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Learning PySpark
Learning PySpark

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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Learning PySpark

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Key benefits

  • Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0
  • Develop and deploy efficient, scalable real-time Spark solutions
  • Take your understanding of using Spark with Python to the next level with this jump start guide

Description

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.

Who is this book for?

If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.

What you will learn

  • Learn about Apache Spark and the Spark 2.0 architecture
  • Build and interact with Spark DataFrames using Spark SQL
  • Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
  • Read, transform, and understand data and use it to train machine learning models
  • Build machine learning models with MLlib and ML
  • Learn how to submit your applications programmatically using spark-submit
  • Deploy locally built applications to a cluster

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 27, 2017
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466259
Category :
Languages :

What do you get with eBook?

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Product Details

Publication date : Feb 27, 2017
Length: 274 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466259
Category :
Languages :

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Frequently bought together


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Total £ 115.97
Mastering Spark for Data Science
£45.99
Python Machine Learning, Second Edition
£32.99
Learning PySpark
£36.99
Total £ 115.97 Stars icon

Table of Contents

12 Chapters
1. Understanding Spark Chevron down icon Chevron up icon
2. Resilient Distributed Datasets Chevron down icon Chevron up icon
3. DataFrames Chevron down icon Chevron up icon
4. Prepare Data for Modeling Chevron down icon Chevron up icon
5. Introducing MLlib Chevron down icon Chevron up icon
6. Introducing the ML Package Chevron down icon Chevron up icon
7. GraphFrames Chevron down icon Chevron up icon
8. TensorFrames Chevron down icon Chevron up icon
9. Polyglot Persistence with Blaze Chevron down icon Chevron up icon
10. Structured Streaming Chevron down icon Chevron up icon
11. Packaging Spark Applications Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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(194 Ratings)
5 star 39.2%
4 star 32%
3 star 13.9%
2 star 7.2%
1 star 7.7%
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