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Author: Rishi Yadav Publisher: Packt Publishing Ltd ISBN: 1783987073 Category : Computers Languages : en Pages : 468
Book Description
By introducing in-memory persistent storage, Apache Spark eliminates the need to store intermediate data in filesystems, thereby increasing processing speed by up to 100 times. This book will focus on how to analyze large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will cover setting up development environments. You will then cover various recipes to perform interactive queries using Spark SQL and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will then focus on machine learning, including supervised learning, unsupervised learning, and recommendation engine algorithms. After mastering graph processing using GraphX, you will cover various recipes for cluster optimization and troubleshooting.
Author: Rishi Yadav Publisher: Packt Publishing Ltd ISBN: 1783987073 Category : Computers Languages : en Pages : 468
Book Description
By introducing in-memory persistent storage, Apache Spark eliminates the need to store intermediate data in filesystems, thereby increasing processing speed by up to 100 times. This book will focus on how to analyze large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will cover setting up development environments. You will then cover various recipes to perform interactive queries using Spark SQL and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will then focus on machine learning, including supervised learning, unsupervised learning, and recommendation engine algorithms. After mastering graph processing using GraphX, you will cover various recipes for cluster optimization and troubleshooting.
Author: Rishi Yadav Publisher: Packt Publishing Ltd ISBN: 1787127516 Category : Computers Languages : en Pages : 288
Book Description
Over 70 recipes to help you use Apache Spark as your single big data computing platform and master its libraries About This Book This book contains recipes on how to use Apache Spark as a unified compute engine Cover how to connect various source systems to Apache Spark Covers various parts of machine learning including supervised/unsupervised learning & recommendation engines Who This Book Is For This book is for data engineers, data scientists, and those who want to implement Spark for real-time data processing. Anyone who is using Spark (or is planning to) will benefit from this book. The book assumes you have a basic knowledge of Scala as a programming language. What You Will Learn Install and configure Apache Spark with various cluster managers & on AWS Set up a development environment for Apache Spark including Databricks Cloud notebook Find out how to operate on data in Spark with schemas Get to grips with real-time streaming analytics using Spark Streaming & Structured Streaming Master supervised learning and unsupervised learning using MLlib Build a recommendation engine using MLlib Graph processing using GraphX and GraphFrames libraries Develop a set of common applications or project types, and solutions that solve complex big data problems In Detail While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand Spark 2.x's real-time processing capabilities and deploy scalable big data solutions. This is a valuable resource for data scientists and those working on large-scale data projects.
Author: Meg Galvin Publisher: Hay House, Inc ISBN: 1401931340 Category : Health & Fitness Languages : en Pages : 498
Book Description
From the team that brought you the New York Times bestseller The Spark This practical yet inspirational guide, which is based on the same easy, real-world principles as the SparkPeople program, takes the guesswork out of making delicious, healthy meals and losing weight-once and for all. Award-winning chef Meg Galvin and SparkRecipes editor Stepfanie Romine have paired up to create this collection of more than 160 satisfying, sustaining, and stress-free recipes that streamline your healthy-eating efforts. With a focus on real food, generous portions, and great flavor, these recipes are not part of a fad diet. They aren't about spending money on obscure ingredients, eliminating key components of a balanced diet, or slaving away for hours at the stove. They are about making smart choices and eating food you love to eat. But this is more than just a collection of recipes—it's an education. The SparkPeople philosophy has always been about encouraging people to achieve personal goals with the help and support of others. And this cookbook works in the just the same way. Along with the recipes, you'll find step-by-step how-tos about the healthiest, most taste-enhancing cooking techniques; lists of kitchen essentials; and simple ingredient swaps that maximize flavor, while cutting fat and calories, plus you'll read motivational SparkPeople success stories from real members who have used these recipes as part of their life-changing transformations. In addition, you'll find: • Results from the SparkPeople "Ditch the Diet" Taste Test, which proves that you don't have to eat tasteless food to lose weight. • 150 meal ideas and recipes that take 30 minutes or less to prepare—plus dozens of other meals for days when you have more time. • Two weeks of meal plans that include breakfast, lunch, dinner, and snacks. So whether you're a novice taking the first steps to improve your health or a seasoned cook just looking for new, healthy recipes to add to your repertoire, this cookbook is for you. Learn to love your food, lose the weight, and ditch the diet forever!
