Mastering Apache Spark
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Mastering Apache Spark
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Author : Mike Frampton
language : en
Publisher:
Release Date : 2015
Mastering Apache Spark written by Mike Frampton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Data mining categories.
Gain expertise in processing and storing data by using advanced techniques with Apache SparkAbout 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 functionalitiesWho This Book Is ForIf 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 AWSIn DetailApache 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 approachThis 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.
Mastering Apache Spark 2 X
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Author : Romeo Kienzler
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-26
Mastering Apache Spark 2 X written by Romeo Kienzler and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-26 with Computers categories.
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.
Mastering Apache Spark 2 X
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Author : Romeo Kienzler
language : en
Publisher:
Release Date : 2017-07-20
Mastering Apache Spark 2 X written by Romeo Kienzler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-20 with Computers categories.
Advanced analytics on your Big Data with latest Apache Spark 2.xAbout 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.xWho This Book Is ForIf 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 projectsIn DetailApache 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 approachThis 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.
Mastering Apache Spark
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date : 2023-09-26
Mastering Apache Spark written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-26 with Computers categories.
Unleash the Potential of Distributed Data Processing with Apache Spark Are you prepared to venture into the realm of distributed data processing and analytics with Apache Spark? "Mastering Apache Spark" is your comprehensive guide to unlocking the full potential of this powerful framework for big data processing. Whether you're a data engineer seeking to optimize data pipelines or a business analyst aiming to extract insights from massive datasets, this book equips you with the knowledge and tools to master the art of Spark-based data processing. Key Features: 1. Deep Dive into Apache Spark: Immerse yourself in the core principles of Apache Spark, comprehending its architecture, components, and versatile functionalities. Construct a robust foundation that empowers you to manage big data with precision. 2. Installation and Configuration: Master the art of installing and configuring Apache Spark across diverse platforms. Learn about cluster setup, resource allocation, and configuration tuning for optimal performance. 3. Spark Core and RDDs: Uncover the core of Spark—Resilient Distributed Datasets (RDDs). Explore the functional programming paradigm and leverage RDDs for efficient and fault-tolerant data processing. 4. Structured Data Processing with Spark SQL: Delve into Spark SQL for querying structured data with ease. Learn how to execute SQL queries, perform data manipulations, and tap into the power of DataFrames. 5. Streamlining Data Processing with Spark Streaming: Discover the power of real-time data processing with Spark Streaming. Learn how to handle continuous data streams and perform near-real-time analytics. 6. Machine Learning with MLlib: Master Spark's machine learning library, MLlib. Dive into algorithms for classification, regression, clustering, and recommendation, enabling you to develop sophisticated data-driven models. 7. Graph Processing with GraphX: Embark on a journey through graph processing with Spark's GraphX. Learn how to analyze and visualize graph data to glean insights from complex relationships. 8. Data Processing with Spark Structured Streaming: Explore the world of structured streaming in Spark. Learn how to process and analyze data streams with the declarative power of DataFrames. 9. Spark Ecosystem and Integrations: Navigate Spark's rich ecosystem of libraries and integrations. From data ingestion with Apache Kafka to interactive analytics with Apache Zeppelin, explore tools that enhance Spark's capabilities. 10. Real-World Applications: Gain insights into real-world use cases of Apache Spark across industries. From fraud detection to sentiment analysis, discover how organizations leverage Spark for data-driven innovation. Who This Book Is For: "Mastering Apache Spark" is a must-have resource for data engineers, analysts, and IT professionals poised to excel in the world of distributed data processing using Spark. Whether you're new to Spark or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of this transformative framework.
Mastering Apache Spark
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Author : Ted Noreux
language : en
Publisher: Independently Published
Release Date : 2024-03-10
Mastering Apache Spark written by Ted Noreux and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-10 with Computers categories.
"Mastering Apache Spark: Essential Techniques" offers a deep dive into the world of Apache Spark, designed for professionals aiming to enhance their skills in handling big data processing tasks. This comprehensive guide covers the entire Spark ecosystem, from fundamental concepts like RDDs and DataFrames to advanced topics including stream processing with Spark Structured Streaming and machine learning with Spark MLlib. Structured in a clear, logical manner, the book guides readers through Apache Spark's architecture, setup and configuration, data processing, querying structured data, performance tuning, deployment, and monitoring. Each chapter is packed with practical examples, case studies, and best practices to ensure the reader not only understands the theoretical underpinnings but also knows how to apply them in real-world scenarios. Whether you're a software engineer, data scientist, data engineer, or analyst, "Mastering Apache Spark: Essential Techniques" serves as an essential resource to harness the power of Apache Spark effectively, optimize your data processing tasks, and achieve scalable, high-performance data analytics solutions. Embrace the opportunity to master Apache Spark and take your big data processing skills to the next level.
