Tabla de Contenidos: “…-- Goal and adoption of JHipster -- Introduction to technologies available -- Client-side technologies -- HTML5 and CSS3 -- HTML5 -- CSS3 -- Sass -- Bootstrap -- MVVM framework -- Angular -- React -- Build tools -- Webpack -- BrowserSync -- Testing tools -- Karma -- Protractor -- Internationalization -- Server-side technologies -- Spring Framework -- Spring Boot -- Spring Security -- Spring MVC -- Spring data -- Security -- JWT -- Session -- OAuth2 -- Build tools -- Maven -- Gradle -- Hibernate -- Liquibase -- Caching -- Ehcache -- Hazelcast -- Infinispan -- Swagger -- Thymeleaf -- Dropwizard metrics -- WebSocket -- Kafka -- Testing frameworks -- JUnit -- Gatling -- Cucumber -- Introduction to database options -- SQL databases -- H2 -- MySQL -- MariaDB -- PostgreSQL -- MS SQL -- Oracle -- NoSQL databases -- MongoDB -- Cassandra -- Elasticsearch -- Installation and setup -- Prerequisites -- Tools required -- Installation procedure -- Java 8 -- Git -- Node.js -- Yarn -- Docker -- IDE configuration -- System setup -- Installation of JHipster -- Summary -- Chapter 3: Building Monolithic Web Applications with JHipster -- Application generation -- Step 1 - preparing the workspace -- Step 2 - generating code using JHipster -- Server-side options -- Client-side options -- Internationalization options -- Testing -- Modules -- Code walkthrough…”

Tabla de Contenidos: “…-- The big data landscape -- Message queuing systems -- Data warehousing -- MongoDB as a data warehouse -- Big data use case with servers on-premises -- Setting up Kafka -- Setting up Hadoop -- Using a Hadoop-to-MongoDB pipeline -- Setting up a Spark connection to MongoDB -- MongoDB Atlas Data Lake -- Summary -- Further reading -- Part 4 - Scaling and High Availability -- Chapter 13: Mastering Replication -- Technical requirements -- Replication -- Logical or physical replication -- Different high availability types -- An architectural overview -- How do elections work? …”

Tabla de Contenidos: “…DE MÉXICO, 1982) -- LA MUCHACHA EBRIA -- TANGO -- DISTANCIA -- MANDAMIENTO EQUIS -- LAS VOCES PROHIBIDAS -- SÍLABAS PARA EL MAXILAR DE FRANZ KAFKA -- PRIMER CANTO DE ABANDONO -- LA ROSA PRIMITIVA -- LOS HOMBRES DEL ALBA -- 15. …”

Tabla de Contenidos: “…SUEÑOS PREMONITORIOS -- VI A KAFKA EN EL CUARTO DE LOS JUGUETES -- VI A HERÁCLITO EN EL RÍO DE SU INFANCIA -- VI A PHILEAS FOGG EN UNA ALDEA DEL AMAZONAS -- VI A BUSTER KEATON COLGANDO DE UN EDIFICIO EN LLAMAS -- VI CIEN MARIPOSAS SALIENDO DEL CEMENTERIO -- VI DIEZ RATAS DEVORÁNDOSE ENTRE SÍ -- VI A FACUNDO CABRAL CARGANDO SU PROPIO ATAÚD -- VI TRES MUÑECAS EN LA TUMBA DE DIOS -- VI AL PERRO INEXPLICABLE EN UNA PLAYA NUDISTA -- VI A DIOS POR EL ESPEJO RETROVISOR -- V. …”

