Search Results for "spark-cookbook"

Spark Cookbook

Spark Cookbook

  • Author: Rishi Yadav
  • Publisher: Packt Publishing Ltd
  • ISBN: 1783987073
  • Category: Computers
  • Page: 226
  • View: 4987
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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.

Spark Cookbook

Spark Cookbook

  • Author: Rishi Yadav
  • Publisher: N.A
  • ISBN: 9781783987061
  • Category: Computers
  • Page: 226
  • View: 4640
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If you are a data engineer, an application developer, or a data scientist who would like to leverage the power of Apache Spark to get better insights from big data, then this is the book for you.

Apache Spark 2.x Cookbook

Apache Spark 2.x Cookbook

  • Author: Rishi Yadav
  • Publisher: Packt Publishing Ltd
  • ISBN: 1787127516
  • Category: Computers
  • Page: 294
  • View: 6360
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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.

Apache Spark 2.x Machine Learning Cookbook

Apache Spark 2.x Machine Learning Cookbook

  • Author: Siamak Amirghodsi,Meenakshi Rajendran,Broderick Hall,Shuen Mei
  • Publisher: Packt Publishing Ltd
  • ISBN: 1782174605
  • Category: Computers
  • Page: 666
  • View: 8275
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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.

Apache Spark for Data Science Cookbook

Apache Spark for Data Science Cookbook

  • Author: Padma Priya Chitturi
  • Publisher: Packt Publishing Ltd
  • ISBN: 1785288806
  • Category: Computers
  • Page: 392
  • View: 9015
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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.

Scala Data Analysis Cookbook

Scala Data Analysis Cookbook

  • Author: Arun Manivannan
  • Publisher: Packt Publishing Ltd
  • ISBN: 1784394998
  • Category: Computers
  • Page: 254
  • View: 2616
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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.

The Sparkpeople Cookbook

The Sparkpeople Cookbook

  • Author: Meg Galvin
  • Publisher: Hay House, Inc
  • ISBN: 1401931340
  • Category: Health & Fitness
  • Page: 465
  • View: 8246
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From the team that brought you SparkPeople.com, America's #1 weight-loss and fitness site, and the New York Times bestseller The Spark , comes The SparkPeople Cookbook . 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!

Apache Spark Deep Learning Cookbook

Apache Spark Deep Learning Cookbook

Over 80 Recipes That Streamline Deep Learning in a Distributed Environment with Apache Spark

  • Author: Ahmed Sherif,Amrith Ravindra
  • Publisher: Packt Publishing
  • ISBN: 9781788474221
  • Category: Computers
  • Page: 474
  • View: 4295
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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.

Food That Says Welcome

Food That Says Welcome

Simple Recipes to Spark the Spirit of Hospitality

  • Author: Barbara Smith
  • Publisher: WaterBrook
  • ISBN: 9780307499820
  • Category: Cooking
  • Page: 208
  • View: 9578
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From the mother of Grammy Award winning singer Michael W. Smith, make your friends and family feel welcome, one meal at a time. "Welcome to my home as we share life and laughter around the table. It means sharing my life in such a way that there is always room for one more."–Barbara Smith Some people naturally have the gift of hospitality, instinctively creating inviting, mouth-watering meals and a warm environment that assures guests, “We’re glad you’re here.” Fortunately, says food expert Barbara Smith, the rest of us have the same potential to make guests feel nurtured, and here she offers an unforgettable treasury of recipes, tips, and how-to’s for everyone with the spiritual gift of hospitality–and for the rest of us who want to look like we do. In Food That Says Welcome you’ ll learn to: • Make welcoming food that is healthy and easy to prepare. • Create an atmosphere that says to your guests, “You are special.” • Make hospitality your ministry and service. Learn what makes Barbara Smith’s meals and outreach so rave-worthy and discover how you can invoke the same spirit of hospitality in your own home and kitchen.

