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

Simulating real-time data


In this recipe, we'll see how to simulate real-time data.

Getting ready

To step through this recipe, you will need Kafka and Zookeeper running on the cluster. Install Scala and Java.

How to do it…

  1. Since the data is available in files, let's simulate the data in real time using a producer which writes the data into Kafka. Here is the code:

          import java.util.{Date, Properties} 
          import kafka.javaapi.producer.Producer 
          import kafka.producer.KeyedMessage 
          import kafka.producer.ProducerConfig 
          import org.apache.spark.mllib.linalg.Vectors 
          import scala.io.{BufferedSource, Source} 
          import scala.util.Random 
       
          object KafkaProducer { 
             def main(args:Array[String]): Unit ={ 
               val random:Random = new Random 
               val props = new Properties 
           props.put("metadata.broker.list","172.22.128.16:9092") 
           props.put("serializer.class","kafka...