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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
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Authors (2):
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Tomasz Drabas Tomasz Drabas
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Tomasz Drabas
Denny Lee Denny Lee
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Denny Lee
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Toc

Table of Contents (9) Chapters Close

Preface 1. Installing and Configuring Spark FREE CHAPTER 2. Abstracting Data with RDDs 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark

Creating a temporary table

DataFrames can easily be manipulated with SQL queries in Spark.

In this recipe, we will learn how to create a temporary view so you can access the data within DataFrame using SQL.

Getting ready

To execute this recipe, you need to have a working Spark 2.3 environment. You should have gone through the previous recipe, as we will be using the sample_data_schema DataFrame we created there.

There are no other requirements.

How to do it...

We simply use the .createTempView(...) method of a DataFrame:

sample_data_schema.createTempView('sample_data_view...
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