Learning Spark

Learning Spark

Lightning-Fast Big Data Analysis

  • Author: Holden Karau,Andy Konwinski,Patrick Wendell,Matei Zaharia
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1449359051
  • Category: Computers
  • Page: 276
  • View: 7139
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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

Spark Operations Cookbook

Spark Operations Cookbook

Solving the Practical Challenges of Spark Implementation

  • Author: Neelesh Srinivas Salian
  • Publisher: O'Reilly Media
  • ISBN: 9781491971581
  • Category: Computers
  • Page: 200
  • View: 410
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The Apache Spark cluster computing system aims to make data analytics fast-both fast to run and fast to write. But as powerful and useful as Spark is for distributed systems, there are many issues that may occur during implementation. This practical cookbook contains recipes solving the most common problems that Spark users face. Author Neelesh Srinivas Salian, a customer operations engineer at Cloudera, has seen all things that can go wrong in the code for Spark applications. Data engineers, system administrators, architects will learn recipes for debugging common and unexpected problems that occur during key phases of Spark implementation on large distributed system environments. From setting up your cluster to running your first application, submitting to a cluster, understanding storage needs, and handling security and monitoring metrics, this book is your guide to facing any Spark operations issue. Learn an approach to debugging Spark from the perspective of improving business logic implementation Understand the nuances of Spark's components, including Spark Core, Spark Streaming, SparkSQL, and MLLib Get an entire chapter devoted to Spark security-an emerging and vital topic

Scala Cookbook

Scala Cookbook

Recipes for Object-Oriented and Functional Programming

  • Author: Alvin Alexander
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1449340334
  • Category: Computers
  • Page: 722
  • View: 2327
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Save time and trouble when using Scala to build object-oriented, functional, and concurrent applications. With more than 250 ready-to-use recipes and 700 code examples, this comprehensive cookbook covers the most common problems you’ll encounter when using the Scala language, libraries, and tools. It’s ideal not only for experienced Scala developers, but also for programmers learning to use this JVM language. Author Alvin Alexander (creator of DevDaily.com) provides solutions based on his experience using Scala for highly scalable, component-based applications that support concurrency and distribution. Packed with real-world scenarios, this book provides recipes for: Strings, numeric types, and control structures Classes, methods, objects, traits, and packaging Functional programming in a variety of situations Collections covering Scala's wealth of classes and methods Concurrency, using the Akka Actors library Using the Scala REPL and the Simple Build Tool (SBT) Web services on both the client and server sides Interacting with SQL and NoSQL databases Best practices in Scala development

Put a Little Spark in Your Ash

Put a Little Spark in Your Ash

Go on and Get Yer Ash Cookin'!

  • Author: Cq Products,G & R Publishing
  • Publisher: Cq Products
  • ISBN: 9781563834486
  • Category: Cooking
  • Page: 64
  • View: 5963
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Mastering Apache Spark

Mastering Apache Spark

  • Author: Mike Frampton
  • Publisher: Packt Publishing Ltd
  • ISBN: 1783987154
  • Category: Computers
  • Page: 318
  • View: 3237
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Gain expertise in processing and storing data by using advanced techniques with Apache Spark About This Book Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan Evaluate how Cassandra and Hbase can be used for storage An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Extend the tools available for processing and storage Examine clustering and classification using MLlib Discover Spark stream processing via Flume, HDFS Create a schema in Spark SQL, and learn how a Spark schema can be populated with data Study Spark based graph processing using Spark GraphX Combine Spark with H20 and deep learning and learn why it is useful Evaluate how graph storage works with Apache Spark, Titan, HBase and Cassandra Use Apache Spark in the cloud with Databricks and AWS In Detail Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.

The Spark TRADE

The Spark TRADE

  • Author: Chris Downie
  • Publisher: Hay House, Inc
  • ISBN: 9781401935771
  • Category: Body, Mind & Spirit
  • Page: 328
  • View: 6654
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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!

Hadoop Real-World Solutions Cookbook

Hadoop Real-World Solutions Cookbook

  • Author: Tanmay Deshpande
  • Publisher: Packt Publishing Ltd
  • ISBN: 1784398004
  • Category: Computers
  • Page: 290
  • View: 9136
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Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout About This Book Implement outstanding Machine Learning use cases on your own analytics models and processes. Solutions to common problems when working with the Hadoop ecosystem. Step-by-step implementation of end-to-end big data use cases. Who This Book Is For Readers who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes. What You Will Learn Installing and maintaining Hadoop 2.X cluster and its ecosystem. Write advanced Map Reduce programs and understand design patterns. Advanced Data Analysis using the Hive, Pig, and Map Reduce programs. Import and export data from various sources using Sqoop and Flume. Data storage in various file formats such as Text, Sequential, Parquet, ORC, and RC Files. Machine learning principles with libraries such as Mahout Batch and Stream data processing using Apache Spark In Detail Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business. Style and approach An easy-to-follow guide that walks you through world of big data. Each tool in the Hadoop ecosystem is explained in detail and the recipes are placed in such a manner that readers can implement them sequentially. Plenty of reference links are provided for advanced reading.

