiarize yourself with Spark's programming model and ecosystem
- Learn general approaches in data science
- Examine complete implementations that analyze large public datasets
- Discover which machine learning tools make sense for particular
problems
- Explore code that can be adapted to many uses