Mastering Apache Spark
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Author : Greyson Chesterfield
language : en
Publisher: Independently Published
Release Date : 2024-12-09
Mastering Apache Spark written by Greyson Chesterfield and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-09 with Computers categories.
Unlock the power of big data with Mastering Apache Spark: Real-Time Big Data Analytics! This comprehensive guide is your ultimate resource for building, processing, and analyzing large-scale data using Apache Spark, the fast, flexible, and powerful open-source framework for big data processing. Whether you're a data engineer, scientist, or analyst, this book will teach you how to harness Spark's real-time analytics capabilities to process and analyze massive datasets. Apache Spark is widely used for its speed, ease of use, and scalability. It's the go-to solution for building data pipelines, running machine learning algorithms, and processing streams of real-time data. In this book, you'll learn everything from the fundamentals of Spark to advanced techniques for scaling your big data workflows. What's Inside: Getting Started with Apache Spark: Learn the core concepts behind Apache Spark, including Spark RDDs, DataFrames, and Spark SQL, and how to set up Spark on your system or in the cloud. Real-Time Data Processing: Dive into real-time data processing with Spark Streaming, handling live data streams, and building real-time analytics applications. Building Data Pipelines: Learn how to design and implement scalable data pipelines that can process large volumes of structured and unstructured data. Data Analytics with Spark: Explore how to analyze big data using Spark's powerful libraries, including Spark MLlib for machine learning and Spark GraphX for graph processing. Optimizing Spark Performance: Discover strategies to optimize Spark performance, including partitioning, caching, and using the Catalyst optimizer for SQL queries. Advanced Spark Topics: Get hands-on with advanced topics like Spark on Kubernetes, Spark integration with Hadoop, and deploying Spark on cloud platforms such as AWS and Azure. Batch vs. Stream Processing: Learn when to use batch processing and when to go for stream processing for different use cases in data analytics. Use Cases and Real-World Applications: Explore real-world use cases for Spark in industries like finance, healthcare, e-commerce, and IoT. By the end of this book, you'll be equipped with the knowledge and hands-on experience to build efficient, scalable data pipelines and perform advanced real-time big data analytics using Apache Spark. Ready to master big data with Spark? Grab your copy now and start building powerful, high-performance data solutions that scale with your business needs!
Stream Processing With Apache Spark
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Author : Gerard Maas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-06-05
Stream Processing With Apache Spark written by Gerard Maas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-05 with Computers categories.
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 in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams
Advanced Hadoop Techniques A Comprehensive Guide To Mastery
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-05-13
Advanced Hadoop Techniques A Comprehensive Guide To Mastery written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-13 with Computers categories.
Unlock the full potential of Hadoop with "Advanced Hadoop Techniques: A Comprehensive Guide to Mastery"—your essential resource for navigating the intricate complexities and harnessing the tremendous power of the Hadoop ecosystem. Designed for data engineers, developers, administrators, and data scientists, this book elevates your skills from foundational concepts to the most advanced optimizations necessary for mastery. Delve deep into the core of Hadoop, unraveling its integral components such as HDFS, MapReduce, and YARN, while expanding your knowledge to encompass critical ecosystem projects like Hive, HBase, Sqoop, and Spark. Through meticulous explanations and real-world examples, "Advanced Hadoop Techniques: A Comprehensive Guide to Mastery" equips you with the tools to efficiently deploy, manage, and optimize Hadoop clusters. Learn to fortify your Hadoop deployments by implementing robust security measures to ensure data protection and compliance. Discover the intricacies of performance tuning to significantly enhance your data processing and analytics capabilities. This book empowers you to not only learn Hadoop but to master sophisticated techniques that convert vast data sets into actionable insights. Perfect for aspiring professionals eager to make an impact in the realm of big data and seasoned experts aiming to refine their craft, "Advanced Hadoop Techniques: A Comprehensive Guide to Mastery" serves as an invaluable resource. Embark on your journey into the future of big data with confidence and expertise—your path to Hadoop mastery starts here.
Mastering Spark With R
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Author : Javier Luraschi
language : en
Publisher:
Release Date : 2019
Mastering Spark With R written by Javier Luraschi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Big data categories.
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.
Mastering Spark With R
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Author : Javier Luraschi
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-10-07
Mastering Spark With R written by Javier Luraschi and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-07 with Computers categories.
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