The Spark Method Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of The Spark Method book. This book definitely worth reading, it is an incredibly well-written.
You can't throw a logo-stamped pen without hitting someone's business book today. Every emerging leader and high-performer needs one to demonstrate their credibility, reach a bigger audience, and connect with the media, but let's face it, you're are busy. And you probably don't know how to write a book. Even worse, many people don't consider themselves to be creative.So how can you write a business book fast without the stress and hassle of DIY? It's simple when you use Cori Wamsley's SPARK Method! In The SPARK Method: How to Write a Book for Your Business Fast, Cori details every step of the writing process, from planning through writing through figuring out the title and cover. She makes a nebulous process incredibly easy for anyone to implement. Even if you have tons of ideas. Even if you lack direction. Even if you don't even know what an ISBN is . . . The SPARK Method can help you get through the writing process so you, too, can finally have a book to represent you, your ideas, your brand, and your story. Bonus downloads from Cori's website enrich the process, giving added value to this fantastic book. Buy this book, and get started on one amazing evergreen marketing tool that will help you make a bigger impact than ever before!
Kristine Barnett’s son Jacob has an IQ higher than Einstein’s, a photographic memory, and he taught himself calculus in two weeks. At nine he started working on an original theory in astrophysics that experts believe may someday put him in line for a Nobel Prize, and at age twelve he became a paid researcher in quantum physics. But the story of Kristine’s journey with Jake is all the more remarkable because his extraordinary mind was almost lost to autism. At age two, when Jake was diagnosed, Kristine was told he might never be able to tie his own shoes. The Spark is a remarkable memoir of mother and son. Surrounded by “experts” at home and in special ed who tried to focus on Jake’s most basic skills and curtail his distracting interests—moving shadows on the wall, stars, plaid patterns on sofa fabric—Jake made no progress, withdrew more and more into his own world, and eventually stopped talking completely. Kristine knew in her heart that she had to make a change. Against the advice of her husband, Michael, and the developmental specialists, Kristine followed her instincts, pulled Jake out of special ed, and began preparing him for mainstream kindergarten on her own. Relying on the insights she developed at the daycare center she runs out of the garage in her home, Kristine resolved to follow Jacob’s “spark”—his passionate interests. Why concentrate on what he couldn’t do? Why not focus on what he could? This basic philosophy, along with her belief in the power of ordinary childhood experiences (softball, picnics, s’mores around the campfire) and the importance of play, helped Kristine overcome huge odds. The Barnetts were not wealthy people, and in addition to financial hardship, Kristine herself faced serious health issues. But through hard work and determination on behalf of Jake and his two younger brothers, as well as an undying faith in their community, friends, and family, Kristine and Michael prevailed. The results were beyond anything anyone could have imagined. Dramatic, inspiring, and transformative, The Spark is about the power of love and courage in the face of overwhelming obstacles, and the dazzling possibilities that can occur when we learn how to tap the true potential that lies within every child, and in all of us. Praise for The Spark “[An] amazing memoir . . . compulsive reading.”—The Washington Post “The Spark is about the transformative power of unconditional love. If you have a child who’s ‘different’—and who doesn’t?—you won’t be able to put it down.”—Sylvia Nasar, author of A Beautiful Mind “Love, illness, faith, tragedy and triumph—it’s all here. . . . Jake Barnett’s story contains wisdom for every parent.”—Newsday “This eloquent memoir about an extraordinary boy and a resilient and remarkable mother will be of interest to every parent and/or educator hoping to nurture a child’s authentic ‘spark.’”—Publishers Weekly “Compelling . . . Jake is unusual, but so is his superhuman mom.”—Booklist “The Spark describes in glowing terms the profound intensity with which a mother can love her child.”—Andrew Solomon, author of The Noonday Demon and Far from the Tree “Every parent and teacher should read this fabulous book!”—Temple Grandin, author of Thinking in Pictures and co-author of The Autistic Brain
Learning Spark by Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia Pdf
Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables
Learning Spark by Jules S. Damji,Brooke Wenig,Tathagata Das,Denny Lee Pdf
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
Direct-current Arc and Alternating-current Spark Emission Spectrographic Field Methods for the Semiquantitative Analysis of Geologic Materials by David J. Grimes,A. P. Marranzino Pdf
Natural Language Processing with Spark NLP by Alex Thomas Pdf
If you want to build an enterprise-quality application that uses natural language text but aren’t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library. Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. You’ll also explore special concerns for developing text-based applications, such as performance. In four sections, you’ll learn NLP basics and building blocks before diving into application and system building: Basics: Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learning Building blocks: Learn techniques for building NLP applications—including tokenization, sentence segmentation, and named-entity recognition—and discover how and why they work Applications: Explore the design, development, and experimentation process for building your own NLP applications Building NLP systems: Consider options for productionizing and deploying NLP models, including which human languages to support
Spark: The Definitive Guide by Bill Chambers,Matei Zaharia Pdf
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
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.
Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API About This Book Learn about the design and implementation of streaming applications, machine learning pipelines, deep learning, and large-scale graph processing applications using Spark SQL APIs and Scala. Learn data exploration, data munging, and how to process structured and semi-structured data using real-world datasets and gain hands-on exposure to the issues and challenges of working with noisy and "dirty" real-world data. Understand design considerations for scalability and performance in web-scale Spark application architectures. Who This Book Is For If you are a developer, engineer, or an architect and want to learn how to use Apache Spark in a web-scale project, then this is the book for you. It is assumed that you have prior knowledge of SQL querying. A basic programming knowledge with Scala, Java, R, or Python is all you need to get started with this book. What You Will Learn Familiarize yourself with Spark SQL programming, including working with DataFrame/Dataset API and SQL Perform a series of hands-on exercises with different types of data sources, including CSV, JSON, Avro, MySQL, and MongoDB Perform data quality checks, data visualization, and basic statistical analysis tasks Perform data munging tasks on publically available datasets Learn how to use Spark SQL and Apache Kafka to build streaming applications Learn key performance-tuning tips and tricks in Spark SQL applications Learn key architectural components and patterns in large-scale Spark SQL applications In Detail In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project. Style and approach This book is a hands-on guide to designing, building, and deploying Spark SQL-centric production applications at scale.
The extraordinary memoir of a mother's love, commitment and nurturing, which allowed her son, originally diagnosed with severe autism, to flourish into a universally recognized genius--and how any parent can help their child find their spark. Today, at 13, Jacob is a paid researcher in quantum physics, working on extending Einstein's theory of relativity. Diagnosed at 1 with severe autism, at 3 he was assigned to life-skills classes and his parents were told to adjust their expectations. The goal: tying his own shoes at 16. Kristine's belief in the power of hope and the dazzling possibilities that can occur when we keep our minds open and learn to fuel a child's true potential changed everything.
Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Foreword by Rob Thomas. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment
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 Description Apache 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 for The 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.
Mastering Spark with R by Javier Luraschi,Kevin Kuo,Edgar Ruiz Pdf
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
