Stream Processing With Apache Flink

Author: Fabian Hueske
Publisher: O'Reilly Media
ISBN: 9781491974292
Size: 20.82 MB
Format: PDF, Kindle
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Get started with Apache Flink, the open source framework that enables you to process streaming data—such as user interactions, sensor data, and machine logs—as it arrives. With this practical guide, you’ll learn how to use Apache Flink’s stream processing APIs to implement, continuously run, and maintain real-world applications. Authors Fabian Hueske, one of Flink’s creators, and Vasia Kalavri, a core contributor to Flink’s graph processing API (Gelly), explains the fundamental concepts of parallel stream processing and shows you how streaming analytics differs from traditional batch data analysis. Software engineers, data engineers, and system administrators will learn the basics of Flink’s DataStream API, including the structure and components of a common Flink streaming application. Solve real-world problems with Apache Flink’s DataStream API Set up an environment for developing stream processing applications for Flink Design streaming applications and migrate periodic batch workloads to continuous streaming workloads Learn about windowed operations that process groups of records Ingest data streams into a DataStream application and emit a result stream into different storage systems Implement stateful and custom operators common in stream processing applications Operate, maintain, and update continuously running Flink streaming applications Explore several deployment options, including the setup of highly available installations

Introduction To Apache Flink

Author: Ellen Friedman
Publisher: "O'Reilly Media, Inc."
ISBN: 1491977167
Size: 73.82 MB
Format: PDF, ePub
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There’s growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing data streams at scale has been difficult to do well—until now. This practical book delivers a deep introduction to Apache Flink, a highly innovative open source stream processor with a surprising range of capabilities. Authors Ellen Friedman and Kostas Tzoumas show technical and nontechnical readers alike how Flink is engineered to overcome significant tradeoffs that have limited the effectiveness of other approaches to stream processing. You’ll also learn how Flink has the ability to handle both stream and batch data processing with one technology. Learn the consequences of not doing streaming well—in retail and marketing, IoT, telecom, and banking and finance Explore how to design data architecture to gain the best advantage from stream processing Get an overview of Flink’s capabilities and features, along with examples of how companies use Flink, including in production Take a technical dive into Flink, and learn how it handles time and stateful computation Examine how Flink processes both streaming (unbounded) and batch (bounded) data without sacrificing performance

Practical Real Time Data Processing And Analytics

Author: Shilpi Saxena
Publisher: Packt Publishing Ltd
ISBN: 1787289869
Size: 33.40 MB
Format: PDF, Mobi
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A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenario About This Book Learn about the various challenges in real-time data processing and use the right tools to overcome them This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time Who This Book Is For If you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great. What You Will Learn Get an introduction to the established real-time stack Understand the key integration of all the components Get a thorough understanding of the basic building blocks for real-time solution designing Garnish the search and visualization aspects for your real-time solution Get conceptually and practically acquainted with real-time analytics Be well equipped to apply the knowledge and create your own solutions In Detail With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you'll be equipped with a clear understanding of how to solve challenges on your own. We'll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You'll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner. Style and Approach In this practical guide to real-time analytics, each chapter begins with a basic high-level concept of the topic, followed by a practical, hands-on implementation of each concept, where you can see the working and execution of it. The book is written in a DIY style, with plenty of practical use cases, well-explained code examples, and relevant screenshots and diagrams.

Fundamentals Of Stream Processing

Author: Henrique C. M. Andrade
Publisher: Cambridge University Press
ISBN: 1107015545
Size: 23.49 MB
Format: PDF, Docs
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This book teaches fundamentals of stream processing, covering application design, distributed systems infrastructure, and continuous analytic algorithms.

Learning Apache Flink

Author: Tanmay Deshpande
Publisher: Packt Publishing Ltd
ISBN: 1786467267
Size: 61.97 MB
Format: PDF, ePub, Docs
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Discover the definitive guide to crafting lightning-fast data processing for distributed systems with Apache Flink About This Book Build your expertize in processing real-time data with Apache Flink and its ecosystem Gain insights into the working of all components of Apache Flink such as FlinkML, Gelly, and Table API filled with real world use cases Exploit Apache Flink's capabilities like distributed data streaming, in-memory processing, pipelining and iteration operators to improve performance. Solve real world big-data problems with real time in-memory and disk-based processing capabilities of Apache Flink. Who This Book Is For Big data developers who are looking to process batch and real-time data on distributed systems. Basic knowledge of Hadoop and big data is assumed. Reasonable knowledge of Java or Scala is expected. What You Will Learn Learn how to build end to end real time analytics projects Integrate with existing big data stack and utilize existing infrastructure Build predictive analytics applications using FlinkML Use graph library to perform graph querying and search. Understand Flink's - "Streaming First" architecture to implementing real streaming applications Learn Flink Logging and Monitoring best practices in order to efficiently design your data pipelines Explore the detailed processes to deploy Flink cluster on Amazon Web Services(AWS) and Google Cloud Platform (GCP). In Detail With the advent of massive computer systems, organizations in different domains generate large amounts of data on a real-time basis. The latest entrant to big data processing, Apache Flink, is designed to process continuous streams of data at a lightning fast pace. This book will be your definitive guide to batch and stream data processing with Apache Flink. The book begins with introducing the Apache Flink ecosystem, setting it up and using the DataSet and DataStream API for processing batch and streaming datasets. Bringing the power of SQL to Flink, this book will then explore the Table API for querying and manipulating data. In the latter half of the book, readers will get to learn the remaining ecosystem of Apache Flink to achieve complex tasks such as event processing, machine learning, and graph processing. The final part of the book would consist of topics such as scaling Flink solutions, performance optimization and integrating Flink with other tools such as ElasticSearch. Whether you want to dive deeper into Apache Flink, or want to investigate how to get more out of this powerful technology, you'll find everything you need inside. Style and approach This book is a comprehensive guide that covers advanced features of the Apache Flink, and communicates them with a practical understanding of the underlying concepts for how, when, and why to use them.

