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
- ISBN10 1098103653
- ISBN13 9781098103651
- Publish Date 24 June 2022 (first published 14 June 2022)
- Publish Status Active
- Publish Country US
- Imprint O'Reilly Media
- Format Paperback
- Pages 275
- Language English