Learning PySpark

Build data-intensive applications locally and deploy at scale using the combined capabilities of Python and Spark 2.0 Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will demonstrate how you can leverage the power of Python and put it to use in the Spark ecosystem. You will start by understanding Spark 2.0 architecture and learning how to set up a Python environment for Spark. You will then get familiar with the modules available in PySpark such as MLib. The book will also guide you on how to abstract data with RDDs and DataFrames. In later chapters, you'll get up to speed with the streaming capabilities of PySpark. Toward the end, you will gain insights into the machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have a strong understanding of the Spark Python API and how it can be used to build data-intensive applications. If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A strong understanding of Python is expected to get the most out of this book. Familiarity with Spark will be useful, but is not mandatory.