Apache Spark

O’Reilly Media, 2018. — 608 p. — ISBN: 978-1491912218. Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of this 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...

O’Reilly Media, 2015. — 274 p. — e-ISBN: 978-1-4493-5904-1, ISBN10: 1-4493-5904-3. Data in all domains is getting bigger. How can you work with it efficiently? 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,...

O’Reilly Media, 2017. — 352 p. — ISBN: 978-1491960110. Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to...

O’Reilly Media, 2017. — 358 p. — ISBN: 978-1491943205. True PDF Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries...

2nd Edition. — O’Reilly Media, 2020. — 398 р. Data is getting bigger, arriving faster, and coming in varied formats—and it all needs to be processed at scale for analytics or machine learning. How can you process such varied data workloads efficiently? Enter Apache Spark. Updated to emphasize new features in Spark 2.x., this second edition shows data engineers and scientists...

Apress, 2016. — 296 p. — ISBN: 9781484221747 This book is about how to integrate full-stack open source big data architecture and how to choose the correct technology—Scala/Spark, Mesos, Akka, Cassandra, and Kafka—in every layer. Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting,...

2nd ed. — O’Reilly Media, 2017. — 283 p. — ASIN B072KFWZ8S. In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an...

Packt Publishing, 2015. — 338 p. — e-ISBN: 978-1-78328-852-6, ISBN10: 1-78328-852-3 Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease...

2nd Edition. — Manning Publications, 2020. — 577 p. — ISBN: 978-1617295522. Spark in Action, Second Edition is an entirely new book that teaches you everything you need to create end-to-end analytics pipelines in Spark. Rewritten from the ground up with lots of helpful graphics, you’ll learn the roles of DAGs and dataframes, the advantages of “lazy evaluation”, and ingestion...

Packt Publishing, 2016. — 326 p. — ISBN: 9781785884696 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...

O’Reilly Media, 2015. — 275 p. — ISBN: 1491912766, 9781491912768 Ryza S., Laserson U., Owen S., Wills J. In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems...

Учебно-методическое пособие. — СПб.: Университет ИТМО, 2019. — 50 с. Учебно-методическое пособие содержит теоретический материал и примеры выполнения задач для курса «Введение в технологии обработки больших данных». Пособие составлено с учётом проведения лабораторных работ с помощью фреймворка Apache Spark. Содержание дисциплины охватывает круг вопросов, связанных с организацией...

Packt Publishing, 2017. — 356 p. — ISBN: 978-1785885136. True PDF Key Features 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. Book...

Apress Media LLC, 2022. — 595 p. — ISBN-13: 978-1-4842-7451-4. Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with...

Sams Publishing, 2017. — 592 p. — ISBN13: 978-0-672-33851-9. Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. In just 24 lessons of one hour or less, Sams Teach Yourself Apache Spark in 24 Hours helps you build practical Big Data solutions that...

O’Reilly Media, 2019. — 452 p. — ISBN13: 978-1-491-94424-0. Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You’ll discover how Spark enables you to write streaming jobs...

Packt Publishing, 2017. — 666 p. — ASIN B01BKL1PD8. 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...

O’Reilly, 2020. — 367 p. — ISBN: 1-492-04776-6. If you want to build an enterprise-quality application that uses natural language text but aren’t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language...

Packt Publishing, 2015. - 206p. Looking for a cluster computing system that provides high-level APIs? Apache Spark is your answer—an open source, fast, and general purpose cluster computing system. Spark's multi-stage memory primitives provide performance up to 100 times faster than Hadoop, and it is also well-suited for machine learning algorithms. Are you a Python developer...

O’Reilly Media, 2022. — 435 p. — ISBN 1492082384. 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...

Apress, 2016. — 231 р. — ISBN: 978-1-4842-4800. Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. Pro Spark Streaming walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter...

Packt Publishing, 2016. — 339 p. — ISBN: 1785885650. — ASIN: B01CGKAILW. Key Features Perform data analysis and build predictive models on huge datasets that leverage Apache Spark Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges Work through practical examples on real-world problems...