Tabla de Contenidos: “…RDD operations -- Caching data -- Performance optimizations -- Analytics with the Dataset API -- Creating Datasets -- Converting a DataFrame to a Dataset -- Converting a Dataset to a DataFrame -- Accessing metadata using Catalog -- Data Sources API -- Read and write functions -- Built-in sources -- Working with text files -- Working with JSON -- Working with Parquet -- Working with ORC -- Working with JDBC -- Working with CSV -- External sources -- Working with AVRO -- Working with XML -- Working with Pandas -- DataFrame based Spark-on-HBase connector -- Spark SQL as a distributed SQL engine -- Spark SQL's Thrift server for JDBC/ODBC access -- Querying data using beeline client -- Querying data from Hive using spark-sql CLI -- Integration with BI tools -- Hive on Spark -- Summary -- Chapter 5: Real-Time Analytics with Spark Streaming and Structured Streaming -- Introducing real-time processing -- Pros and cons of Spark Streaming -- History of Spark Streaming -- Architecture of Spark Streaming -- Spark Streaming application flow -- Stateless and stateful stream processing -- Spark Streaming transformations and actions -- Union -- Join -- Transform operation -- updateStateByKey -- mapWithState -- Window operations -- Output operations -- Input sources and output stores -- Basic sources -- Advanced sources -- Custom sources -- Receiver reliability -- Output stores -- Spark Streaming with Kafka and HBase -- Receiver-based approach -- Role of Zookeeper -- Direct approach (no receivers) -- Integration with HBase -- Advanced concepts of Spark Streaming -- Using DataFrames -- MLlib operations -- Caching/persistence -- Fault-tolerance in Spark Streaming -- Failure of executor -- Failure of driver -- Performance tuning of Spark Streaming applications -- Monitoring applications -- Introducing Structured Streaming -- Structured Streaming application flow…”

Tabla de Contenidos: “…-- Design a Data Storage Structure -- Design an Azure Data Lake Solution -- Recommended File Types for Storage -- Recommended File Types for Analytical Queries -- Design for Efficient Querying -- Design for Data Pruning -- Design a Folder Structure That Represents the Levels of Data Transformation -- Design a Distribution Strategy -- Design a Data Archiving Solution -- Design a Partition Strategy -- Design a Partition Strategy for Files -- Design a Partition Strategy for Analytical Workloads -- Design a Partition Strategy for Efficiency and Performance -- Design a Partition Strategy for Azure Synapse Analytics -- Identify When Partitioning Is Needed in Azure Data Lake Storage Gen2 -- Design the Serving/Data Exploration Layer -- Design Star Schemas -- Design Slowly Changing Dimensions -- Design a Dimensional Hierarchy -- Design a Solution for Temporal Data -- Design for Incremental Loading -- Design Analytical Stores -- Design Metastores in Azure Synapse Analytics and Azure Databricks -- The Ingestion of Data into a Pipeline -- Azure Synapse Analytics -- Azure Data Factory -- Azure Databricks -- Event Hubs and IoT Hub -- Azure Stream Analytics -- Apache Kafka for HDInsight -- Migrating and Moving Data -- Summary -- Exam Essentials -- Review Questions -- Chapter 4 The Storage of Data -- Implement Physical Data Storage Structures -- Implement Compression -- Implement Partitioning -- Implement Sharding -- Implement Different Table Geometries with Azure Synapse Analytics Pools -- Implement Data Redundancy -- Implement Distributions…”

Tabla de Contenidos: “…Whiteboarding as an information-gathering tool -- Conducting a whiteboarding session -- Identifying data consumers and understanding their requirements -- Identifying data sources and ingesting data -- Identifying data transformations and optimizations -- File format optimizations -- Data standardization -- Data quality checks -- Data partitioning -- Data denormalization -- Data cataloging -- Whiteboarding data transformation -- Loading data into data marts -- Wrapping up the whiteboarding session -- Hands-on - architecting a sample pipeline -- Detailed notes from the project "Bright Light" whiteboarding meeting of GP Widgets, Inc -- Meeting notes -- Summary -- Chapter 6: Ingesting Batch and Streaming Data -- Technical requirements -- Understanding data sources -- Data variety -- Structured data -- Semi-structured data -- Unstructured data -- Data volume -- Data velocity -- Data veracity -- Data value -- Questions to ask -- Ingesting data from a relational database -- AWS DMS -- AWS Glue -- Full one-off loads from one or more tables -- Initial full loads from a table, and subsequent loads of new records -- Creating AWS Glue jobs with AWS Lake Formation -- Other ways to ingest data from a database -- Deciding on the best approach to ingesting from a database -- The size of the database -- Database load -- Data ingestion frequency -- Technical requirements and compatibility -- Ingesting streaming data -- Amazon Kinesis versus Amazon Managed Streaming for Kafka (MSK) -- Serverless services versus managed services -- Open-source flexibility versus proprietary software with strong AWS integration -- At-least-once messaging versus exactly once messaging -- A single processing engine versus niche tools -- Deciding on a streaming ingestion tool -- Hands-on - ingesting data with AWS DMS -- Deploying MySQL and an EC2 data loader via CloudFormation…”

