Tabla de Contenidos: “…; Apache Tez; Apache Giraph; HOYA (HBase on YARN); KOYA (Kafka on YARN)…”

Tabla de Contenidos: “…-- Use cases and case studies -- Real-time insights for Advertising Tech (PubMatic) -- Industrial IoT applications (GE) -- Real-time threat detection (Capital One) -- Silver Spring Networks (SSN) -- Application Model and API -- Directed Acyclic Graph (DAG) -- Apex DAG Java API -- High-level Stream Java API -- SQL -- JSON -- Windowing and time -- Value proposition of Apex -- Low latency and stateful processing -- Native streaming versus micro-batch -- Performance -- Where Apex excels -- Where Apex is not suitable -- Summary -- Chapter 2: Getting Started with Application Development -- Development process and methodology -- Setting up the development environment -- Creating a new Maven project -- Application specifications -- Custom operator development -- The Apex operator model -- CheckpointListener/CheckpointNotificationListener -- ActivationListener -- IdleTimeHandler -- Application configuration -- Testing in the IDE -- Writing the integration test -- Running the application on YARN -- Execution layer components -- Installing Apex Docker sandbox -- Running the application -- Working on the cluster -- YARN web UI -- Apex CLI -- Logging -- Dynamically adjusting logging levels -- Summary -- Chapter 3: The Apex Library -- An overview of the library -- Integrations -- Apache Kafka -- Kafka input -- Kafka output -- Other streaming integrations -- JMS (ActiveMQ, SQS, and so on) -- Kinesis streams -- Files -- File input -- File splitter and block reader -- File writer -- Databases -- JDBC input -- JDBC output -- Other databases…”

Tabla de Contenidos: “…-- CAP theorem and databases -- Distributed system patterns -- CQRS -- EDA -- Dealing with failure -- Two-phase commit (2PC) -- The saga pattern -- What is a message bus? -- Kafka -- RabbitMQ -- NATS -- Summary -- Further reading -- Chapter 8: TDD, BDD, and DDD -- Technical requirements -- TDD -- Adding a test -- Run the test we just wrote - it should fail (and we should expect it to) -- Write as little code as possible to pass the test -- Refactoring -- BDD -- Summary -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…ENTRE LÍNEAS: ENSAYOS SOBRE LITERATURA Y SOCIEDAD; PÁGINA LEGAL; ÍNDICE; PRESENTACIÓN; I ESCRITURA Y REALIDAD SOCIAL A TRAVÉS DE LOS AUTORES Y LOS TEXTOS; MERCADOS DE PALABRAS Y EXPERIENCIA ÉTICA: UNA LECTURA DE «LA LOZANA ANDALUZA»; ENTRE LA HISTORIA Y LA POLÍTICA: ALEGORÍAS DEL LENGUAJE Y LA LEY EN ROUSSEAU Y KAFKA; EL MODERNISMO REPUBLICANO DE JOSÉ MARTÍ; MAX WEBER Y RILKE: LA MAGIA DEL LENGUAJE Y DE LA MÚSICA EN UN MUNDO DESENCANTADO; EXPRESIONISMO Y REVOLUCIÓN: EL ABISMO DE LA REALIDAD; LA NARRATIVA SOCIOLÓGICA DE FRANCISCO AYALA; II MEMORIA Y TRADICIÓN, CONSUMO Y RENOVACIÓN…”

Tabla de Contenidos: “…Tymieniecka, The Work of the Analecta Husserliana and Conversion -- Lifelong Learning and Self-Actualization -- Faces of Memory – The Work of Franz Kafka as a Record of Consciouness Lost in the Labirynth of Being in the Context of Existential Philosophy -- Which Self? …”

Tabla de Contenidos: “…8.4 Trident 操作 -- 8.4.1 函数 -- 8.4.2 Map函数and FlatMap函数 -- 8.4.3 peek 函数 -- 8.4.4 滤器 -- 8.4.5 窗口操作 -- 8.4.6 合操作 -- 8.4.7 分组操作 -- 8.4.8 合并和组合操作 -- 8.5 DRPC -- 8.6 小 牛刀 -- 用以绑定spout 和bolt 的拓扑构建器 -- 从事件流中 滤电 号码 -- 从事件流中提取 备信息 -- 8.7 小结 -- 第四 分 使用Spark 实现实时 算 -- 第9章 用Spark引擎 -- 9.1 Spark 概 -- Spark 框架和 度器 -- 9.2 Spark 的独特优势 -- 何时 免使用Spark -- 9.3 Spark 用例 -- 9.4 Spark架构--引擎内 的 模式 -- 9.5 Spark 的 用概念 -- RDD- 如其名 -- 9.6 Spark 2.x--数据框和数据 的出现 -- 9.7 小结 -- 第10章 用Spark操作 -- 10.1 Spark--封 和 API -- 10.2 RDD 用探索 -- 10.2.1 换 -- 10.2.2 动作 -- 10.3 共享变 --广播变 和累加器 -- 10.3.1 广播变 -- 10.3.2 累加器 -- 10.4 小结 -- 第11章 Spark Streaming -- 11.1 Spark Streaming 的概念 -- 11.2 Spark Streaming 的简介和体系结构 -- 11.3 Spark Streaming 的封 结构 -- 11.3.1 Spark Streaming API -- 11.3.2 Spark Streaming操作 -- 11.4 接 Kafka 和 Spark Streaming -- 11.5 小结 -- 第五 分 使用Flink实现实时分析 -- 第12章 用Apache Flink -- 12.1 Flink 体系结构和执 引擎 -- 12.2 Flink 的基本组件和 程 -- 12.3 将源流 成到 Flink -- 12.3.1 和Apache Kafka 成 -- 12.3.2 和RabbitMQ 成 -- 12.4 Flink 处理和 算 -- 12.4.1 Datastream API -- 12.4.2 DataSet API -- 12.5 Flink 持久化 -- 和Cassandra 成 -- 12.6 Flink CEP -- 12.7 Pattern API -- 12.7.1 检测模式 -- 12.7.2 模式 择 -- 12.7.3 示例 -- 12.8 Gelly -- Gelly API -- 12.9 小 牛刀 -- 12.10 小结 -- 第六 分 综合应用 -- 第13章 用例研究 -- 13.1 概 -- 13.2 数据建模 -- 13.3 工具和框架 -- 13.4 建立基础 施 -- 13.5 实现用例 -- 13.5.1 构建数据模拟器 -- 13.5.2 Hazelcast 加 器 -- 13.5.3 构建Storm拓扑 -- 13.6 用例 -- 加 Hazelcast -- 13.7 小结…”

