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Hands-On Big Data Analytics with PySpark

You're reading from   Hands-On Big Data Analytics with PySpark Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Length 182 pages
Edition 1st Edition
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Authors (3):
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James Cross James Cross
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James Cross
Bartłomiej Potaczek Bartłomiej Potaczek
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Bartłomiej Potaczek
Rudy Lai Rudy Lai
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Rudy Lai
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Table of Contents (15) Chapters Close

Preface 1. Installing Pyspark and Setting up Your Development Environment 2. Getting Your Big Data into the Spark Environment Using RDDs FREE CHAPTER 3. Big Data Cleaning and Wrangling with Spark Notebooks 4. Aggregating and Summarizing Data into Useful Reports 5. Powerful Exploratory Data Analysis with MLlib 6. Putting Structure on Your Big Data with SparkSQL 7. Transformations and Actions 8. Immutable Design 9. Avoiding Shuffle and Reducing Operational Expenses 10. Saving Data in the Correct Format 11. Working with the Spark Key/Value API 12. Testing Apache Spark Jobs 13. Leveraging the Spark GraphX API 14. Other Books You May Enjoy

Tabular formats – CSV

In this section, we will be covering text data, but in a tabular format—CSV. The following topics will be covered:

  • Saving data in CSV format
  • Loading CSV data
  • Testing

Saving CSV files is even more involved than JSON and plain text because we need to specify whether we want to retain headers of our data in our CSV file.

First, we will create a DataFrame:

test("should save and load CSV with header") {
//given
import spark.sqlContext.implicits._
val rdd = spark.sparkContext
.makeRDD(List(UserTransaction("a", 100), UserTransaction("b", 200)))
.toDF()

Then, we will use the write format CSV. We also need to specify that we don't want to include the header option in it:

//when
rdd.coalesce(1)
.write
.format("csv")
.option("header", "false")
.save(FileName)

We will then perform...

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