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Apache Spark for Data Science Cookbook

You're reading from   Apache Spark for Data Science Cookbook Solve real-world analytical problems

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Product type Paperback
Published in Dec 2016
Publisher
ISBN-13 9781785880100
Length 392 pages
Edition 1st Edition
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Authors (2):
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Padma Priya Chitturi Padma Priya Chitturi
Author Profile Icon Padma Priya Chitturi
Padma Priya Chitturi
Nagamallikarjuna Inelu Nagamallikarjuna Inelu
Author Profile Icon Nagamallikarjuna Inelu
Nagamallikarjuna Inelu
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Toc

Table of Contents (11) Chapters Close

Preface 1. Big Data Analytics with Spark 2. Tricky Statistics with Spark FREE CHAPTER 3. Data Analysis with Spark 4. Clustering, Classification, and Regression 5. Working with Spark MLlib 6. NLP with Spark 7. Working with Sparkling Water - H2O 8. Data Visualization with Spark 9. Deep Learning on Spark 10. Working with SparkR

Visualizing data on HDFS - parameterizing inputs


Once we start the service, we can point our browser to http://localhost:8080 (change the port as per your modified port configuration) to view the Zeppelin UI. Zeppelin organizes its contents as notes and paragraphs. A note is simply a list of all the paragraphs on a single web page.

Using data from HDFS simply means that we point to the HDFS location instead of the local file system location. Before we consume the file from HDFS, let's quickly check the Spark version that Zeppelin uses. This can be achieved by issuing sc.version on a paragraph. The sc variable is an implicit variable representing the SparkContext inside Zeppelin, which simply means that we need not programmatically create a SparkContext within Zeppelin:

sc.version 
res0: String = 1.6.0

Let's load the sample file profiles.json, convert it into a DataFrame, and print the schema and the first 20 rows (show) for verification. Let's also finally register the DataFrame as a...

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