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.
publication date
2019-03-29
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
key features
Work with large amounts of agile data using distributed datasets and in-memory caching * Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3 * Employ the easy-to-use PySpark API to deploy big data Analytics for production
approach
This hands-on book is divided into bite-size chunks so you can learn at your own pace and focus on the areas that interest you the most. It is practical and packed with step-by-step instructions, working examples, and helpful advice from our expert authors. You will learn how PySpark provides an easy to use, performant way to do data analysis with Big Data.
audience
This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.
short description
In this book, you'll learn to implement some practical and proven techniques to improve aspects of programming and administration in Apache Spark. Techniques are demonstrated using practical examples and best practices. You will also learn how to use Spark and its Python API to create performant analytics with large-scale data.
subtitle
Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs
keywords
PySpark, Business Intelligence, Big data, Apache Spark, RDD, Data Frame, Spark API, Spark Architecture, Spark streaming
Product ISBN
9781838644130
