Tabla de Contenidos: “…Contiene: Yanquis / por Christine Sparks. Mi traje de carne muerta / por Annie Baury. …”

Tabla de Contenidos: “…Bohr, Andrew P. Somlyo, Harvey V. Sparks, Jr. - VIII, 666 p. : il…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Exploring Machine Learning -- Exploring Supervised Methods -- Exploring Nonlinear Models -- Exploring Ensemble Methods -- Exploring Unsupervised Methods -- Exploring Cluster Methods -- Exploring Dimension Reduction -- Exploring Deep Learning -- Conclusion -- Chapter 2: Big Data, Machine Learning, and Deep Learning Frameworks -- Big Data -- Big Data Features -- Impact of Big Data on Business and People -- Better Customer Relationships -- Refined Product Development -- Improved Decision-Making -- Big Data Warehousing -- Big Data ETL -- Big Data Frameworks -- Apache Spark -- Resilient Distributed Data Sets -- Spark Configuration -- Spark Frameworks -- SparkSQL -- Spark Streaming -- Spark MLlib -- GraphX -- ML Frameworks -- Scikit-Learn -- H2O -- XGBoost -- DL Frameworks -- Keras -- Chapter 3: Linear Modeling with Scikit-Learn, PySpark, and H2O -- Exploring the Ordinary Least-Squares Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 4: Survival Analysis with PySpark and Lifelines -- Exploring Survival Analysis -- Exploring Cox Proportional Hazards Method -- Lifeline in Action -- Exploring the Accelerated Failure Time Method -- PySpark in Action -- Conclusion -- Chapter 5: Nonlinear Modeling With Scikit-Learn, PySpark, and H2O -- Exploring the Logistic Regression Method -- Scikit-Learn in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 6: Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O -- Decision Trees -- Preprocessing Features -- Scikit-Learn in Action -- Gradient Boosting -- XGBoost in Action -- PySpark in Action -- H2O in Action -- Conclusion -- Chapter 7: Neural Networks with Scikit-Learn, Keras, and H2O…”

Tabla de Contenidos: “…Introduction -- Accelerating large dataset work: map and parallel computing -- Function pipelines for mapping complex transformations -- Processing large datasets with lazy workflows -- Accumulation operations with reduce -- Speeding up map and reduce with advanced parallelization -- Processing truly big datasets with Hadoop and Spark -- Best practices for large data with Apache Streaming and mrjob -- PageRank with map and reduce in PySpark -- Faster decision-making with machine learning and PySpark -- Large datasets in the cloud with Amazon Web Services and S3 -- MapReduce in the cloud with Amazon's Elastic MapReduce…”

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: “…Getting started -- ActionScript 3, XML, and E4X -- Hello spark: primitives, components, FXG and MXML graphics, and even video -- Spark containers, view states, effects, and styling -- Halo Flex 4: using datagrid -- navigator containers, and popups -- Building user-friendly forms using flex formatters and validators -- Cairngorm in action: socialstalkr (Twitter + Yahoo! …”

Tabla de Contenidos: “…Table of Contents An Overview of Amazon EMR Exploring the Architecture and Deployment Options Common Use Cases and Architecture Patterns Big Data Applications and Notebooks Available in Amazon EMR Setting Up and Configuring EMR Clusters Monitoring, Scaling, and High Availability Understanding Security in Amazon EMR Understanding Data Governance in Amazon EMR Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Migrating On-Premises Hadoop Workloads to Amazon EMR Best Practices and Cost Optimization Techniques…”

Tabla de Contenidos: “…Creating Kinesis stream producersCreating Kinesis stream consumers; Generating and consuming crime alerts; Summary; Chapter 6: Getting Acquainted with Spark; An overview of Spark; Batch data processing; Real-time data processing; Apache Spark - a one-stop solution; When to use Spark - practical use cases; The architecture of Spark; High-level architecture; Spark extensions/libraries; Spark packaging structure and core APIs; The Spark execution model - master-worker view; Resilient distributed datasets (RDD); RDD - by definition; Fault tolerance; Storage; Persistence; Shuffling…”

