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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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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

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

TensorFrames – quick start


After all this preamble, let's jump start our use of TensorFrames with this quick start tutorial. You can download and use the full notebook within Databricks Community Edition at http://bit.ly/2hwGyuC.

You can also run this from the PySpark shell (or other Spark environments), like any other Spark package:

# The version we're using in this notebook
$SPARK_HOME/bin/pyspark --packages tjhunter:tensorframes:0.2.2-s_2.10  

# Or use the latest version 
$SPARK_HOME/bin/pyspark --packages databricks:tensorframes:0.2.3-s_2.10

Note, you will only use one of the above commands (that is, not both). For more information, please refer to the databricks/tensorframes GitHub repository (https://github.com/databricks/tensorframes).

Configuration and setup

Please follow the configuration and setup steps in the following order:

Launching a Spark cluster

Launch a Spark cluster using Spark 1.6 (Hadoop 1) and Scala 2.10. This has been tested with Spark 1.6, Spark 1.6.2, and Spark 1.6.3 ...

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