Fundamentals: Real Time Analytics, Apache Kafka and Spark Streaming


Book Description

Mrs.Preethi.J, Assistant Professor, Department of Computer Applications, Dhanalakshmi Srinivasan College of Arts & Science for Women (Autonomous), Perambalur, Tamil Nadu, India. Dr.R.Srinivasan, Associate Professor & Head, Department of Computer Science, SLS MAVMM Ayira Vasiyar College, Kallampatti, Madurai, Tamil Nadu, India. Dr.S.Rasheed Mansoor Ali, Assistant Professor, Department of Computer Science, Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India. Mrs.M.Shiyamala, Department of Computer Applications, Dhanalakshmi Srinivasan College of Arts and Science for Women (Autonomous), Perambalur, Tamil Nadu, India.




Real-Time Streaming with Apache Kafka, Spark, and Storm


Book Description

Build a platform using Apache Kafka, Spark, and Storm to generate real-time data insights and view them through Dashboards. KEY FEATURES ● Extensive practical demonstration of Apache Kafka concepts, including producer and consumer examples. ● Includes graphical examples and explanations of implementing Kafka Producer and Kafka Consumer commands and methods. ● Covers integration and implementation of Spark-Kafka and Kafka-Storm architectures. DESCRIPTION Real-Time Streaming with Apache Kafka, Spark, and Storm is a book that provides an overview of the real-time streaming concepts and architectures of Apache Kafka, Storm, and Spark. The readers will learn how to build systems that can process data streams in real time using these technologies. They will be able to process a large amount of real-time data and perform analytics or generate insights as a result of this. The architecture of Kafka and its various components are described in detail. A Kafka Cluster installation and configuration will be demonstrated. The Kafka publisher-subscriber system will be implemented in the Eclipse IDE using the Command Line and Java. The book discusses the architecture of Apache Storm, the concepts of Spout and Bolt, as well as their applications in a Transaction Alert System. It also describes Spark's core concepts, applications, and the use of Spark to implement a microservice. To learn about the process of integrating Kafka and Storm, two approaches to Spark and Kafka integration will be discussed. This book will assist a software engineer to transition to a Big Data engineer and Big Data architect by providing knowledge of big data processing and the architectures of Kafka, Storm, and Spark Streaming. WHAT YOU WILL LEARN ● Creation of Kafka producers, consumers, and brokers using command line. ● End-to-end implementation of Kafka messaging system with Java in Eclipse. ● Perform installation and creation of a Storm Cluster and execute Storm Management commands. ● Implement Spouts, Bolts and a Topology in Storm for Transaction alert application system. ● Perform the implementation of a microservice using Spark in Scala IDE. ● Learn about the various approaches of integrating Kafka and Spark. ● Perform integration of Kafka and Storm using Java in the Eclipse IDE. WHO THIS BOOK IS FOR This book is intended for Software Developers, Data Scientists, and Big Data Architects who want to build software systems to process data streams in real time. To understand the concepts in this book, knowledge of any programming language such as Java, Python, etc. is needed. TABLE OF CONTENTS 1. Introduction to Kafka 2. Installing Kafka 3. Kafka Messaging 4. Kafka Producers 5. Kafka Consumers 6. Introduction to Storm 7. Installation and Configuration 8. Spouts and Bolts 9. Introduction to Spark 10. Spark Streaming 11. Kafka Integration with Storm 12. Kafka Integration with Spark




Kafka Streams in Action


Book Description

Summary Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort. Foreword by Neha Narkhede, Cocreator of Apache Kafka Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Not all stream-based applications require a dedicated processing cluster. The lightweight Kafka Streams library provides exactly the power and simplicity you need for message handling in microservices and real-time event processing. With the Kafka Streams API, you filter and transform data streams with just Kafka and your application. About the Book Kafka Streams in Action teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you'll explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You'll even dive into streaming SQL with KSQL! Practical to the very end, it finishes with testing and operational aspects, such as monitoring and debugging. What's inside Using the KStreams API Filtering, transforming, and splitting data Working with the Processor API Integrating with external systems About the Reader Assumes some experience with distributed systems. No knowledge of Kafka or streaming applications required. About the Author Bill Bejeck is a Kafka Streams contributor and Confluent engineer with over 15 years of software development experience. Table of Contents PART 1 - GETTING STARTED WITH KAFKA STREAMS Welcome to Kafka Streams Kafka quicklyPART 2 - KAFKA STREAMS DEVELOPMENT Developing Kafka Streams Streams and state The KTable API The Processor APIPART 3 - ADMINISTERING KAFKA STREAMS Monitoring and performance Testing a Kafka Streams applicationPART 4 - ADVANCED CONCEPTS WITH KAFKA STREAMS Advanced applications with Kafka StreamsAPPENDIXES Appendix A - Additional configuration information Appendix B - Exactly once semantics




