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  • 1
    ISBN: 9781788992251 , 1788992253
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Apache (Computer file : Apache Group) ; Apache Kafka (Electronic resource) ; Electronic data processing ; Distributed processing ; Telecommunication ; Message processing ; Web servers ; Computer programs ; Electronic books ; Electronic books ; local
    Abstract: Process large volumes of data in real-time while building high performance and robust data stream processing pipeline using the latest Apache Kafka 2.0 Key Features Solve practical large data and processing challenges with Kafka Tackle data processing challenges like late events, windowing, and watermarking Understand real-time streaming applications processing using Schema registry, Kafka connect, Kafka streams, and KSQL Book Description Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the ?y. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines. This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment. Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows. What you will learn How to validate data with Kafka Add information to existing data ?ows Generate new information through message composition Perform data validation and versioning with the Schema Registry How to perform message Serialization and Deserialization How to perform message Serialization and Deserialization Process data streams with Kafka Streams Understand the duality between tables and streams with KSQL Who this book is for This book is for developers who want to quickly master the practical concepts behind Apache Kafka. The audience need not have come across Apache Kafka previously; however, a familiarity of Java or any JVM language will be helpful in understanding the code in this book.
    Note: Description based on online resource; title from title page (Safari, viewed March 6, 2019)
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  • 2
    ISBN: 9781787282186 , 178728218X
    Language: English
    Pages: 1 online resource (1 volume)
    Parallel Title: Erscheint auch als
    Keywords: Apache (Computer file : Apache Group) ; Apache Kafka (Electronic resource) ; Electronic data processing ; Telecommunication Message processing ; Web servers Computer programs
    Note: Description based on online resource; title from title page (viewed January 31, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    ISBN: 9781484221754 , 1484221753
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Apache Mesos (Electronic resource) ; Akka (Electronic resource) ; Apache Cassandra ; Apache Kafka ; Big data ; Database management ; Electronic books ; Electronic books ; local
    Abstract: Learn how to integrate full-stack open source big data architecture and to choose the correct technology-Scala/Spark, Mesos, Akka, Cassandra, and Kafka-in every layer. Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses. Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer: What You'll Learn The language: Scala The engine: Spark (SQL, MLib, Streaming, GraphX) The container: Mesos, Docker The view: Akka The storage: Cassandra The message broker: Kafka What You Will Learn: Make big data architecture without using complex Greek letter architectures Build a cheap but effective cluster infrastructure Make queries, reports, and graphs that business demands Manage and exploit unstructured and No-SQL data sources Use tools to monitor the performance of your architecture Integrate all technologies and decide which ones replace and which ones reinforce Who This Book Is For Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer
    Note: Includes index. - Description based on online resource; title from cover (viewed February 1, 2017)
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  • 4
    ISBN: 9781786468062 , 1786468069
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Electronic data processing ; Distributed processing ; Management ; Data structures (Computer science) ; Database management ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Combine the incredible powers of Spark, Mesos, Akka, Cassandra, and Kafka to build data processing platforms that can take on even the hardest of your data troubles! About This Book This highly practical guide shows you how to use the best of the big data technologies to solve your response-critical problems Learn the art of making cheap-yet-effective big data architecture without using complex Greek-letter architectures Use this easy-to-follow guide to build fast data processing systems for your organization Who This Book Is For If you are a developer, data architect, or a data scientist looking for information on how to integrate the Big Data stack architecture and how to choose the correct technology in every layer, this book is what you are looking for. What You Will Learn Design and implement a fast data Pipeline architecture Think and solve programming challenges in a functional way with Scala Learn to use Akka, the actors model implementation for the JVM Make on memory processing and data analysis with Spark to solve modern business demands Build a powerful and effective cluster infrastructure with Mesos and Docker Manage and consume unstructured and No-SQL data sources with Cassandra Consume and produce messages in a massive way with Kafka In Detail SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique developers have begun to use to tackle critical real-time analytics for big data. This highly practical guide will teach you how to integrate these technologies to create a highly efficient data analysis system for fast data processing. We'll start off with an introduction to SMACK and show you when to use it. First you'll get to grips with functional thinking and problem solving using Scala. Next you'll come to understand the Akka architecture. Then you'll get to know how to improve the data structure architecture and optimize resources using Apache Spark. Moving forward, you'll learn how to perform linear scalability in databases with Apache Cassandra. You'll grasp the high throughput distributed messaging systems using Apache Kafka. We'll show you how to build a cheap but effective cluster infrastructure with Apache Mesos. Finally, you will deep dive into the different aspect of SMACK using a few case studies. By the end of the book, you will be able to integrate all the components of the SMACK stack and use them together to ach...
    Note: Description based on online resource; title from cover (Safari, viewed January 17, 2017)
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