Build data-intensive applications locally and deploy at scale using the combined capabilities of Python and Spark 2.0Key FeaturesGet up to speed with Spark 2.0 architecture and techniques for using Spark with PythonLearn how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0Develop and deploy efficient, scalable real-time Spark solutionsBook DescriptionApache 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.What you will learnLearn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively Build and interact with Spark DataFrames using Spark SQLRead, transform, and understand data and use it to train machine learning models Develop machine learning models with MLlib Learn to submit your applications programmatically using spark-submit Deploy locally built applications to a clusterWho this book is forIf 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.Table of ContentsUnderstanding SparkResilient Distributed DatasetsDataFramesPrepare Data for ModelingIntroducing MLlib Introducing the ML PackageGraphFrames TensorFramesPolyglot Persistence with Blaze Structured Streaming Packaging Spark Applications
