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| spark in a nutshell: Data Algorithms with Spark Mahmoud Parsian, 2022-04-08 Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns |
| spark in a nutshell: Spark: The Definitive Guide Bill Chambers, Matei Zaharia, 2018-02-08 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 |
| spark in a nutshell: Data Algorithms Mahmoud Parsian, 2015-07-13 If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis) |
| spark in a nutshell: Big Data Analytics Venkat Ankam, 2016-09-28 A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science |
| spark in a nutshell: Python in a Nutshell Alex Martelli, 2006-07-14 This volume offers Python programmers a straightforward guide to the important tools and modules of this open source language. It deals with the most frequently used parts of the standard library as well as the most popular and important third party extensions. |
| spark in a nutshell: Data Algorithms with Spark Mahmoud Parsian, 2022-01-18 Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Design machine learning algorithms including Naive Bayes, linear regression, and logistic regression Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data (such as DNA-Seq) |
| spark in a nutshell: Hands-On Data Science and Python Machine Learning Frank Kane, 2017-07-31 This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time. |
| spark in a nutshell: Apache Kafka Quick Start Guide Raúl Estrada, 2018-12-27 Process large volumes of data in real-time while building high performance and robust data stream processing pipeline using the latest Apache Kafka 2.0 Key FeaturesSolve practical large data and processing challenges with KafkaTackle data processing challenges like late events, windowing, and watermarkingUnderstand real-time streaming applications processing using Schema registry, Kafka connect, Kafka streams, and KSQLBook Description Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the fly. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines. This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment. Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows. What you will learnHow to validate data with KafkaAdd information to existing data flowsGenerate new information through message compositionPerform data validation and versioning with the Schema RegistryHow to perform message Serialization and DeserializationHow to perform message Serialization and DeserializationProcess data streams with Kafka StreamsUnderstand the duality between tables and streams with KSQLWho this book is for This book is for developers who want to quickly master the practical concepts behind Apache Kafka. The audience need not have come across Apache Kafka previously; however, a familiarity of Java or any JVM language will be helpful in understanding the code in this book. |
| spark in a nutshell: C in a Nutshell Peter Prinz, Tony Crawford, 2015-12-10 The new edition of this classic O’Reilly reference provides clear, detailed explanations of every feature in the C language and runtime library, including multithreading, type-generic macros, and library functions that are new in the 2011 C standard (C11). If you want to understand the effects of an unfamiliar function, and how the standard library requires it to behave, you’ll find it here, along with a typical example. Ideal for experienced C and C++ programmers, this book also includes popular tools in the GNU software collection. You’ll learn how to build C programs with GNU Make, compile executable programs from C source code, and test and debug your programs with the GNU debugger. In three sections, this authoritative book covers: C language concepts and language elements, with separate chapters on types, statements, pointers, memory management, I/O, and more The C standard library, including an overview of standard headers and a detailed function reference Basic C programming tools in the GNU software collection, with instructions on how use them with the Eclipse IDE |
| spark in a nutshell: Ruby in a Nutshell Yukihiro Matsumoto, 2002 Portable and convenient, Ruby Essentials is a concise reference to the features of Ruby's command-line options, syntax, built-in variables, functions and other commonly used classes. Additional code, discussion and examples are included. |
| spark in a nutshell: Scala and Spark for Big Data Analytics Md. Rezaul Karim, Sridhar Alla, 2017-07-22 Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye!About This Book* Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts* Work on a wide array of applications, from simple batch jobs to stream processing and machine learning* Explore the most common as well as some complex use-cases to perform large-scale data analysis with SparkWho This Book Is ForAnyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker.What You Will Learn* Understand object-oriented & functional programming concepts of Scala* In-depth understanding of Scala collection APIs* Work with RDD and DataFrame to learn Spark's core abstractions* Analysing structured and unstructured data using SparkSQL and GraphX* Scalable and fault-tolerant streaming application development using Spark structured streaming* Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML* Build clustering models to cluster a vast amount of data* Understand tuning, debugging, and monitoring Spark applications* Deploy Spark applications on real clusters in Standalone, Mesos, and YARNIn DetailScala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you.The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment.You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio.By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.Style and approachFilled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist. |