“…Vigorosa denuncia contra toda marginación, Sefarad es una sabia mezcla de personajes reales (Kafka, Primo Levi, Willi Münzenberg...) y personajes ficticios, de tragedias e ironías, de odios y afectos, al mismo tiempo que de géneros literarios: cada uno de los relatos entrelazados que componen esta novela enriquece y profundiza los anteriores…”

“…A partir de un sondeo realizado en institutos y universidades de cinco países de la Unión Europea (Alemania, Italia, Portugal, Rumanía y España), este volumen antologiza quince textos, de Homero a Kafka, más una última obra, la decimosexta, nacional, con los que se da respuesta positiva a estos interrogantes…”
Enlace del recurso

“…Los textos de autores del siglo XX como Franz Kafka, Alejo Carpentier, Albert Camus, Jorge Luis Borges, D. …”
Texto completo en Odilo

“…With so little else known of Marie's life, this book seeks to read her back into existence by drawing on a host of contemporaneous texts -- largely modernist texts, by Virginia Woolf, Franz Kafka, the Paris Surrealists, Stephane Mallarme, Oscar Wilde, Katherine Mansfield, and Walter Benjamin. …”

“…Engineers from Lightbend identify the key characteristics of fast data architectures, separate them into functional blocks, and show you how to implement those functions using components like those in the SMACK stack—Spark, Mesos, Akka, Cassandra, and Kafka, as well as others. Architects will learn how to choose, combine, and run SMACK stack technologies to build resilient, scalable, and responsive systems that your company requires. …”

“…Master the art of implementing scalable and reactive microservices in your production environment with Java 11 Key Features Use domain-driven designs to build microservices Explore various microservices design patterns such as service discovery, registration, and API Gateway Use Kafka, Avro, and Spring Streams to implement event-based microservices Book Description Microservices are key to designing scalable, easy-to-maintain applications. …”

“…With this book, you will design a streaming data mesh using Kafka; learn how to identify a domain; build your first data product using self-service tools; apply data governance to the data products you create; learn the differences between synchronous and asynchronous data services; and implement self-services that support decentralized data…”

“…You will also explore Kafka serialization and deserialization for Spark and how to work with Kafka AVRO Source. …”

“…Hans Castorp (Thomas Mann's The Magic Mountain), Joseph K. (Franz Kafka's The Trial), John the Savage (Aldous Huxley's Brave New World), Winston Smith (George Orwell's 1984), Ralph (William Golding's Lord of the Flies), Merusault (Albert Camus's The Stranger), Ida Ramundo (Elsa Morante's History), and Chauncey Gardiner (Jerzy Kosinski's Being There) participate in ideological, technological, and organizational projects of the twentieth century. …”

“…What You Will Learn Understand modern software applications architectures Build secure microservices that can effectively communicate with other services Get to know about event-driven architectures by diving into message queues such as Kafka, Rabbitmq, and AWS SQS. Understand key modern database technologies such as MongoDB, and Amazon’s DynamoDB Leverage the power of containers Explore Amazon cloud services fundamentals Know how to utilize the power of the Go language to access key services in the Amazon cloud such as S3, SQS, DynamoDB and more. …”

“…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. …”

“…You'll see world-class experts like Jay Kreps (Confluent) and Michael Armbrust (Databricks) discuss advances in tools such as Kafka and Spark, and leaders in the field explain how they design real-time platforms at scale, address common challenges, and implement streaming architectures across industries. …”

“…You'll enjoy overviews of technologies and platforms like Microsoft’s Azure HDInsight, O'Reilly's Oriole, BMC's Control-M, Google's TensorFlow, AWS Lambda, Twitter's Heron, Intel's BigDL, as well as talks on almost every Apache ecosystem tool available today, including Arrow, Beam, Cassandra, Druid, Flink, Flume, HAWQ, Hive, Impala, Kafka, Kinetica, Kudu, MXNet, Presto, Spark, Spark ML, Spark Streaming, and Spark SQL. …”

“…They also use products, such as Kafka and Kubernetes, or databases, such as YugabyteDB, Cassandra, MongoDB, and Neo4j, that are distributed by nature. …”