Data is getting bigger, arriving faster, and coming in varied formats-and it all needs to be processed at scale for analytics or machine learning. How can you process such varied data workloads efficiently? Enter Apache Spark. Updated to emphasize new features in Spark 2.4., this second edition shows data engineers and scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine-learning algorithms. Through discourse, code snippets, and notebooks, you'll be able to: Learn Python, SQL, Scala, or Java high-level APIs: DataFrames and Datasets Peek under the hood of the Spark SQL engine to understand Spark transformations and performance Inspect, tune, and debug your Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow Use open source Pandas framework Koalas and Spark for data transformation and feature engineering

A landmark collection from one of Canada's literary icons, and the founder of House of Anansi Press, Heart Residence collects for the first time work from all corners of Dennis Lee's extraordinary career. This book is an exhilarating revelation. No other poet in Canada has the depth and range of Dennis Lee - jazzman, jester, and metaphysician, hardball political thinker and passionate lover, he has been publishing poems for fifty years. His first book, Kingdom of Absence, published in 1967, was the founding publication of House of Anansi Press. Since then Lee has produced work across the poetic spectrum, from nursery rhymes and skipping songs to uncompromising moral introspection to full-tilt love songs, plangent psalms, and ecstatic, solitary prayer. There are poets' poets and people's poets. And then there are those few who are neither and both: the few who become, over time, part of the warp and weft of their culture. Heart Residence collects, for the first time, work from all corners of this extraordinary career. In its verve and variety, this is a one-of-a-kind collection.

The 12th and final adventure in the legendary Hardman series It's doing the small, simple favors that gets Jim Hardman in the most trouble. A waitress he knows asks him to find her missing husband, a freelance bartender who never returned from a weekend party that he was working. Hardman figures it's a two-day job that will lead to a tawdry end. Instead, Hardman and his buddy Hump, the ex-NFL star, get sucked into a scheme involving a hundred grand in stolen jewelry and two corpses...and counting. "Hardman spoke directly to me. If I had a spirit guide with the Hap & Leonard novels, it was Ralph Dennis. I wish you the joy I got from first reading these novels." Joe R. Lansdale, New York Times bestselling author "Everything one expects from a private eye novel: exceptional characterization, strong and vigorous prose, and a glimpse into a place and time that has long since disappeared." Mystery Scene Magazine "Ralph Dennis has mastered the genre and supplied top entertainment." The New York Times "Among the best book series around." Philadelphia Daily News "A lightning-paced crime story packed with irreverence and loads of action." Publishers Weekly "By far the best of the action-adventure series." Mother Jones Magazine This new edition includes an afterword by Lee Goldberg, the #1 New York Times bestselling author of True Fiction

Combine the power of Apache Spark and Python to build effective big data applications Key Features 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 data Book DescriptionApache 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. 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 streams Who 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.

At once deeply personal and yet universal, the poet's reflections, musings, and chronicles of life from birth to death impart a plethora of emotions, from tenderness to outrage, but also an intellectual grasp and appreciation of the astronomically low odds of being born at all. His poems both celebrate and commiserate, embrace and embroil, tantalize and deny, but, always and in all ways, depict what it means to be human.

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About 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 guide 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 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 cluster In Detail Apache 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 approach This 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.