Kafka: The Definitive Guide


Book Description

Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems




Stream Processing with Apache Spark


Book Description

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




Microsoft Certified Azure Data Fundamentals (DP-900) Exam Guide


Book Description

Boost your Azure career by mastering essential data concepts and cloud services with this pragmatic guide Purchase of this book unlocks access to web-based exam prep resources such as mock exams, flashcards, exam tips, and the eBook PDF Key Features Gain Azure certification insights from industry veteran and Microsoft MVP, Steve Miles Dive into expertly crafted content aligned with the latest DP-900 exam requirements Test your skills with mock exams that mirror the actual certification exam Book DescriptionMicrosoft's Azure Data Fundamentals (DP-900) certification exam validates your expertise in core data concepts and Azure’s powerful data services capabilities. This comprehensive guide written by Steve Miles—a Microsoft Azure MVP and certified trainer with over 25 years of experience in cloud data services and 30+ certifications across major platforms—serves as your gateway to a future shaped by data and AI, regardless of your technical background. With the help of examples, you'll learn fundamental data concepts, including data representation, data storage options, and common workloads and gain clarity on the roles and responsibilities of key data professionals such as data administrators, engineers, and analysts. This guide covers all crucial exam domains, from data services capabilities of the Azure cloud platform to considerations for relational, non-relational, and analytics workloads, encompassing both Microsoft and open-source technologies. To supplement your exam prep, this book gives you access to a suite of online resources designed to boost your confidence, including mock tests, interactive flashcards, and invaluable exam tips By the end of this book, you’ll be fully prepared not only to pass the DP-900 exam but also to confidently tackle data solutions in Azure, setting a strong foundation for your data-driven careerWhat you will learn Analyze features of structured, semi-structured, and unstructured data Utilize Azure SQL and open-source database services confidently Identify and evaluate Azure storage options Understand the versatility of Azure Cosmos DB through use cases and APIs Apply cutting-edge strategies for large-scale analytics in Azure Master core data concepts crucial for Azure environments Explore Microsoft's cloud services for real-time analytics Demonstrate proficiency in data visualization using Power BI Who this book is for This exam guide is designed for anyone who wants to work with Azure data services and prepare for the Azure DP-900 exam. Whether you're an administrator, engineer, architect, developer, analyst, aspiring data scientist, or a non-technical enthusiast interested in learning data concepts, this book is for you. It also lays the groundwork for those planning to pursue more advanced data or AI certifications. A foundational understanding of cloud concepts and client-server applications is assumed.




Mastering Kafka Streams and ksqlDB


Book Description

Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time. Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You'll learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing. Learn the basics of Kafka and the pub/sub communication pattern Build stateless and stateful stream processing applications using Kafka Streams and ksqlDB Perform advanced stateful operations, including windowed joins and aggregations Understand how stateful processing works under the hood Learn about ksqlDB's data integration features, powered by Kafka Connect Work with different types of collections in ksqlDB and perform push and pull queries Deploy your Kafka Streams and ksqlDB applications to production




Real-Time Analytics


Book Description

Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.




Practical Apache Spark


Book Description

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 Apache Spark also covers the integration of Apache Spark with Kafka with examples. You’ll follow a learn-to-do-by-yourself approach to learning – learn the concepts, practice the code snippets in Scala, and complete the assignments given to get an overall exposure. On completion, you’ll have knowledge of the functional programming aspects of Scala, and hands-on expertise in various Spark components. You’ll also become familiar with machine learning algorithms with real-time usage. What You Will LearnDiscover the functional programming features of Scala Understand the complete architecture of Spark and its componentsIntegrate Apache Spark with Hive and Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts and libraries using Spark's MLlib packages Who This Book Is For Developers and professionals who deal with batch and stream data processing.




Frank Kane's Taming Big Data with Apache Spark and Python


Book Description

Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace.