eBook downloads, eBook resources & eBook authors
Bestselling author and renowned psychiatrist Dr. Ratey presents a groundbreaking and fascinating investigation into the transformative effects of exercise on the brain.
As the evil Nacht spreads his darkness across the valley, Tom and his friends, the Bone family, desperately try to find the Spark that will heal the Dreaming and save the world.
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions
Professor Peyton Wilde has an enviable life teaching sociology at an idyllic liberal arts college--yet she is troubled by a sense of fading inspiration. One day an invitation arrives. Peyton has been selected to attend a luxurious all-expense-paid seminar in Iceland, where participants, billed as some of the greatest thinkers in the world, will be charged with answering one perplexing question. Meeting her diverse teammates--two neuroscientists, a philosopher, a dance teacher, a collage artist, and a farmer--Peyton wonders what she could ever have to contribute. The ensuing journey of discovery will transform the characters' work, their biases, and themselves. This suspenseful novel shows that the answers you seek can be found in the most unlikely places. It can be read for pleasure, is a great choice for book clubs, and can be used as unique and inspiring reading in qualitative research and other courses in education, sociology, social work, psychology, and communication.
Perfect for readers of Song for a Whale and Counting by 7s, a neurodivergent girl campaigns for a memorial when she learns that her small Scottish town used to burn witches simply because they were different. "A must-read for students and adults alike." -School Library Journal, Starred Review Ever since Ms. Murphy told us about the witch trials that happened centuries ago right here in Juniper, I can’t stop thinking about them. Those people weren’t magic. They were like me. Different like me. I’m autistic. I see things that others do not. I hear sounds that they can ignore. And sometimes I feel things all at once. I think about the witches, with no one to speak for them. Not everyone in our small town understands. But if I keep trying, maybe someone will. I won’t let the witches be forgotten. Because there is more to their story. Just like there is more to mine. Award-winning and neurodivergent author Elle McNicoll delivers an insightful and stirring debut about the European witch trials and a girl who refuses to relent in the fight for what she knows is right.
From award-winning author Pablo Cartaya comes a deeply moving middle grade novel about a daughter and father finding their way back to each other in the face of their changing family and community. A SCHNEIDER FAMILY BOOK AWARD HONOR FOR MIDDLE GRADE Emilia Torres has a wandering mind. It's hard for her to follow along at school, and sometimes she forgets to do what her mom or abuela asks. But she remembers what matters: a time when her family was whole and home made sense. When Dad returns from deployment, Emilia expects that her life will get back to normal. Instead, it unravels. Dad shuts himself in the back stall of their family's auto shop to work on an old car. Emilia peeks in on him daily, mesmerized by his welder. One day, Dad calls Emilia over. Then, he teaches her how to weld. And over time, flickers of her old dad reappear. But as Emilia finds a way to repair the relationship with her father at home, her community ruptures with some of her classmates, like her best friend, Gus, at the center of the conflict. Each Tiny Spark by Pablo Cartaya is a tender story about asking big questions and being brave enough to reckon with the answers.
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages
Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data 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 step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug 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