Author: Siamak Amirghodsi Publisher: Packt Publishing Ltd ISBN: 1782174605 Category : Computers Languages : en Pages : 658
Book Description
Simplify machine learning model implementations with Spark About This Book Solve the day-to-day problems of data science with Spark This unique cookbook consists of exciting and intuitive numerical recipes Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark Build a recommendation engine that scales with Spark Find out how to build unsupervised clustering systems to classify data in Spark Build machine learning systems with the Decision Tree and Ensemble models in Spark Deal with the curse of high-dimensionality in big data using Spark Implement Text analytics for Search Engines in Spark Streaming Machine Learning System implementation using Spark In Detail Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects.
Author: Ahmed Sherif Publisher: Packt Publishing Ltd ISBN: 1788471555 Category : Computers Languages : en Pages : 462
Book Description
A solution-based guide to put your deep learning models into production with the power of Apache Spark Key Features Discover practical recipes for distributed deep learning with Apache Spark Learn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache Spark Book Description With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras. By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark. What you will learn Set up a fully functional Spark environment Understand practical machine learning and deep learning concepts Apply built-in machine learning libraries within Spark Explore libraries that are compatible with TensorFlow and Keras Explore NLP models such as Word2vec and TF-IDF on Spark Organize dataframes for deep learning evaluation Apply testing and training modeling to ensure accuracy Access readily available code that may be reusable Who this book is for If you’re looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.
Author: Padma Priya Chitturi Publisher: Packt Publishing Ltd ISBN: 1785288806 Category : Computers Languages : en Pages : 388
Book Description
Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Implement end-to-end, large-scale data analysis better than ever before Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. Learn about numerical and scientific computing using NumPy and SciPy on Spark. Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.
Author: Arun Manivannan Publisher: Packt Publishing Ltd ISBN: 1784394998 Category : Computers Languages : en Pages : 254
Book Description
Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes About This Book Implement Scala in your data analysis using features from Spark, Breeze, and Zeppelin Scale up your data anlytics infrastructure with practical recipes for Scala machine learning Recipes for every stage of the data analysis process, from reading and collecting data to distributed analytics Who This Book Is For This book shows data scientists and analysts how to leverage their existing knowledge of Scala for quality and scalable data analysis. What You Will Learn Familiarize and set up the Breeze and Spark libraries and use data structures Import data from a host of possible sources and create dataframes from CSV Clean, validate and transform data using Scala to pre-process numerical and string data Integrate quintessential machine learning algorithms using Scala stack Bundle and scale up Spark jobs by deploying them into a variety of cluster managers Run streaming and graph analytics in Spark to visualize data, enabling exploratory analysis In Detail This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits. Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you'll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX. Style and approach This book contains a rich set of recipes that covers the full spectrum of interesting data analysis tasks and will help you revolutionize your data analysis skills using Scala and Spark.
Author: Chris Downie Publisher: Hay House, Inc ISBN: 9781401935771 Category : Body, Mind & Spirit Languages : en Pages : 328
Book Description
Updated Edition! From the experts who created SparkPeople.com, America’s #1 diet and fitness site, comes The Spark. This groundbreaking book outlines the best of what has worked for millions of members who have lost weight, kept it off, and reached other goals. Driven by positive energy and proven results, The Spark outlines a breakthrough formula that combines nutrition, exercise, goal setting, motivation, and community, which has helped people change their lives beyond the scale. Discover the 27 Secrets of Success—the best action steps, foods, and proven medical advice that have helped tens of thousands of members lose from 2 to 200 pounds. Special tips from people who lost 100 pounds or more—see what these people had in common and what they did and didn’t do to make huge transformations in their lives. A step-by-step 28-day program that brings together the most effective, medically accepted nutrition and fitness practices from SparkPeople experts in an easy-to-follow plan, including flexible mix-and-match meal plans, fully illustrated workout programs, full-color before-and-after success stories, and more! And, new to this edition! Breakthrough survey results have been used to create a Strong Start Guide to help you jumpstart your weight-loss efforts. Based on what tens of thousands of successful SparkPeople members did to lose weight and change their lives, this guide tells you what to do in the first two weeks to make you five times more likely to reach your ultimate weight-loss goal! Whether you want to fit into your "skinny jeans," improve your health and fitness levels, change your outlook and mood, or reach all new goals, The Spark can help you transform your body and your life. What are you waiting for? Spark your life today!