| spark in a nutshell: Java in a Nutshell David Flanagan, 2005 This landmark book is the most widely used Java reference inthe world. Edition after edition, Java in a Nutshell haskept developers up to speed on changes to the Java platformand programming language, offering them a single source ofinformation when they need help with critical details. The5th edition not only covers deep changes in the ...... |
| spark in a nutshell: Data Algorithms Mahmoud Parsian, 2015-07-13 If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis) |
| spark in a nutshell: Scala and Spark for Big Data Analytics Md. Rezaul Karim, Sridhar Alla, 2017-07-25 Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist. |
| spark in a nutshell: Spark Timothy J. Jorgensen, 2023-06-06 A fresh look at electricity and its powerful role in life on Earth When we think of electricity, we likely imagine the energy humming inside our home appliances or lighting up our electronic devices—or perhaps we envision the lightning-streaked clouds of a stormy sky. But electricity is more than an external source of power, heat, or illumination. Life at its essence is nothing if not electrical. The story of how we came to understand electricity’s essential role in all life is rooted in our observations of its influences on the body—influences governed by the body’s central nervous system. Spark explains the science of electricity from this fresh, biological perspective. Through vivid tales of scientists and individuals—from Benjamin Franklin to Elon Musk—Timothy Jorgensen shows how our views of electricity and the nervous system evolved in tandem, and how progress in one area enabled advancements in the other. He explains how these developments have allowed us to understand—and replicate—the ways electricity enables the body’s essential functions of sight, hearing, touch, and movement itself. Throughout, Jorgensen examines our fascination with electricity and how it can help or harm us. He explores a broad range of topics and events, including the Nobel Prize–winning discoveries of the electron and neuron, the history of experimentation involving electricity’s effects on the body, and recent breakthroughs in the use of electricity to treat disease. Filled with gripping adventures in scientific exploration, Spark offers an indispensable look at electricity, how it works, and how it animates our lives from within and without. |
| spark in a nutshell: SQL in a Nutshell Kevin Kline, Brand Hunt, Daniel Kline, 2004-09-24 SQL in a Nutshell applies the eminently useful Nutshell format to Structured Query Language (SQL), the elegant--but complex--descriptive language that is used to create and manipulate large stores of data. For SQL programmers, analysts, and database administrators, the new second edition of SQL in a Nutshell is the essential date language reference for the world's top SQL database products. SQL in a Nutshell is a lean, focused, and thoroughly comprehensive reference for those who live in a deadline-driven world.This invaluable desktop quick reference drills down and documents every SQL command and how to use it in both commercial (Oracle, DB2, and Microsoft SQL Server) and open source implementations (PostgreSQL, and MySQL). It describes every command and reference and includes the command syntax (by vendor, if the syntax differs across implementations), a clear description, and practical examples that illustrate important concepts and uses. And it also explains how the leading commercial and open sources database product implement SQL. This wealth of information is packed into a succinct, comprehensive, and extraordinarily easy-to-use format that covers the SQL syntax of no less than 4 different databases.When you need fast, accurate, detailed, and up-to-date SQL information, SQL in a Nutshell, Second Edition will be the quick reference you'll reach for every time. SQL in a Nutshell is small enough to keep by your keyboard, and concise (as well as clearly organized) enough that you can look up the syntax you need quickly without having to wade through a lot of useless fluff. You won't want to work on a project involving SQL without it. |
| spark in a nutshell: Frank Kane's Taming Big Data with Apache Spark and Python Frank Kane, 2017-06-30 Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace. |
