Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Length 182 pages
Edition 1st Edition
Languages
Tools
Concepts
Arrow right icon
Authors (3):
Arrow left icon
James Cross James Cross
Author Profile Icon James Cross
James Cross
Bartłomiej Potaczek Bartłomiej Potaczek
Author Profile Icon Bartłomiej Potaczek
Bartłomiej Potaczek
Rudy Lai Rudy Lai
Author Profile Icon Rudy Lai
Rudy Lai
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Installing Pyspark and Setting up Your Development Environment FREE CHAPTER 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 learned how to detect shuffle in a process. We covered testing operations that cause a shuffle in Apache Spark. We also learned how to employ partitioning in the RDD. It is important to know how to use the API if partitioned data is needed, because RDD is still widely used, so we use the keyBy operations to reduce shuffle. We also learned how to use the custom partitioner to reduce shuffle.

In the next chapter, we'll learn how to save data in the correct format using the Spark API.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime