Tabla de Contenidos: “…Differences (1867) / Nathan Mayer -- As it was written: a Jewish musician's story (1885) / Sidney Luska -- Other things being equal (1892) / Emma Wolf -- Joseph Zalmonah (1893) / Edward King -- Yekl (1896) and The imported bridegroom and other stories of the New York ghetto (1898) / Abraham Cahan -- Children of men (1903) / Bruno Lessing -- Q.E.D. (1903) / Gertrude Stein -- Motl, the cantor's son (1907-16) / Sholem Aleichem -- The tether (1908) / Ezra Brudno -- The rise of David Levinsky (1917) / Abraham Cahan -- The chosen people (1917) / Sidney Nyburg -- Fanny herself (1917) / Edna Ferber -- Salome of the tenements (1923) / Anzia Yezierska -- Haunch, paunch and jowl (1923) / Samuel Ornitz -- Bread givers (1925) / Anzia Yezierska -- Amerika (1927) / Franz Kafka -- The island within (1928) / Ludwig Lewisohn -- Arnold Levenberg (1928) / David Pinski -- Singermann (1929) / Myron Brinig -- Bottom dogs (1929) / Edward Dahlberg -- Jews without money (1930) / Michael Gold -- By the waters of Manhattan (1930) / Charles Reznikoff -- Thicker than water (1932) / Vera Caspary -- Miss Lonelyhearts (1933) and The day of the locust (1939) / Nathanael West -- Call it sleep (1934) / Henry Roth -- Summer in Williamsburg, homage to Blenholt, and Low Company (1934) / Daniel Fuchs -- The education of H*Y*M*A*N K*A*P*L*A*N (1937) / Leonard Q. …”
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“…Add Spark Streaming to your data science and machine learning Python projects About This Video Create big data streaming pipelines with Spark using Python Run analytics on live tweet data from Twitter Integrate Spark Streaming with tools such as Apache Kafka, used by Fortune 500 companies Work with the new features of the most recent version of Spark: 2.3 In Detail Spark Streaming is becoming incredibly popular, and with good reason. …”

“…El gran pensador y ensayista Rüdiger Safranski explora en estas páginas una cuestión esencial para la historia del pensamiento y que filósofos y escritores como Rousseau, Kleist, Nietzsche o Kafka tensaron al máximo: el hombre tiene la conciencia de ser un sujeto escindido, separado tanto de sí mismo como del resto de la naturaleza, y necesita las creaciones del arte y de la cultura para reconciliarse consigo mismo y con sus semejantes. …”

“…Skill building sessions on Python, R, Apache Spark, Kafka, Kudu, and Cassandra delivered by Hadoop specialists at Dato, Google, Continuum Analytics, and Confluent. …”

“…A short list of the technologies you’ll learn about include Apache Hadoop, Spark, Hive, Beam, Impala, Solr, Kudu, Mesos, Zeppelin, Akka, Cassandra, Myriad, Kafka, Apex, Druid, Python, R, graph databases, data lakes, the Trusted Analytics platform, YARN, Spark Streaming, JupyterLab, holographic data visualizations, and event-based microservices applications. …”

“…This book touches on a range of cultural figures including Gide, Princess Diana, Kafka, Gautier and Rilke, and such films as Gattaca, The Talented Mr Ripley, Being John Malkovich, The Piano and The Truman Show , together with theories of the caress, phantom limbs and replacement children. …”

“…Ma per passare dal conte al romanzo, dall'apologo e dai trattati a personaggi complessi che pure mantengono una forte allure speculativa, bisognava lasciare il Settecento, sperimentare il Romanticismo, nutrire nel secolo successivo le rêveries dei nuovi promeneurs solitaires con l'inquietudine e gli interrogativi di Dostoevskij, Kafka, Sartre, Camus, e di Pirandello, Proust, Musil. …”

“…., Hadoop, Spark, Hive, Presto, Kafka, and Avro) as well as what Uber's tools such as Hudi and Marmaray. …”

“…En esta cuidada selección se recogen los relatos capitales de una de las autoras más relevantes del panorama literario chino contemporáneo, cuyo nombre se menciona siempre en relación a genios como Kafka o Borges, y como una de las candidatas al Premio Nobel. …”

Tabla de Contenidos: “…Analyzing missing data -- Combining data using a JOIN operation -- Munging textual data -- Processing multiple input data files -- Removing stop words -- Munging time series data -- Pre-processing of the&amp -- #160 -- time-series Dataset -- Processing date fields -- Persisting and loading data -- Defining a date-time index -- Using the&amp -- #160 -- &amp -- #160 -- TimeSeriesRDD&amp -- #160 -- object -- Handling missing time-series data -- Computing basic statistics -- Dealing with variable length records -- Converting variable-length records to fixed-length records -- Extracting data from "messy" columns -- Preparing data for machine learning -- Pre-processing data for machine learning -- Creating and running a machine learning pipeline -- Summary -- Chapter 5: Using Spark SQL in Streaming Applications -- Introducing streaming data applications -- Building Spark streaming applications -- Implementing sliding window-based functionality -- Joining a streaming Dataset with a static Dataset -- Using the Dataset API in Structured Streaming -- Using output sinks -- Using the Foreach Sink for arbitrary computations on output -- Using the Memory Sink to save output to a table -- Using the File Sink to save output to a partitioned table -- Monitoring streaming queries -- Using Kafka with Spark Structured Streaming -- Introducing Kafka concepts -- Introducing ZooKeeper concepts -- Introducing Kafka-Spark integration -- Introducing Kafka-Spark Structured Streaming -- Writing a receiver for a custom data source -- Summary -- Chapter 6: Using Spark SQL in Machine Learning Applications -- Introducing machine learning applications -- Understanding Spark ML pipelines and their components -- Understanding the steps in a pipeline application development process -- Introducing feature engineering -- Creating new features from raw data…”

Tabla de Contenidos: “…Benefits -- Shortcomings -- Communication -- Microservice architectures -- Sharing data and technology in enterprises -- Shared-nothing architectures -- Interdependent systems -- 12-factor and cloud native applications -- When to use and when not to use microservices -- Implementing microservices with Java EE -- Zero-dependency applications -- Application servers -- Implementing application boundaries -- Implementing CQRS -- System interfaces -- Example scenario using Apache Kafka -- Integrating Java EE -- CDI events -- Event handlers -- State representation -- Consuming Kafka messages -- Producing Kafka messages -- Application boundaries -- Integrating further CQRS concepts -- Java EE in the age of distribution -- Discovering services -- Communicating resiliently -- Validating responses -- Breaking timeouts and circuits -- Bulkheads -- Shaking hands and pushing back -- More on being resilient -- Summary -- Chapter 9: Monitoring, Performance, and Logging -- Business metrics -- Collecting business metrics -- Emitting metrics -- Enter Prometheus -- Realization with Java EE -- Integrating the environment -- Meeting performance requirements in distributed systems -- Service level agreements -- Achieving SLAs in distributed systems -- Tackling performance issues -- Theory of constraints -- Identifying performance regression with jPDM -- Subsystems -- Actors -- Application -- JVM -- Operating system and hardware -- jPDM instances - production situations -- Analyzing the jPDM instances -- Dominating consumer - OS -- Dominating consumer - none -- Dominating consumer - JVM -- Dominating consumer - application -- Conclusion -- Technical metrics -- Types of technical metrics -- High frequency monitoring versus sampling -- Collecting technical metrics -- Boundary metrics -- Logging and tracing -- Shortcomings of traditional logging -- Performance -- Log levels…”

Tabla de Contenidos: “…Der Mensch im Lichte des Maschinenbaus 193 »Die Welt geht ihren Gang, und Du machst Deine Fahrt.« Zur Problematik des ›normalen Lebens‹ bei Franz Kafka 211 Mediale Schnittstellen. Ausdruckshand und Arbeitshand 235 Vitale Maschinen und programmierte Androiden. …”

Tabla de Contenidos: “…Collecting User Activity Log with Spark and KafkaReal-Time Recommendation with Spark; Real-Time Categorization of Twitter Bots; Summary; Index; EULA…”

Tabla de Contenidos: “…Binary classification using LogisticRegression with Pipeline APIClustering using K-means; Feature reduction using principal component analysis; Chapter 6: Scaling Up; Introduction; Building the Uber JAR; Submitting jobs to the Spark cluster (local); Running the Spark Standalone cluster on EC2; Running the Spark Job on Mesos (local); Running the Spark Job on YARN (local); Chapter 7: Going Further; Introduction; Using Spark Streaming to subscribe to a Twitter stream; Using Spark as an ETL tool; Using StreamingLogisticRegression to classify a Twitter stream using Kafka as a training stream…”

Tabla de Contenidos: “…The utterance as transgression: contextual liminality and the rhetoric of the verisimilar Tomi Moisio -- 7. Kafka's actors: Josef K.'s journey to theatricality Tanja Marcotte -- 8. …”

Tabla de Contenidos: “…Sampling by time window -- Extracting audio signatures -- Building a song analyzer -- Selling data science is all about selling cupcakes -- Using Cassandra -- Using the Play framework -- Building a recommender -- The PageRank algorithm -- Building a Graph of Frequency Co-occurrence -- Running PageRank -- Building personalized playlists -- Expanding our cupcake factory -- Building a playlist service -- Leveraging the Spark job server -- User interface -- Summary -- Chapter 9: News Dictionary and Real-Time Tagging System -- The mechanical Turk -- Human intelligence tasks -- Bootstrapping a classification model -- Learning from Stack Exchange -- Building text features -- Training a Naive Bayes model -- Laziness, impatience, and hubris -- Designing a Spark Streaming application -- A tale of two architectures -- The CAP theorem -- The Greeks are here to help -- Importance of the Lambda architecture -- Importance of the Kappa architecture -- Consuming data streams -- Creating a GDELT data stream -- Creating a Kafka topic -- Publishing content to a Kafka topic -- Consuming Kafka from Spark Streaming -- Creating a Twitter data stream -- Processing Twitter data -- Extracting URLs and hashtags -- Keeping popular hashtags -- Expanding shortened URLs -- Fetching HTML content -- Using Elasticsearch as a caching layer -- Classifying data -- Training a Naive Bayes model -- Thread safety -- Predict the GDELT data -- Our Twitter mechanical Turk -- Summary -- Chapter 10: Story De-duplication and Mutation -- Detecting near duplicates -- First steps with hashing -- Standing on the shoulders of the Internet giants -- Simhashing -- The hamming weight -- Detecting near duplicates in GDELT -- Indexing the GDELT database -- Persisting our RDDs -- Building a REST API -- Area of improvement -- Building stories -- Building term frequency vectors…”

“…Así, mezclados en el tiempo y en la historia de la literatura universal, el lector descubre las lecturas calvinianas de La Odisea, Orlando furioso, Cyrano de Bergerac, Las Metamorfosis (las de Ovidio y la de Kafka), Cándido o Doctor Zhivago, libros con los que se codean otros escritores tan queridos por el autor como Gadda y Borges, u otros por los que manifiesta su simpatía cómplice como Ponge y Queneau. …”