| spark in a nutshell: Fast Data Processing with Spark 2 Krishna Sankar, 2016-10-24 Learn how to use Spark to process big data at speed and scale for sharper analytics. Put the principles into practice for faster, slicker big data projects. About This Book A quick way to get started with Spark – and reap the rewards From analytics to engineering your big data architecture, we've got it covered Bring your Scala and Java knowledge – and put it to work on new and exciting problems Who This Book Is For This book is for developers with little to no knowledge of Spark, but with a background in Scala/Java programming. It's recommended that you have experience in dealing and working with big data and a strong interest in data science. What You Will Learn Install and set up Spark in your cluster Prototype distributed applications with Spark's interactive shell Perform data wrangling using the new DataFrame APIs Get to know the different ways to interact with Spark's distributed representation of data (RDDs) Query Spark with a SQL-like query syntax See how Spark works with big data Implement machine learning systems with highly scalable algorithms Use R, the popular statistical language, to work with Spark Apply interesting graph algorithms and graph processing with GraphX In Detail When people want a way to process big data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it's unsurprising that it's becoming popular with data analysts and engineers everywhere. Beginning with the fundamentals, we'll show you how to get set up with Spark with minimum fuss. You'll then get to grips with some simple APIs before investigating machine learning and graph processing – throughout we'll make sure you know exactly how to apply your knowledge. You will also learn how to use the Spark shell, how to load data before finding out how to build and run your own Spark applications. Discover how to manipulate your RDD and get stuck into a range of DataFrame APIs. As if that's not enough, you'll also learn some useful Machine Learning algorithms with the help of Spark MLlib and integrating Spark with R. We'll also make sure you're confident and prepared for graph processing, as you learn more about the GraphX API. Style and approach This book is a basic, step-by-step tutorial that will help you take advantage of all that Spark has to offer. |
| spark in a nutshell: Mastering MongoDB 6.x Alex Giamas, 2022-08-30 Design and build solutions with the most powerful document database, MongoDB Key FeaturesLearn from the experts about every new feature in MongoDB 6 and 5Develop applications and administer clusters using MongoDB on premise or in the cloudExplore code-rich case studies showcasing MongoDB's major features followed by best practicesBook Description MongoDB is a leading non-relational database. This book covers all the major features of MongoDB including the latest version 6. MongoDB 6.x adds many new features and expands on existing ones such as aggregation, indexing, replication, sharding and MongoDB Atlas tools. Some of the MongoDB Atlas tools that you will master include Atlas dedicated clusters and Serverless, Atlas Search, Charts, Realm Application Services/Sync, Compass, Cloud Manager and Data Lake. By getting hands-on working with code using realistic use cases, you will master the art of modeling, shaping and querying your data and become the MongoDB oracle for the business. You will focus on broadly used and niche areas such as optimizing queries, configuring large-scale clusters, configuring your cluster for high performance and availability and many more. Later, you will become proficient in auditing, monitoring, and securing your clusters using a structured and organized approach. By the end of this book, you will have grasped all the practical understanding needed to design, develop, administer and scale MongoDB-based database applications both on premises and on the cloud. What you will learnUnderstand data modeling and schema design, including smart indexingMaster querying data using aggregationUse distributed transactions, replication and sharding for better resultsAdminister your database using backups and monitoring toolsSecure your cluster with the best checklists and adviceMaster MongoDB Atlas, Search, Charts, Serverless, Realm, Compass, Cloud Manager and other tools offered in the cloud or on premisesIntegrate MongoDB with other big data sourcesDesign and deploy MongoDB in mobile, IoT and serverless environmentsWho this book is for This book is for MongoDB developers and database administrators who want to learn how to model their data using MongoDB in depth, for both greenfield and existing projects. An understanding of MongoDB, shell command skills and basic database design concepts is required to get the most out of this book. |
| spark in a nutshell: Algorithms in a Nutshell George T. Heineman, Gary Pollice, Stanley Selkow, 2008-10-14 Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs -- with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will: Solve a particular coding problem or improve on the performance of an existing solution Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips Learn the expected performance of an algorithm, and the conditions it needs to perform at its best Discover the impact that similar design decisions have on different algorithms Learn advanced data structures to improve the efficiency of algorithms With Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications. |
| spark in a nutshell: The Ancient World in 100 Words Clive Gifford, 2019-10-15 How do you sum up the ancient world in just 100 words? This book takes on the challenge! With 100 carefully chosen words, each explained in just 100 words, this book provides a quick and fun insight into the characters, events and inventions of the ancient world. With entries on the Egyptians, the Phoenicians, the Minoans, the Greeks,and the Romans, this book is an easy way to gain a rounded knowledge of the subject area, while also sparking discussion and provoking thought from readers, young and old. What were pyramids used for? How did the Romans fight battles? Which Greek inventions are still used today? Each word is brought to life with engaging illustrations and absorbing text, sure to inspire the imagination of budding historians. |
