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 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
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.
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
Apache Spark 2 X For Java Developers
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Author : Sourav Gulati
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-07-26
Apache Spark 2 X For Java Developers written by Sourav Gulati 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.
Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java About This Book 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 Who This Book Is For If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful. What You Will Learn Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark In Detail 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 features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications. Style and approach This practical guide teaches readers the fundamentals of the Apache Spark framework and how to implement components using the Java language. It is a unique blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.
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.
Learning Spark
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Author : Jules S. Damji
language : en
Publisher: O'Reilly Media
Release Date : 2020-07-16
Learning Spark written by Jules S. Damji and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-16 with Computers categories.
Data is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, youâ??ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow
Mastering Apache Pulsar
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Author : Jowanza Joseph
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-06
Mastering Apache Pulsar written by Jowanza Joseph 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 2021-12-06 with Computers categories.
Every enterprise application creates data, including log messages, metrics, user activity, and outgoing messages. Learning how to move these items is almost as important as the data itself. If you're an application architect, developer, or production engineer new to Apache Pulsar, this practical guide shows you how to use this open source event streaming platform to handle real-time data feeds. Jowanza Joseph, staff software engineer at Finicity, explains how to deploy production Pulsar clusters, write reliable event streaming applications, and build scalable real-time data pipelines with this platform. Through detailed examples, you'll learn Pulsar's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the load manager, and the storage layer. This book helps you: Understand how event streaming fits in the big data ecosystem Explore Pulsar producers, consumers, and readers for writing and reading events Build scalable data pipelines by connecting Pulsar with external systems Simplify event-streaming application building with Pulsar Functions Manage Pulsar to perform monitoring, tuning, and maintenance tasks Use Pulsar's operational measurements to secure a production cluster Process event streams using Flink and query event streams using Presto
Mastering Machine Learning On Aws
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Author : Dr. Saket S.R. Mengle
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20
Mastering Machine Learning On Aws written by Dr. Saket S.R. Mengle 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 2019-05-20 with Computers categories.
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Mastering Hadoop 3
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Author : Chanchal Singh
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28
Mastering Hadoop 3 written by Chanchal Singh 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 2019-02-28 with Computers categories.
A comprehensive guide to mastering the most advanced Hadoop 3 concepts Key FeaturesGet to grips with the newly introduced features and capabilities of Hadoop 3Crunch and process data using MapReduce, YARN, and a host of tools within the Hadoop ecosystemSharpen your Hadoop skills with real-world case studies and codeBook Description Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines. What you will learnGain an in-depth understanding of distributed computing using Hadoop 3Develop enterprise-grade applications using Apache Spark, Flink, and moreBuild scalable and high-performance Hadoop data pipelines with security, monitoring, and data governanceExplore batch data processing patterns and how to model data in HadoopMaster best practices for enterprises using, or planning to use, Hadoop 3 as a data platformUnderstand security aspects of Hadoop, including authorization and authenticationWho this book is for If you want to become a big data professional by mastering the advanced concepts of Hadoop, this book is for you. You’ll also find this book useful if you’re a Hadoop professional looking to strengthen your knowledge of the Hadoop ecosystem. Fundamental knowledge of the Java programming language and basics of Hadoop is necessary to get started with this book.