Streaming Architecture

Author: Ted Dunning
Publisher: "O'Reilly Media, Inc."
ISBN: 149195390X
Size: 38.58 MB
Format: PDF
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More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you'll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm. Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases. Ideal for developers and non-technical people alike, this book describes: Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layerNew messaging technologies, including Apache Kafka and MapR Streams, with links to sample codeTechnology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache ApexHow stream-based architectures are helpful to support microservicesSpecific use cases such as fraud detection and geo-distributed data streams Ted Dunning is Chief Applications Architect at MapR Technologies, and active in the open source community. He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and as committer and PMC member of the Apache ZooKeeper and Drill projects. Ted is on Twitter as @ted_dunning. Ellen Friedman, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics. Ellen is on Twitter as @Ellen_Friedman.

Building Data Streaming Applications With Apache Kafka

Author: Manish Kumar
Publisher: Packt Publishing Ltd
ISBN: 1787287637
Size: 63.90 MB
Format: PDF, Mobi
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Design and administer fast, reliable enterprise messaging systems with Apache Kafka About This Book Build efficient real-time streaming applications in Apache Kafka to process data streams of data Master the core Kafka APIs to set up Apache Kafka clusters and start writing message producers and consumers A comprehensive guide to help you get a solid grasp of the Apache Kafka concepts in Apache Kafka with pracitcalpractical examples Who This Book Is For If you want to learn how to use Apache Kafka and the different tools in the Kafka ecosystem in the easiest possible manner, this book is for you. Some programming experience with Java is required to get the most out of this book What You Will Learn Learn the basics of Apache Kafka from scratch Use the basic building blocks of a streaming application Design effective streaming applications with Kafka using Spark, Storm &, and Heron Understand the importance of a low -latency , high- throughput, and fault-tolerant messaging system Make effective capacity planning while deploying your Kafka Application Understand and implement the best security practices In Detail Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur. This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security. By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it. Style and approach A step-by –step, comprehensive guide filled with practical and real- world examples

I Heart Logs

Author: Jay Kreps
Publisher: "O'Reilly Media, Inc."
ISBN: 1491909331
Size: 52.42 MB
Format: PDF, ePub, Mobi
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Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention. Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models. Go ahead and take the plunge with logs; you’re going love them. Learn how logs are used for programmatic access in databases and distributed systems Discover solutions to the huge data integration problem when more data of more varieties meet more systems Understand why logs are at the heart of real-time stream processing Learn the role of a log in the internals of online data systems Explore how Jay Kreps applies these ideas to his own work on data infrastructure systems at LinkedIn

Agile Swift

Author: Godfrey Nolan
Publisher: Apress
ISBN: 1484221028
Size: 17.33 MB
Format: PDF, ePub, Docs
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Make your Swift apps agile and sound with this short step by step guide. You'll learn about unit testing, mocking and continuous integration and how to get these key ingredients running in your Swift projects. This book also looks at how to write your Swift apps using test driven development (TDD). Agile practices have made major inroads in iOS development, however it’s very unusual to see something as basic as unit testing on a Swift application. Done correctly, Agile development results in a significant increase in development efficiency and a reduction in the number of defects. Apple has released unit testing and code coverage frameworks for Swift development in XCode. Up until now getting unit testing up and running in Swift was not for the faint-hearted. Thankfully now, there is no excuse other than a lack of information on where to get started. iOS developers are faced with their own set of problems such as tightly coupled code, fragmentation, immature testing tools all of which can be solved using existing Agile tools and techniques. Swift Programming Using Agile Tools and Techniques is your solution to handling these tasks. What You Will Learn Write unit tests in Swift Write an application using test driven development Examine GUI testing, refactoring, and mocking frameworks Set up and configure a continuous integration server Measure code coverage Who This Book Is For Swift developers and would be mobile app testers will benefit from the guidance in this book.