Packt, 2019. — 322 p. — ISBN: 1788994613. Speed up the design and implementation of deep learning solutions using Apache Spark Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep...

Manning Publications, 2016. — 282 p. in color. — ISBN: 1617292524, 9781617292521 Spark GraphX in Action starts out with an overview of Apache Spark and the GraphX graph processing API. This example-based tutorial then teaches you how to configure GraphX and how to use it interactively. Along the way, you'll collect practical techniques for enhancing applications and applying...

Packt Publishing, 2018. — 142 p. — ASIN B07HRTNFZ9. No need to spend hours ploughing through endless data – let Spark, one of the fastest big data processing engines available, do the hard work for you. Key Features Get up and running with Apache Spark and Python Integrate Spark with AWS for real-time analytics Apply processed data streams to machine learning APIs of Apache...

ДМК Пресс, 2015. — 303 c. — ISBN: 5970603236, 9785970603239 Объем обрабатываемых данных во всех областях человеческой деятельности продолжает расти быстрыми темпами. Существуют ли эффективные приемы работы с ним? В этой книге рассказывается об Apache Spark, открытой системе кластерных вычислений, которая позволяет быстро создавать высокопроизводительные программы анализа...

Packt Publishing, 2017. — 350 p. — ASIN B01LY3N7ZO. Key Features Perform big data processing with Spark—without having to learn Scala! Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark Book Description...

Packt Publishing, 2017. — 294 p. — ISBN13: 9781787127265. True PDF Over 70 recipes to help you use Apache Spark as your single big data computing platform and master its libraries. 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...

Manning Publications, 2016. — 472 p. Big data systems distribute datasets across clusters of machines, making it a challenge to efficiently query, stream, and interpret them. Spark can help. It is a processing system designed specifically for distributed data. It provides easy-to-use interfaces, along with the performance you need for production-quality analytics and machine...

Springer, 2018. — 274 p. — ISBN: 9811305498. The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD...

Packt Publishing, 2015. - 318p. 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...

Packt Publishing, 2017. — 452 p. — ISBN: 978-1-78588-835-9. Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build...

2nd Edition. — Apress Media, LLC, 2021. — 445 p. — ISBN-13: 978-1-4842-7382-1. Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and...

Packt, 2016. — 501 p. — ISBN: 978-1-78588-874-8 True PDF Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2.0.0 Use Spark's machine learning library in a big data environment You will learn how to develop high-value applications at scale with ease and a develop a personalized design Who This...

Мануал от компании Databricks по использованию Apache Spark. Log Analysis with Spark Introduction to Apache Spark Importing Data Exporting Data Log Analyzer Application Twitter Streaming Language Classifier Collect a Dataset of Tweets Examine the Tweets and Train a Model Apply the Model in Real-time

Apress, 2018. — 375 p. — ISBN: 978-1-4842-2148-8. See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together. In the , the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that,...

Apress, 2018. — 393 p. — ISBN: 978-1484235782. Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it. Along the way, you’ll...

Packt Publishing, 2016. — 392 p. — ISBN: 1785880101. True PDF 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...

Packt Publishing Ltd., Birmingham, UK, 2016. — 325 p. — ISBN: 9781785884696. A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets,...

Apress Media, LLC, 2023. — 416 p. — ISBN-13: 978-1-4842-9379-9. This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark’s structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows. This book covers Spark 3’s new...

Packt Publishing, 2017. — 550 p. Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products. Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need...

2nd Edition. — Packt Publishing, 2017. — 345 p. Apache Spark is an in-memory, cluster-based, parallel processing system that provides a wide range of functionality such as graph processing, machine learning, stream processing, and SQL. This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand your Spark functionality. The book opens...

Packt Publishing, 2016. - 252p. - ASIN: B01GEUF1H6 True PDF Key Features Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development Develop a set of practical Machine Learning applications that can be implemented in real-life projects A comprehensive, project-based guide to improve and...

Independently published, 2021. — 301 p. — ASIN B0959QYBSW. Distributed Processing for Massive Datasets About the Author About the Technical Reviewer Part I: Getting Started Understanding Apache Spark An Example The Core Use Cases Transform Your Data Analyze Your Data Machine Learning NET for Apache Spark Feature Parity Setting Up Spark Choosing Your Software Versions Choosing a...

