Book Image

Apache Spark for Data Science Cookbook

By : Padma Priya Chitturi
Book Image

Apache Spark for Data Science Cookbook

By: Padma Priya Chitturi

Overview of this book

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
Table of Contents (17 chapters)
Apache Spark for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

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...