Tabla de Contenidos: “…II Engineering IoT Networks -- ch. 3 Smart Objects: The "Things" in IoT -- Sensors, Actuators, and Smart Objects -- Sensors -- Actuators -- Micro-Electro-Mechanical Systems (MEMS) -- Smart Objects -- Smart Objects: A Definition -- Trends in Smart Objects -- Sensor Networks -- Wireless Sensor Networks (WSNs) -- Communication Protocols for Wireless Sensor Networks -- Summary -- ch. 4 Connecting Smart Objects -- Communications Criteria -- Range -- Frequency Bands -- Power Consumption -- Topology -- Constrained Devices -- Constrained-Node Networks -- Data Rate and Throughput -- Latency and Determinism -- Overhead and Payload -- IoT Access Technologies -- IEEE 802.15.4 -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 802.15.4 Conclusions -- IEEE 802.15.4g and 802.15.4e -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 802.15.4g and 802.15.4e Conclusions -- IEEE 1901.2a -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 1901.2a Conclusions -- IEEE 802.1 lah -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- IEEE 802.1 lah Conclusions -- LoRaWAN -- Standardization and Alliances -- Physical Layer -- MAC Layer -- Topology -- Security -- Competitive Technologies -- LoRaWAN Conclusions -- NB-IoT and Other LTE Variations -- Standardization and Alliances -- LTE Cat 0 -- LTE-M -- NB-IoT -- Topology -- Competitive Technologies -- NB-IoT and Other LTE Variations Conclusions -- Summary -- ch. 5 IP as the IoT Network Layer -- The Business Case for IP -- The Key Advantages of Internet Protocol -- Adoption or Adaptation of the Internet Protocol -- The Need for Optimization -- Constrained Nodes -- Constrained Networks -- IP Versions -- Optimizing IP for IoT -- From 6LoWPAN to 6Lo -- Header Compression -- Fragmentation -- Mesh Addressing -- Mesh-Under Versus Mesh-Over Routing -- 6L0 Working Group -- 6TiSCH -- RPL -- Objective Function (OF) -- Rank -- RPL Headers -- Metrics -- Authentication and Encryption on Constrained Nodes -- ACE -- DICE -- Profiles and Compliances -- Internet Protocol for Smart Objects (IPSO) Alliance -- Wi-SUN Alliance -- Thread -- IPv6 Ready Logo -- Summary -- ch. 6 Application Protocols for IoT -- The Transport Layer -- IoT Application Transport Methods -- Application Layer Protocol Not Present -- SCADA -- A Little Background on SCADA -- Adapting SCADA for IP -- Tunneling Legacy SCADA over IP Networks -- SCADA Protocol Translation -- SCADA Transport over LLNs with MAP-T -- Generic Web-Based Protocols -- IoT Application Layer Protocols -- CoAP -- Message Queuing Telemetry Transport (MQTT) -- Summary -- ch. 7 Data and Analytics for IoT -- An Introduction to Data Analytics for IoT -- Structured Versus Unstructured Data -- Data in Motion Versus Data at Rest -- IoT Data Analytics Overview -- IoT Data Analytics Challenges -- Machine Learning -- Machine Learning Overview -- Supervised Learning -- Unsupervised Learning -- Neural Networks -- Machine Learning and Getting Intelligence from Big Data -- Predictive Analytics -- Big Data Analytics Tools and Technology -- Massively Parallel Processing Databases -- NoSQL Databases -- Hadoop -- YARN -- The Hadoop Ecosystem -- Apache Kafka -- Lambda Architecture -- Edge Streaming Analytics -- Comparing Big Data and Edge Analytics -- Edge Analytics Core Functions -- Distributed Analytics Systems -- Network Analytics -- Flexible NetFlow Architecture -- FNF Components -- Flexible NetFlow in Multiservice IoT Networks -- Summary -- References -- ch. 8 Securing IoT -- A Brief History of OT Security -- Common Challenges in OT Security -- Erosion of Network Architecture -- Pervasive Legacy Systems -- Insecure Operational Protocols -- Modbus -- DNP3 (Distributed Network Protocol) -- ICCP (Inter-Control Center Communications Protocol) -- OPC (OLE for Process Control) -- International Electrotechnical Commission (IEC) Protocols -- Other Protocols -- Device Insecurity -- Dependence on External Vendors -- Security Knowledge -- How IT and OT Security Practices and Systems Vary -- The Purdue Model for Control Hierarchy -- OT Network Characteristics Impacting Security -- Security Priorities: Integrity, Availability, and Confidentiality -- Security Focus -- Formal Risk Analysis Structures: OCTAVE and FAIR -- OCTAVE -- FAIR -- The Phased Application of Security in an Operational Environment -- Secured Network Infrastructure and Assets -- Deploying Dedicated Security Appliances -- Higher-Order Policy Convergence and Network Monitoring -- Summary -- pt. …”

