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Big Data Analytics

You're reading from   Big Data Analytics Real time analytics using Apache Spark and Hadoop

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785884696
Length 326 pages
Edition 1st Edition
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Author (1):
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Venkat Ankam Venkat Ankam
Author Profile Icon Venkat Ankam
Venkat Ankam
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Table of Contents (12) Chapters Close

Preface 1. Big Data Analytics at a 10,000-Foot View 2. Getting Started with Apache Hadoop and Apache Spark FREE CHAPTER 3. Deep Dive into Apache Spark 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets 5. Real-Time Analytics with Spark Streaming and Structured Streaming 6. Notebooks and Dataflows with Spark and Hadoop 7. Machine Learning with Spark and Hadoop 8. Building Recommendation Systems with Spark and Mahout 9. Graph Analytics with GraphX 10. Interactive Analytics with SparkR Index

Analyzing flight data using GraphX


Let's analyze flight data by representing the airports as vertices and routes as edges. Let's do some basic graph analytics to find out departures and arrivals and also analyze the data with the Pregel API to find out the cheapest fares. Download the flight data from the following location:

http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time

The steps to analyze the data are as follows:

  1. Select OriginAirportID, Origin, DestAirportID, Dest, and Distance then click Download. Copy the ZIP file onto the cluster, unzip it, and then copy the contents to HDFS:

    unzip 355968671_T_ONTIME.zip
    
    hadoop fs -put 355968671_T_ONTIME.csv
    
  2. Get into the Scala shell using the spark-shell command and then import all dependencies, as follows:

    scala> import org.apache.spark.graphx._
    scala> import org.apache.spark.rdd.RDD
    
  3. Define a Scala case class for the flight schema corresponding to the CSV data file:

    scala> case class Flight(org_id...
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