| spark in a nutshell: Mastering Spark with R Javier Luraschi, Kevin Kuo, Edgar Ruiz, 2019-10-07 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 |
| spark in a nutshell: Windows XP in a Nutshell David A. Karp, Tim O'Reilly, Troy Mott, 2002 Discusses how to install, run, and configure Windows XP for both the home and office, explaining how to connect to the Internet, design a LAN, and share drives and printers, and includes tips and troubleshooting techniques. |
| spark in a nutshell: The Giver Lois Lowry, 2014 The Giver, the 1994 Newbery Medal winner, has become one of the most influential novels of our time. The haunting story centers on twelve-year-old Jonas, who lives in a seemingly ideal, if colorless, world of conformity and contentment. Not until he is given his life assignment as the Receiver of Memory does he begin to understand the dark, complex secrets behind his fragile community. This movie tie-in edition features cover art from the movie and exclusive Q&A with members of the cast, including Taylor Swift, Brenton Thwaites and Cameron Monaghan. |
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| spark in a nutshell: Hardware Dealers' Magazine , 1915 |
| spark in a nutshell: The Automobile , 1904 |
| spark in a nutshell: Fast Data Processing Systems with SMACK Stack Raul Estrada, 2016-12-22 Combine the incredible powers of Spark, Mesos, Akka, Cassandra, and Kafka to build data processing platforms that can take on even the hardest of your data troubles! About This Book This highly practical guide shows you how to use the best of the big data technologies to solve your response-critical problems Learn the art of making cheap-yet-effective big data architecture without using complex Greek-letter architectures Use this easy-to-follow guide to build fast data processing systems for your organization Who This Book Is For If you are a developer, data architect, or a data scientist looking for information on how to integrate the Big Data stack architecture and how to choose the correct technology in every layer, this book is what you are looking for. What You Will Learn Design and implement a fast data Pipeline architecture Think and solve programming challenges in a functional way with Scala Learn to use Akka, the actors model implementation for the JVM Make on memory processing and data analysis with Spark to solve modern business demands Build a powerful and effective cluster infrastructure with Mesos and Docker Manage and consume unstructured and No-SQL data sources with Cassandra Consume and produce messages in a massive way with Kafka In Detail SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique developers have begun to use to tackle critical real-time analytics for big data. This highly practical guide will teach you how to integrate these technologies to create a highly efficient data analysis system for fast data processing. We'll start off with an introduction to SMACK and show you when to use it. First you'll get to grips with functional thinking and problem solving using Scala. Next you'll come to understand the Akka architecture. Then you'll get to know how to improve the data structure architecture and optimize resources using Apache Spark. Moving forward, you'll learn how to perform linear scalability in databases with Apache Cassandra. You'll grasp the high throughput distributed messaging systems using Apache Kafka. We'll show you how to build a cheap but effective cluster infrastructure with Apache Mesos. Finally, you will deep dive into the different aspect of SMACK using a few case studies. By the end of the book, you will be able to integrate all the components of the SMACK stack and use them together to achieve highly effective and fast data processing. Style and approach With the help of various industry examples, you will learn about the full stack of big data architecture, taking the important aspects in every technology. You will learn how to integrate the technologies to build effective systems rather than getting incomplete information on single technologies. You will learn how various open source technologies can be used to build cheap and fast data processing systems with the help of various industry examples |
| spark in a nutshell: Automotive Industries , 1915 |
| spark in a nutshell: Field & Stream , 1978-06 FIELD & STREAM, America’s largest outdoor sports magazine, celebrates the outdoor experience with great stories, compelling photography, and sound advice while honoring the traditions hunters and fishermen have passed down for generations. |
| spark in a nutshell: Manufacturers Record , 1916 |
| spark in a nutshell: Motors in a Nutshell Swinfen Bramley-Moore, 1922 |
| spark in a nutshell: Mac OS X in a Nutshell Jason McIntosh, Chuck Toporek, Chris Stone, 2003 Complete overview of Mac OS Jaguar (Mac OS X 10.2) including basic system and network administration features, hundreds of tips and tricks, with an overview of Mac OS X's Unix text editors and CVS. |
| spark in a nutshell: Motor Age , 1909 |
| spark in a nutshell: Farm Economy , 1915 |
| spark in a nutshell: R in a Nutshell Joseph Adler, 2012-10-09 Presents a guide to the R computer language, covering such topics as the user interface, packages, syntax, objects, functions, object-oriented programming, data sets, lattice graphics, regression models, and bioconductor. |