Apress, 2020. — 281 p. — ISBN13: 978-1-4842-5780-7. Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical...

Packt Publishing, 2017. — 296 p. — ASIN B071VVFDMP. True PDF +Sample files Key Features 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 Book Description Frank Kane's Taming Big Data with Apache Spark...

NY: InfoQ, 2018. — 104 p. Apache Spark is an open-source big-data processing framework built around speed, ease of use, and sophisticated analytics. Spark has several advantages compared to other big-data and MapReduce technologies like Hadoop and Storm. It provides a comprehensive, unified framework with which to manage big-data processing requirements for datasets that are...

Apress, 2019. — 288 p. — ISBN: 1484236513, 9781484236512. Work with Apache Spark using Scala to deploy and set up single-node, multi-node, and high-availability clusters. This book discusses various components of Spark such as Spark Core, DataFrames, Datasets and SQL, Spark Streaming, Spark MLib, and R on Spark with the help of practical code snippets for each topic. Practical...

2nd ed. — Packt Publishing, 2017. — 532 p. — ISBN: 978-1-78588-993-6. True PDF Create scalable machine learning applications to power a modern data-driven business using Spark 2.x This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will...

O’Reilly Media, 2019. — 156 р. — ISBN: 1491944242. To build analytics tools that provide faster insights, knowing how to process data in real time is a must, and moving from batch processing to stream processing is absolutely required. Fortunately, the Spark in-memory framework/platform for processing data has added an extension devoted to fault-tolerant stream processing:...

2nd Edition. — Manning Publications, 2020. — 629 p. — ISBN: 978-1617295522. Spark in Action, Second Edition is an entirely new book that teaches you everything you need to create end-to-end analytics pipelines in Spark. Rewritten from the ground up with lots of helpful graphics, you’ll learn the roles of DAGs and dataframes, the advantages of “lazy evaluation”, and ingestion...

2nd Edition (Second Early Release) — O’Reilly Media, 2024. — 350 p. — ISBN: 9780137957002. Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected or still don't feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau, Rachel Warren, and Anya Bida walk you through the...

Packt Publishing, 2017. — 350 p. — ISBN: 978-1-78712-649-7. Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark...

Independently published, 2020. — 154 p. Nowadays the internet is an integral part of our life, right from the waking moment we indulge in the world of the internet like creating a Facebook post or watch a YouTube video or so, and in this process we tend to create data. And think of it as the entire human population participating in this process of creating data every day, every...

True PDF Packt Publishing, 2016. — 322 p. — ISBN: 978-1-78588-500-6. Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists. This book starts with the fundamentals of Spark 2 and covers the core data processing framework and...

Indianapolis, IN : Wiley, 2016. — 205 p. — ISBN: 978-1-119-25404-1. Spark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from...

O’Reilly Media, 2017. — 358 p. — ISBN: 978-1-491-94320-5. Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run...

Packt Publishing, 2017. — 323 p. — ISBN: 978-1-78528-345-1. Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key...

Packt Publishing, 2021. — 480 p. — ISBN 1801077746, 9781801077743. Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key Features Become well-versed with the core concepts of Apache Spark and Delta Lake for building data platforms Learn how to...

Packt Publishing, 2016. — 322 p. — ISBN: 1785885006. Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools which that are equally useful for application developers as well as data scientists. SparkR or “R on Spark” in the Spark framework opened the door of Spark data processing capability to the R...

Sussex: Apress, 2021. — 269 р. — ISBN: 978-1-4842-6991-6 Get started using Apache Spark via C# or F# and the .NET for Apache Spark bindings. This book is an introduction to both Apache Spark and the .NET bindings. Readers new to Apache Spark will get up to speed quickly using Spark for data processing tasks performed against large and very large datasets. You will learn how to...

Packt Publishing, 2017. — 874 p. — ISBN10: 1785280848, 13 978-1785280849. 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...

3rd Ed. — Packt Publishing, 2016. — 269 p. — ISBN: 1785889273. 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...