Author: Alvin Alexander Publisher: "O'Reilly Media, Inc." ISBN: 1492051497 Category : Computers Languages : en Pages : 802
Book Description
Save time and trouble building object-oriented, functional, and concurrent applications with Scala. The latest edition of this comprehensive cookbook is packed with more than 250 ready-to-use recipes and 1,000 code examples to help you solve the most common problems when working with Scala 3 and its popular libraries. Scala changes the way you think about programming--and that's a good thing. Whether you're working on web, big data, or distributed applications, this cookbook provides recipes based on real-world scenarios for both experienced Scala developers and programmers just learning to use this JVM language. Author Alvin Alexander includes practical solutions from his experience using Scala for component-based, highly scalable applications that support concurrency and distribution. Recipes cover: Strings, numbers, and control structures Classes, methods, objects, traits, packaging, and imports Functional programming techniques Scala's wealth of collections classes and methods Building and publishing Scala applications with sbt Actors and concurrency with Scala Future and Akka Typed Popular libraries, including Spark, Scala.js, Play Framework, and GraalVM Types, such as variance, givens, intersections, and unions Best practices, including pattern matching, modules, and functional error handling
Author: Nagaraj Venkatesan Publisher: Packt Publishing Ltd ISBN: 1803235004 Category : Computers Languages : en Pages : 608
Book Description
Nearly 80 recipes to help you collect and transform data from multiple sources into a single data source, making it way easier to perform analytics on the data Key FeaturesBuild data pipelines from scratch and find solutions to common data engineering problemsLearn how to work with Azure Data Factory, Data Lake, Databricks, and Synapse AnalyticsMonitor and maintain your data engineering pipelines using Log Analytics, Azure Monitor, and Azure PurviewBook Description The famous quote 'Data is the new oil' seems more true every day as the key to most organizations' long-term success lies in extracting insights from raw data. One of the major challenges organizations face in leveraging value out of data is building performant data engineering pipelines for data visualization, ingestion, storage, and processing. This second edition of the immensely successful book by Ahmad Osama brings to you several recent enhancements in Azure data engineering and shares approximately 80 useful recipes covering common scenarios in building data engineering pipelines in Microsoft Azure. You'll explore recipes from Azure Synapse Analytics workspaces Gen 2 and get to grips with Synapse Spark pools, SQL Serverless pools, Synapse integration pipelines, and Synapse data flows. You'll also understand Synapse SQL Pool optimization techniques in this second edition. Besides Synapse enhancements, you'll discover helpful tips on managing Azure SQL Database and learn about security, high availability, and performance monitoring. Finally, the book takes you through overall data engineering pipeline management, focusing on monitoring using Log Analytics and tracking data lineage using Azure Purview. By the end of this book, you'll be able to build superior data engineering pipelines along with having an invaluable go-to guide. What you will learnProcess data using Azure Databricks and Azure Synapse AnalyticsPerform data transformation using Azure Synapse data flowsPerform common administrative tasks in Azure SQL DatabaseBuild effective Synapse SQL pools which can be consumed by Power BIMonitor Synapse SQL and Spark pools using Log AnalyticsTrack data lineage using Microsoft Purview integration with pipelinesWho this book is for This book is for data engineers, data architects, database administrators, and data professionals who want to get well versed with the Azure data services for building data pipelines. Basic understanding of cloud and data engineering concepts will help in getting the most out of this book.
Author: Rishi Yadav
Author: Rishi Yadav
Author: Rishi Yadav
Author: Meg Galvin
Author: Siamak Amirghodsi
Author: Ahmed Sherif
Author: Padma Priya Chitturi
Author: Arun Manivannan
Author: Chris Downie
Author: Alvin Alexander
Author: Nagaraj Venkatesan