Hands-On Big Data Analytics with PySpark: Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

Hands-On Big Data Analytics with PySpark: Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobsReviews
Author: Rudy Lai
Pub Date: 2019
ISBN: 978-1838644130
Pages: 182
Language: English
Format: EPUB
Size: 10 Mb

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Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs
Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.
You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.
By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
What you will learn

  • Get practical big data experience while working on messy datasets
  • Analyze patterns with Spark SQL to improve your business intelligence
  • Use PySpark’s interactive shell to speed up development time
  • Create highly concurrent Spark programs by leveraging immutability
  • Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation
  • Re-design your jobs to use reduceByKey instead of groupBy
  • Create robust processing pipelines by testing Apache Spark jobs