SIGMOD '15 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Spark SQL is a new module in Apache Spark that integrates relational processing with Spark’s functional programming API. Built on our experience with Shark, Spark SQL lets Spark programmers leverage the benefits of relational processing (e.g., declarative queries and optimized storage),...

Packt Publishing, 2021. — 414 p. — ISBN 978-1838647216. Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key Features Get to grips with the distributed training and deployment of machine learning and deep learning models Learn how ETLs are integrated with Azure Data Factory and Delta Lake Explore deep learning and machine learning...

СПб.: Питер, 2017. — 272 с. В этой практичной книге четверо специалистов Cloudera по анализу данных описывают самодостаточные паттерны для выполнения крупномасштабного анализа данных при помощи Spark. Авторы комплексно рассматривают Spark, статистические методы и множества данных, собранные в реальных условиях, и на этих примерах демонстрируют решения распространенных...

Syncfusion Inc., 2015. — 111 p. Mastering big data requires an aptitude at every step of information processing. Post-processing, one of the most important steps, is where you find Apache Spark frequently employed. Spark Succinctly, by Marko Švaljek, addresses Spark’s use in the ultimate step in handling big data. Topics included: - Introduction - Installing Spark - Hello...

Packt Publishing, 2017. — 323 p. — ISBN: 978-1-78528-345-1. Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key...

BPB Publications, 2022. — 554 p. — ISBN: 978-93-91392-086. This book provides the reader with an up-to-date explanation of Machine Learning and an in-depth, comprehensive, and straightforward understanding of the architectural techniques used to evaluate and anticipate the futuristic insights of data using Apache Spark.

Apress, 2020. — 281 p. — ISBN: 9781484257814. Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics...

Birmingham: Packt Publishing, 2025. — 292 p. — ISBN 1803232250. Master the fundamentals of time series analysis with Apache Spark and Databricks and uncover actionable insights at scale. Key Features Quickly get started with your first models and explore the potential of Generative AI. Learn how to use Apache Spark and Databricks for scalable time series solutions. Establish...

Packt Publishing, 2018. — 474 p. — ISBN: 978-1788474221. 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...

Packt, 2019 — 322 p. — ISBN: 1788994613. Speed up the design and implementation of deep learning solutions using Apache Spark Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep...

Packt Publishing, 2019. — 233 p. — ISBN: 978-1-78934-656-5. Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Every person and every organization in the world manages data, whether they realize it or...

Packt Publishing, 2019. — 233 p. — ISBN: 978-1-78934-656-5. Code files only! Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Every person and every organization in the world manages data, whether...

СПб.: Питер, 2018. — 352 с.: ил. — (Бестселлеры O’Reilly). — ISBN: 978-5-4461-0705-6. Если у вас уже есть положительный опыт использования Spark для решения небольших задач, но вы по-прежнему ломаете голову – где та самая непревзойденная производительность Spark, позволяющая перемалывать колоссальные объемы данных – то эта книга для вас. Она расскажет, как эффективно...

М.: ДМК Пресс, 2015. — 304 с. Объем обрабатываемых данных во всех областях человеческой деятельности продолжает расти быстрыми темпами. Существуют ли эффективные приемы работы с ним? В этой книге рассказывается об Apache Spark, открытой системе кластерных вычислений, которая позволяет быстро создавать высокопроизводительные программы анализа данных. С помощью Spark вы сможете...

М.: ДМК Пресс, 2020. — 636 с. — ISBN: 978-5-97060-879-1. В этой книге подробно рассматривается организация обработки больших данных с использованием аналитической операционной системы Apache Spark. Тщательно описываются процессы потребления, преобразования и публикации результатов обработки данных; продемонстрированы возможности Apache Spark при работе с разнообразными...

М.: ДМК Пресс, 2021. — 637 c. — ISBN 978-5-97060-879-1. Анализ корпоративных данных начинается с чтения, фильтрации и объединения файлов и потоков из многих источников. Механизм обработки данных Spark способен обрабатывать эти разнообразные объемы информации как признанный лидер в этой области, обеспечивая в 100 раз большую скорость, чем например Hadoop. Благодаря поддержке...