Tabla de Contenidos: “…""Example for MapReduce""""When to Use MapReduce""; ""Spark""; ""Spark Overview""; ""Overview of Spark Components""; ""Basic Spark Concepts""; ""Benefits of Using Spark""; ""Spark Example""; ""When to Use Spark""; ""Abstractions""; ""Pig""; ""Pig Example""; ""When to Use Pig""; ""Crunch""; ""Crunch Example""; ""When to Use Crunch""; ""Cascading""; ""Cascading Example""; ""When to Use Cascading""; ""Hive""; ""Hive Overview""; ""Example of Hive Code""; ""When to Use Hive""; ""Impala""; ""Impala Overview""; ""Speed-Oriented Design""; ""Impala Example""; ""When to Use Impala""; ""Conclusion""…”

Tabla de Contenidos: “…Don't panic ; Shiver ; Spies ; Sparks ; Yellow ; Trouble ; Parachutes ; High speed ; We never change ; Everything's not lost…”

Tabla de Contenidos: “…Chapter 5: Big Data Processing with Apache Spark -- Technical requirements -- Getting started with Spark -- Installing Spark locally -- Spark architecture -- Spark executors -- Components of execution -- Starting a Spark program -- The DataFrame API and the Spark SQL API -- Transformations -- Actions -- Lazy evaluation -- Data partitioning -- Narrow versus wide transformations -- Analyzing the titanic dataset -- Working with real data -- How Spark performs joins -- Joining IMDb tables -- Summary -- Chapter 6: Building Pipelines with Apache Airflow -- Technical requirements -- Getting started with Airflow -- Installing Airflow with Astro -- Airflow architecture -- Airflow's distributed architecture -- Building a data pipeline -- Airflow integration with other tools -- Summary -- Chapter 7: Apache Kafka for Real-Time Events and Data Ingestion -- Technical requirements -- Getting started with Kafka -- Exploring the Kafka architecture -- The PubSub design -- How Kafka delivers exactly-once semantics -- First producer and consumer -- Streaming from a database with Kafka Connect -- Real-time data processing with Kafka and Spark -- Summary -- Part 3: Connecting It All Together -- Chapter 8: Deploying the Big Data Stack on Kubernetes -- Technical requirements -- Deploying Spark on Kubernetes -- Deploying Airflow on Kubernetes -- Deploying Kafka on Kubernetes -- Summary -- Chapter 9: Data Consumption Layer -- Technical requirements -- Getting started with SQL query engines -- The limitations of traditional data warehouses -- The rise of SQL query engines -- The architecture of SQL query engines -- Deploying Trino in Kubernetes -- Connecting DBeaver with Trino -- Deploying Elasticsearch in Kubernetes -- How Elasticsearch stores, indexes and manages data -- Elasticsearch deployment -- Summary -- Chapter 10: Building a Big Data Pipeline on Kubernetes…”

Tabla de Contenidos: “…Cover; FLASH BUILDER @ WORK: CUSTOMIZING THE USER INTERFACE; Copyright; CONTENTS; mx and Spark Components; Themes, Skins, and Styles; Styling Single Components; Skinning; Conclusion…”

Tabla de Contenidos: “…-- Big data for analytics -- Big data - a bigger pay package for Java developers -- Basics of Hadoop - a Java sub-project -- Distributed computing on Hadoop -- HDFS concepts -- Design and architecture of HDFS -- Main components of HDFS -- HDFS simple commands -- Apache Spark -- Concepts -- Transformations -- Actions -- Spark Java API -- Spark samples using Java 8 -- Loading data -- Data operations - cleansing and munging -- Analyzing data - count, projection, grouping, aggregation, and max/min -- Actions on RDDs -- Paired RDDs -- Saving data -- Collecting and printing results -- Executing Spark programs on Hadoop -- Apache Spark sub-projects -- Spark machine learning modules -- Mahout - a popular Java ML library -- Deeplearning4j - a deep learning library -- Summary -- Chapter 2: First Steps in Data Analysis -- Datasets -- Data cleaning and munging -- Basic analysis of data with Spark SQL -- Building SparkConf and context -- Dataframe and datasets -- Load and parse data -- Analyzing data - the Spark-SQL way -- Spark SQL for data exploration and analytics -- Market basket analysis - Apriori algorithm -- Implementation of the Apriori algorithm in Apache Spark -- Efficient market basket analysis using FP-Growth algorithm -- Running FP-Growth on Apache Spark -- Summary -- Chapter 3: Data Visualization -- Data visualization with Java JFreeChart -- Using charts in big data analytics -- Time series chart -- All India seasonal and annual average temperature series dataset -- Simple single Time Series chart -- Multiple Time Series on a single chart window -- Bar charts -- Histograms -- When would you use a histogram?…”