Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Why do we need Spark Streaming?


As noted by Tathagata Das – committer and member of the project management committee (PMC) to the Apache Spark project and lead developer of Spark Streaming – in the Datanami article Spark Streaming: What is It and Who's Using it (https://www.datanami.com/2015/11/30/spark-streaming-what-is-it-and-whos-using-it/), there is a business need for streaming. With the prevalence of online transactions and social media, as well as sensors and devices, companies are generating and processing more data at a faster rate.

The ability to develop actionable insight at scale and in real time provides those businesses with a competitive advantage. Whether you are detecting fraudulent transactions, providing real-time detection of sensor anomalies, or reacting to the next viral tweet, streaming analytics is becoming increasingly important in data scientists' and data engineer's toolbox.

The reason Spark Streaming is itself being rapidly adopted is because Apache Spark unifies...