| spark in a nutshell: THE CRUCIBLE ARTHUR MILLER, 1971 |
| spark in a nutshell: Mastering MongoDB 3.x Alex Giamas, 2017-11-17 An expert's guide to build fault tolerant MongoDB application About This Book Master the advanced modeling, querying, and administration techniques in MongoDB and become a MongoDB expert Covers the latest updates and Big Data features frequently used by professional MongoDB developers and administrators If your goal is to become a certified MongoDB professional, this book is your perfect companion Who This Book Is For Mastering MongoDB is a book for database developers, architects, and administrators who want to learn how to use MongoDB more effectively and productively. If you have experience in, and are interested in working with, NoSQL databases to build apps and websites, then this book is for you. What You Will Learn Get hands-on with advanced querying techniques such as indexing, expressions, arrays, and more. Configure, monitor, and maintain highly scalable MongoDB environment like an expert. Master replication and data sharding to optimize read/write performance. Design secure and robust applications based on MongoDB. Administer MongoDB-based applications on-premise or in the cloud Scale MongoDB to achieve your design goals Integrate MongoDB with big data sources to process huge amounts of data In Detail MongoDB has grown to become the de facto NoSQL database with millions of users—from small startups to Fortune 500 companies. Addressing the limitations of SQL schema-based databases, MongoDB pioneered a shift of focus for DevOps and offered sharding and replication maintainable by DevOps teams. The book is based on MongoDB 3.x and covers topics ranging from database querying using the shell, built in drivers, and popular ODM mappers to more advanced topics such as sharding, high availability, and integration with big data sources. You will get an overview of MongoDB and how to play to its strengths, with relevant use cases. After that, you will learn how to query MongoDB effectively and make use of indexes as much as possible. The next part deals with the administration of MongoDB installations on-premise or in the cloud. We deal with database internals in the next section, explaining storage systems and how they can affect performance. The last section of this book deals with replication and MongoDB scaling, along with integration with heterogeneous data sources. By the end this book, you will be equipped with all the required industry skills and knowledge to become a certified MongoDB developer and administrator. Style and approach This book takes a practical, step-by-step approach to explain the concepts of MongoDB. Practical use-cases involving real-world examples are used throughout the book to clearly explain theoretical concepts. |
| spark in a nutshell: Modern Big Data Processing with Hadoop V Naresh Kumar, Prashant Shindgikar, 2018-03-30 A comprehensive guide to design, build and execute effective Big Data strategies using Hadoop Key Features -Get an in-depth view of the Apache Hadoop ecosystem and an overview of the architectural patterns pertaining to the popular Big Data platform -Conquer different data processing and analytics challenges using a multitude of tools such as Apache Spark, Elasticsearch, Tableau and more -A comprehensive, step-by-step guide that will teach you everything you need to know, to be an expert Hadoop Architect Book Description The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems. What you will learn Build an efficient enterprise Big Data strategy centered around Apache Hadoop Gain a thorough understanding of using Hadoop with various Big Data frameworks such as Apache Spark, Elasticsearch and more Set up and deploy your Big Data environment on premises or on the cloud with Apache Ambari Design effective streaming data pipelines and build your own enterprise search solutions Utilize the historical data to build your analytics solutions and visualize them using popular tools such as Apache Superset Plan, set up and administer your Hadoop cluster efficiently Who this book is for This book is for Big Data professionals who want to fast-track their career in the Hadoop industry and become an expert Big Data architect. Project managers and mainframe professionals looking forward to build a career in Big Data Hadoop will also find this book to be useful. Some understanding of Hadoop is required to get the best out of this book. |
| spark in a nutshell: Metamorphosis Franz Kafka, 2024-02-02 Metamorphosis by Franz Kafka is a haunting and surreal exploration of existentialism and the human condition. This novella introduces readers to Gregor Samsa, a diligent traveling salesman who wakes up one morning to find himself transformed into a gigantic insect. Kafka's narrative delves into the isolation, alienation, and absurdity that Gregor experiences as he grapples with his new identity. The novella is a profound examination of the individual's struggle to maintain a sense of self and belonging in a world that often feels incomprehensible. Kafka's writing is characterized by its dreamlike quality and a sense of impending doom. As Gregor's physical and emotional transformation unfolds, readers are drawn into a nightmarish world that blurs the lines between reality and illusion. Metamorphosis