“…You'll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. …”

“…Bohnenkamp: Hofmannsthals Antikenrezeption; Werner Volke: Herausgeben als Aufgabe des Dichters; Ellen Ritter: Hofmannsthal und die Bibliothek der Gräfin Ottonie von Degenfeld; Heinrich Detering: Nietzsche, Ibsen, Strindberg und das Drama der Abstraktion; Bernd Stiegler: Ernst Mach und die Momentphotographie; Franziska Schößler: Künstlerkonzepte in Kafkas »Der Verschollene«…”

“…You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. …”

“…Gain total access to SACON NY 2018's eight tutorials and 50 sessions—75+ hrs. of material Learn from 70+ of the world's top software architects, engineers, developer advocates, and designers Watch keynotes from Cornelia Davis (Pivotal), Adrian Cockcroft (AWS), and Kevin Stewart (Heptio) Hear from experts in microservices, distributed systems, application architecture, enterprise architecture, cloud native, DevOps, serverless, reactive, integration architecture, UX design, and security Boost your skills with tutorials on the role of REST in microservices; building streaming apps with Kafka, Akka Streams, and Kafka Streams; blockchain pros and cons; the use of speedily loading AMP page templates in web design; and more Upgrade your leadership skills by learning from transformational managers at Cerner, Target, Xebia, Sabre, Nav, SkyHook Consulting, and HS2 Solutions Pick up in-the-trenches insights from software architects at Comcast, ING Bank, Confluent, Netflix, AWS, CA Technologies, Google, Warby Parker, Symphonia, and dozens more Download the video or use O'Reilly's video player to view whatever you choose at your own pace…”

“…You will also learn how to use top-notch libraries such as Akka and Play and integrate Scala apps with Kafka, Spark, and Zeppelin, along with deploying applications on a cloud platform. …”

“…Additionally, by integrating Kafka, you'll be able to build robust event-driven systems. …”

“…Zwischen dem Beginn der Untersuchungszeit mit Leopold Zunz und ihrem vorläufigen Ende in Gershom Scholems und Walter Benjamins Diskussion über die Texte Franz Kafkas wird eine Entstehungsgeschichte deutsch-jüdischer Wissenschaft und Literatur nachgezeichnet, in der die jüdische Moderne die rabbinische Tradition umdeutet und neu auslegt…”

“…Implementation of model serving leveraging stream processing enginesand frameworks including Apache Flink, Apache Spark streaming, ApacheBeam, Apache Kafka streams, and Akka streams. Monitoring approaches for model serving implementations…”

“…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…”

“…Innovate quickly and save money with AWS’s on-demand, serverless, and cloud-managed services Implement open source technologies such as Kubeflow, Kubernetes, TensorFlow, and Apache Spark on AWS Build and deploy an end-to-end, continuous ML pipeline with the AWS data science stack Perform advanced analytics on at-rest and streaming data with AWS and Spark Integrate streaming data into your ML pipeline for continuous delivery of ML models using AWS and Apache Kafka…”

“…Understand the fundamentals of reactive systems and event-driven architecture Learn how to use Quarkus to build reactive applications Combine Quarkus with Apache Kafka or AMQP to build reactive systems Develop microservices that utilize messages with Quarkus for use in event-driven architectures…”

“…Understand Cassandra's distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh (the CQL shell) Create a working data model and compare it with an equivalent relational model Design and develop applications using client drivers Explore cluster topology and learn how nodes exchange data Maintain a high level of performance in your cluster Deploy Cassandra onsite, in the cloud, or with Docker and Kubernetes Integrate Cassandra with Spark, Kafka, Elasticsearch, Solr, and Lucene…”