Materias:

“…Data streaming and event-driven architectures are inherently flexible and scalable, allowing organizations to build systems that manage large amounts of data based on actual business usage and needs and to work with that data in real-time. …”

Tabla de Contenidos: “…Fundamental Concepts and Mechanisms of Stream Control Transmission Protocol (SCTP) Multihoming; 2. …”

Tabla de Contenidos: “…Chapter 1: Introduction: Big data, Auto Machine Learning and Data Streams -- Chapter 2: Transactional Machine Learning -- Chapter 3: Industry Challenges with Data Streams and AutoML -- Chapter 4: The Business Value of Transactional Machine Learning -- Chapter 5: The Technical Components and Architecture for Transactional Machine Learning -- Overview of a TML Solution -- Chapter 6: Template for Transactional Machine Learning Solutions -- CHAPTER 7: Visualize Your TML Model Insights: Optimization, Predictions and Anomalies -- Chapter 8: Evolution and Opportunities For Transactional Machine Learning in Almost Every Industry -- Chapter 9: Conclusion and Final Thoughts…”

“…Streaming data enables you to rapidly assess and respond to events, but only if you have the right methods for processing it. …”

“…This report provides a concise, practical guide to building a data architecture that efficiently delivers big, complex, and streaming data to both internal users and customers. …”

“…Kafka streams for data processing…”

“…Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. …”

“…We will look at a solution developed with Spring XD to stream real time analytics from a moving car using open standards. …”

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…”