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Hands-On Big Data Analytics with PySpark

You're reading from  Hands-On Big Data Analytics with PySpark

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Pages 182 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Rudy Lai Rudy Lai
Profile icon Rudy Lai
Bartłomiej Potaczek Bartłomiej Potaczek
Profile icon Bartłomiej Potaczek
View More author details

Table of Contents (15) Chapters

Preface 1. Installing Pyspark and Setting up Your Development Environment 2. Getting Your Big Data into the Spark Environment Using RDDs 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

Summary

In this chapter, we first learned how to separate logic from the Spark engine. We then looked at a component that was well-tested in separation without the Spark engine, and we carried out integration testing using SparkSession. For this, we created a SparkSession test by reusing the component that was already well-tested. By doing that, we did not have to cover all edge cases in the integration test and our test was much faster. We then learned how to leverage partial functions to supply mocked data that's provided at the testing phase. We also covered ScalaCheck for property-based testing. By the end of this chapter, we had tested our code in different versions of Spark and learned how to change our DataFrame mock test to RDD.

In the next chapter, we will learn how to leverage the Spark GraphX API.

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