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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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 Drabas Drabas
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Drabas
 Lee Lee
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Lee
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Table of Contents (20) Chapters Close

Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications 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...

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