Tomasz Drabas (51 results)

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Digital. Condition: New. Combine the power of Apache Spark and Python to build effective big data applicationsAbout This Book. Perform effective data processing, machine learning, and analytics using PySpark. Overcome challenges in developing and deploying Spark solutions using Python. Explore recipes for efficiently combining Python and Apache Spark to process dataWho This Book Is ForThe PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.What You Will Learn. Configure a local instance of PySpark in a virtual environment . Install and configure Jupyter in local and multi-node environments. Create DataFrames from JSON and a dictionary using pyspark.sql. Explore regression and clustering models available in the ML module. Use DataFrames to transform data used for modeling. Connect to PubNub and perform aggregations on streamsIn DetailApache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.You'll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You'll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you'll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You'll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.Style and approachThis book is a rich collection of recipes that will come in handy when you are working with PySparkAddressing your common and not-so-common pain points, this is a book that you must have on the shelf.…

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Digital. Condition: New. Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0About This Book. Learn why and 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. Take your understanding of using Spark with Python to the next level with this jump start guideWho 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 firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.What You Will Learn. Learn about Apache Spark and the Spark 2.0 architecture. Build and interact with Spark DataFrames using Spark SQL. Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively. Read, transform, and understand data and use it to train machine learning models. Build machine learning models with MLlib and ML. Learn how to submit your applications programmatically using spark-submit. Deploy locally built applications to a clusterIn DetailApache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark.You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of 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 established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.Style and approachThis book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.…

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