Learning PySpark

Learning PySpark

  • Author: Tomasz Drabas,Denny Lee
  • Publisher: Packt Publishing Ltd
  • ISBN: 1786466252
  • Category: Computers
  • Page: 274
  • View: 4634
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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.

Arduino Cookbook

Arduino Cookbook

  • Author: Michael Margolis
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1449313876
  • Category: Computers
  • Page: 699
  • View: 1288
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Presents an introduction to the open-source electronics prototyping platform.

Learning Apache Spark 2

Learning Apache Spark 2

  • Author: Muhammad Asif Abbasi
  • Publisher: Packt Publishing Ltd
  • ISBN: 1785889583
  • Category: Computers
  • Page: 356
  • View: 364
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Learn about the fastest-growing open source project in the world, and find out how it revolutionizes big data analytics About This Book Exclusive guide that covers how to get up and running with fast data processing using Apache Spark Explore and exploit various possibilities with Apache Spark using real-world use cases in this book Want to perform efficient data processing at real time? This book will be your one-stop solution. Who This Book Is For This guide appeals to big data engineers, analysts, architects, software engineers, even technical managers who need to perform efficient data processing on Hadoop at real time. Basic familiarity with Java or Scala will be helpful. The assumption is that readers will be from a mixed background, but would be typically people with background in engineering/data science with no prior Spark experience and want to understand how Spark can help them on their analytics journey. What You Will Learn Get an overview of big data analytics and its importance for organizations and data professionals Delve into Spark to see how it is different from existing processing platforms Understand the intricacies of various file formats, and how to process them with Apache Spark. Realize how to deploy Spark with YARN, MESOS or a Stand-alone cluster manager. Learn the concepts of Spark SQL, SchemaRDD, Caching and working with Hive and Parquet file formats Understand the architecture of Spark MLLib while discussing some of the off-the-shelf algorithms that come with Spark. Introduce yourself to the deployment and usage of SparkR. Walk through the importance of Graph computation and the graph processing systems available in the market Check the real world example of Spark by building a recommendation engine with Spark using ALS. Use a Telco data set, to predict customer churn using Random Forests. In Detail Spark juggernaut keeps on rolling and getting more and more momentum each day. Spark provides key capabilities in the form of Spark SQL, Spark Streaming, Spark ML and Graph X all accessible via Java, Scala, Python and R. Deploying the key capabilities is crucial whether it is on a Standalone framework or as a part of existing Hadoop installation and configuring with Yarn and Mesos. The next part of the journey after installation is using key components, APIs, Clustering, machine learning APIs, data pipelines, parallel programming. It is important to understand why each framework component is key, how widely it is being used, its stability and pertinent use cases. Once we understand the individual components, we will take a couple of real life advanced analytics examples such as 'Building a Recommendation system', 'Predicting customer churn' and so on. The objective of these real life examples is to give the reader confidence of using Spark for real-world problems. Style and approach With the help of practical examples and real-world use cases, this guide will take you from scratch to building efficient data applications using Apache Spark. You will learn all about this excellent data processing engine in a step-by-step manner, taking one aspect of it at a time. This highly practical guide will include how to work with data pipelines, dataframes, clustering, SparkSQL, parallel programming, and such insightful topics with the help of real-world use cases.

Mastering Apache Spark 2.x

Mastering Apache Spark 2.x

  • Author: Romeo Kienzler
  • Publisher: Packt Publishing Ltd
  • ISBN: 178528522X
  • Category: Computers
  • Page: 354
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Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. Master the art of real-time processing with the help of Apache Spark 2.x Who This Book Is For If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected. What You Will Learn Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames Learn how specific parameter settings affect overall performance of an Apache Spark cluster Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks. Style and approach This book is an extensive guide to Apache Spark modules and tools and shows how Spark's functionality can be extended for real-time processing and storage with worked examples.