Stream Processing With Apache Flink

Author: Fabian Hueske
Publisher: O'Reilly Media
ISBN: 9781491974292
Size: 49.66 MB
Format: PDF, Docs
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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: 72.27 MB
Format: PDF, Kindle
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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: 45.75 MB
Format: PDF, ePub, Docs
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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.

Learning Apache Flink

Author: Tanmay Deshpande
Publisher: Packt Publishing Ltd
ISBN: 1786467267
Size: 75.84 MB
Format: PDF, Kindle
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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: 37.96 MB
Format: PDF, ePub, Docs
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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.

Designing Data Intensive Applications

Author: Martin Kleppmann
Publisher: "O'Reilly Media, Inc."
ISBN: 1491903104
Size: 58.92 MB
Format: PDF, Mobi
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Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

High Performance Spark

Author: Holden Karau
Publisher: "O'Reilly Media, Inc."
ISBN: 1491943173
Size: 49.53 MB
Format: PDF, ePub
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Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages

I Heart Logs

Author: Jay Kreps
Publisher: "O'Reilly Media, Inc."
ISBN: 1491909331
Size: 32.78 MB
Format: PDF, Kindle
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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

Handbook Of Big Data Technologies

Author: Albert Y. Zomaya
Publisher: Springer
ISBN: 331949340X
Size: 58.42 MB
Format: PDF, ePub
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This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.