Mastering Apache Storm
Mastering Apache Storm

Author: Ankit Jain

Publisher: Packt Publishing Ltd

Published: 2017-08-16

Total Pages: 276

ISBN-13: 1787120406

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Master the intricacies of Apache Storm and develop real-time stream processing applications with ease About This Book Exploit the various real-time processing functionalities offered by Apache Storm such as parallelism, data partitioning, and more Integrate Storm with other Big Data technologies like Hadoop, HBase, and Apache Kafka An easy-to-understand guide to effortlessly create distributed applications with Storm Who This Book Is For If you are a Java developer who wants to enter into the world of real-time stream processing applications using Apache Storm, then this book is for you. No previous experience in Storm is required as this book starts from the basics. After finishing this book, you will be able to develop not-so-complex Storm applications. What You Will Learn Understand the core concepts of Apache Storm and real-time processing Follow the steps to deploy multiple nodes of Storm Cluster Create Trident topologies to support various message-processing semantics Make your cluster sharing effective using Storm scheduling Integrate Apache Storm with other Big Data technologies such as Hadoop, HBase, Kafka, and more Monitor the health of your Storm cluster In Detail Apache Storm is a real-time Big Data processing framework that processes large amounts of data reliably, guaranteeing that every message will be processed. Storm allows you to scale your data as it grows, making it an excellent platform to solve your big data problems. This extensive guide will help you understand right from the basics to the advanced topics of Storm. The book begins with a detailed introduction to real-time processing and where Storm fits in to solve these problems. You'll get an understanding of deploying Storm on clusters by writing a basic Storm Hello World example. Next we'll introduce you to Trident and you'll get a clear understanding of how you can develop and deploy a trident topology. We cover topics such as monitoring, Storm Parallelism, scheduler and log processing, in a very easy to understand manner. You will also learn how to integrate Storm with other well-known Big Data technologies such as HBase, Redis, Kafka, and Hadoop to realize the full potential of Storm. With real-world examples and clear explanations, this book will ensure you will have a thorough mastery of Apache Storm. You will be able to use this knowledge to develop efficient, distributed real-time applications to cater to your business needs. Style and approach This easy-to-follow guide is full of examples and real-world applications to help you get an in-depth understanding of Apache Storm. This book covers the basics thoroughly and also delves into the intermediate and slightly advanced concepts of application development with Apache Storm.

Mastering Apache Spark 2.x
Mastering Apache Spark 2.x

Author: Romeo Kienzler

Publisher: Packt Publishing Ltd

Published: 2017-07-26

Total Pages: 345

ISBN-13: 178528522X

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

Stream Processing with Apache Spark
Stream Processing with Apache Spark

Author: Gerard Maas

Publisher: "O'Reilly Media, Inc."

Published: 2019-06-05

Total Pages: 396

ISBN-13: 1491944196

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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

Mastering Apache Hadoop
Mastering Apache Hadoop

Author: Cybellium Ltd

Publisher: Cybellium Ltd

Published: 2023-09-26

Total Pages: 194

ISBN-13:

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Unleash the Power of Big Data Processing with Apache Hadoop Ecosystem Are you ready to embark on a journey into the world of big data processing and analysis using Apache Hadoop? "Mastering Apache Hadoop" is your comprehensive guide to understanding and harnessing the capabilities of Hadoop for processing and managing massive datasets. Whether you're a data engineer seeking to optimize processing pipelines or a business analyst aiming to extract insights from large data, this book equips you with the knowledge and tools to master the art of Hadoop-based data processing. Key Features: 1. Deep Dive into Hadoop Ecosystem: Immerse yourself in the core components and concepts of the Apache Hadoop ecosystem. Understand the architecture, components, and functionalities that make Hadoop a powerful platform for big data. 2. Installation and Configuration: Master the art of installing and configuring Hadoop on various platforms. Learn about cluster setup, resource management, and configuration settings for optimal performance. 3. Hadoop Distributed File System (HDFS): Uncover the power of HDFS for distributed storage and data management. Explore concepts like replication, fault tolerance, and data placement to ensure data durability. 4. MapReduce and Data Processing: Delve into MapReduce, the core data processing paradigm in Hadoop. Learn how to write MapReduce jobs, optimize performance, and leverage parallel processing for efficient data analysis. 5. Data Ingestion and ETL: Discover techniques for ingesting and transforming data in Hadoop. Explore tools like Apache Sqoop and Apache Flume for extracting data from various sources and loading it into Hadoop. 6. Data Querying and Analysis: Master querying and analyzing data using Hadoop. Learn about Hive, Pig, and Spark SQL for querying structured and semi-structured data, and uncover insights that drive informed decisions. 7. Data Storage Formats: Explore data storage formats optimized for Hadoop. Learn about Avro, Parquet, and ORC, and understand how to choose the right format for efficient storage and retrieval. 8. Batch and Stream Processing: Uncover strategies for batch and real-time data processing in Hadoop. Learn how to use Apache Spark and Apache Flink to process data in both batch and streaming modes. 9. Data Visualization and Reporting: Discover techniques for visualizing and reporting on Hadoop data. Explore integration with tools like Apache Zeppelin and Tableau to create compelling visualizations. 10. Real-World Applications: Gain insights into real-world use cases of Apache Hadoop across industries. From financial analysis to social media sentiment analysis, explore how organizations are leveraging Hadoop's capabilities for data-driven innovation. Who This Book Is For: "Mastering Apache Hadoop" is an essential resource for data engineers, analysts, and IT professionals who want to excel in big data processing using Hadoop. Whether you're new to Hadoop or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of big data technology.

Mastering Hadoop 3
Mastering Hadoop 3

Author: Chanchal Singh

Publisher: Packt Publishing Ltd

Published: 2019-02-28

Total Pages: 531

ISBN-13: 1788628322

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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.

Mastering Hadoop
Mastering Hadoop

Author: Sandeep Karanth

Publisher: Packt Publishing Ltd

Published: 2014-12-29

Total Pages: 549

ISBN-13: 1783983655

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Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop.

Mastering Apache Pulsar
Mastering Apache Pulsar

Author: Jowanza Joseph

Publisher: "O'Reilly Media, Inc."

Published: 2021-12-06

Total Pages: 242

ISBN-13: 1492084859

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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

Spark: The Definitive Guide
Spark: The Definitive Guide

Author: Bill Chambers

Publisher: "O'Reilly Media, Inc."

Published: 2018-02-08

Total Pages: 594

ISBN-13: 1491912294

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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

Mastering Azure Analytics
Mastering Azure Analytics

Author: Zoiner Tejada

Publisher: "O'Reilly Media, Inc."

Published: 2017-04-06

Total Pages: 411

ISBN-13: 1491956623

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Helps users understand the breadth of Azure services by organizing them into a reference framework they can use when crafting their own big-data analytics solution.

Learning Spark
Learning Spark

Author: Jules S. Damji

Publisher: O'Reilly Media

Published: 2020-07-16

Total Pages: 400

ISBN-13: 1492050016

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Data is bigger, arrives faster, and comes in a variety of formats—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, you’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