Pro.Spark.Streaming.The.Zen.of.Real-Time.Analytics.Using.Apache.Spark.1484

【文件属性】：
文件名称：Pro.Spark.Streaming.The.Zen.of.Real-Time.Analytics.Using.Apache.Spark.1484
文件大小：13.41MB
文件格式：PDF
更新时间：2019-07-18 04:56:27
Pro Spark Streaming Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. Pro Spark Streaming walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in the book include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Spark Streaming application development and best practices Low-level details of discretized streams The application and vitality of streaming analytics to a number of industries and domains Optimization of production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingestion of data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integration and coupling with HBase, Cassandra, and Redis Design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Streaming machine learning, predictive analytics, and recommendations Meshing batch processing with stream processing via the Lambda architecture Who This Book Is For The audience includes data scientists, big data experts, BI analysts, and data architects. Table of Contents Chapter 1: The Hitchhiker’s Guide to Big Data Chapter 2: Introduction to Spark Chapter 3: DStreams: Real-Time RDDs Chapter 4: High-Velocity Streams: Parallelism and Other Stories Chapter 5: Real-Time Route 66: Linking External Data Sources Chapter 6: The Art of Side Effects Chapter 7: Getting Ready for Prime Time Chapter 8: Real-Time ETL and Analytics Magic Chapter 9: Machine Learning at Scale Chapter 10: Of Clouds, Lambdas, and Pythons