Book : Learning Pyspark - Drabas, Tomasz

-Titulo Original : Learning Pyspark

-Fabricante :

Packt Publishing

-Descripcion Original:

Build data-intensive applications locally and deploy at scale using the combined capabilities of Python and Spark 2.0 Key Features Get up to speed with Spark 2.0 architecture and techniques for using Spark with Python Learn how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0 Develop and deploy efficient, scalable real-time Spark solutions Book Description 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, youll 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 learn Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively Build and interact with Spark DataFrames using Spark SQL Read, 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 cluster Who this book is for 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. Table of Contents Understanding Spark Resilient Distributed Datasets DataFrames Prepare Data for Modeling Introducing MLlib Introducing the ML Package GraphFrames TensorFrames Polyglot Persistence with Blaze Structured Streaming Packaging Spark Applications About the Author Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 13 years of experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting he gained while working on three continents: Europe, Australia, and North America. While in Australia, Tomasz has been working on his PhD in Operations Research with a focus on choice modeling and revenue management applications in the airline industry. At Microsoft, Tomasz works with big data on a daily basis, solving machine learning problems such as anomaly detection, churn prediction, and pattern recognition using Spark. Tomasz has also authored the Practical Data Analysis Cookbook published by Packt Publishing in 2016. Denny Lee Denny Lee is a Principal Program Manager at Microsoft for the Azure DocumentDB team Microsofts blazing fast, planet-scale managed document store service. He is a hands-on distributed systems and data science engineer with more than 18 years of experience developing Internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He has extensive experience of building greenfield teams as well as turnaround/ change catalyst. Prior to joining the Azure DocumentDB team, Denny worked as a Technology Evangelist at Databricks; he has been working with Apache Spark since 0.5. He was also the Senior Director of Data Sciences Engineering at Concur, and was on the incub