is a timeless work that continues to captivate readers with its exploration of themes such as identity, family, and the dehumanizing effects of modern society. Kafka's unique style and ability to evoke a sense of existential unease make this novella a literary classic. Step into the surreal and unsettling world of Metamorphosis and embark on a journey of self-discovery and existential reflection. Kafka's masterpiece challenges readers to confront the complexities of the human psyche and the enigmatic nature of existence. ABOUT THE AUTHOR Franz Kafka (1883-1924) was a Czech-born German-speaking novelist and short story writer whose works have had a profound influence on modern literature. Born in Prague, which was then part of the Austro-Hungarian Empire, Kafka's writing is characterized by its exploration of existentialism, alienation, and the absurdity of human existence. Kafka's most famous works include Metamorphosis, where the protagonist wakes up one morning transformed into a giant insect, and The Trial, a nightmarish tale of a man arrested and tried by an inscrutable and oppressive bureaucracy. His writing often delves into the themes of isolation and the struggle to find meaning in an indifferent world. Despite his relatively small body of work, Kafka's impact on literature and philosophy has been immense. His writings have been interpreted in various ways, and the term Kafkaesque is often used to describe situations characterized by surreal complexity and absurdity. Kafka's legacy as a literary innovator and his exploration of the human psyche continue to captivate readers and scholars alike, making him a central figure in the world of modern literature. |
Apache Spark™ - Unified Engine for large-scale data analytics
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Overview - Spark 4.0.0 Documentation - Apache Spark
Running Spark Client Applications Anywhere with Spark Connect. Spark Connect is a new client-server architecture introduced in Spark 3.4 that decouples Spark client applications and allows …
Quick Start - Spark 4.0.0 Documentation - Apache Spark
Unlike the earlier examples with the Spark shell, which initializes its own SparkSession, we initialize a SparkSession as part of the program. To build the program, we also write a Maven …
Documentation - Apache Spark
The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Spark Streaming, and GraphX. In addition, this page lists other resources …
PySpark Overview — PySpark 4.0.0 documentation - Apache Spark
May 19, 2025 · PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python. …
Downloads - Apache Spark
Download Spark: Verify this release using the and project release KEYS by following these procedures. Note that Spark 4 is pre-built with Scala 2.13, and support for Scala 2.12 has been …
Examples - Apache Spark
Spark is a great engine for small and large datasets. It can be used with single-node/localhost environments, or distributed clusters. Spark’s expansive API, excellent performance, and …
Spark SQL & DataFrames - Apache Spark
Seamlessly mix SQL queries with Spark programs. Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, …
Getting Started — PySpark 4.0.0 documentation - Apache Spark
Quickstart: Spark Connect. Launch Spark server with Spark Connect; Connect to Spark Connect server; Create DataFrame; Quickstart: Pandas API on Spark. Object Creation; Missing Data; …
Spark SQL and DataFrames - Spark 4.0.0 Documentation - Apache …
Spark SQL, DataFrames and Datasets Guide. Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide …
Apache Spark™ - Unified Engine for large-scale data analytics
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Overview - Spark 4.0.0 Documentation - Apache Spark
Running Spark Client Applications Anywhere with Spark Connect. Spark Connect is a new client-server architecture introduced in Spark 3.4 that decouples Spark client applications and allows …
Quick Start - Spark 4.0.0 Documentation - Apache Spark
Unlike the earlier examples with the Spark shell, which initializes its own SparkSession, we initialize a SparkSession as part of the program. To build the program, we also write a Maven …
Documentation - Apache Spark
The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Spark Streaming, and GraphX. In addition, this page lists other resources …
PySpark Overview — PySpark 4.0.0 documentation - Apache Spark
May 19, 2025 · PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python. …
Downloads - Apache Spark
Download Spark: Verify this release using the and project release KEYS by following these procedures. Note that Spark 4 is pre-built with Scala 2.13, and support for Scala 2.12 has been …
Examples - Apache Spark
Spark is a great engine for small and large datasets. It can be used with single-node/localhost environments, or distributed clusters. Spark’s expansive API, excellent performance, and …
Spark SQL & DataFrames - Apache Spark
Seamlessly mix SQL queries with Spark programs. Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, …
Getting Started — PySpark 4.0.0 documentation - Apache Spark
Quickstart: Spark Connect. Launch Spark server with Spark Connect; Connect to Spark Connect server; Create DataFrame; Quickstart: Pandas API on Spark. Object Creation; Missing Data; …
Spark SQL and DataFrames - Spark 4.0.0 Documentation
Spark SQL, DataFrames and Datasets Guide. Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide …
