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. This book 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 Pro Spark Streaming 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 Streamingwill act as the bible of Spark Streaming.What You'll LearnDiscover Spark Streaming application development and best practicesWork with the low-level details of discretized streamsOptimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and NagiosIngest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiverIntegrate and couple with HBase, Cassandra, and RedisTake advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch modelImplement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkRUse streaming machine learning, predictive analytics, and recommendationsMesh batch processing with stream processing via the Lambda architectureWho This Book Is ForData scientists, big data experts, BI analysts, and data architects.

Highlights the differences between traditional stream processing and the Spark Streaming micro-batch model Targets real-world applications from multiple industry verticals Provides an introduction to other popular Big Data solutions, such as Apache Kafka

Auteur
Zubair Nabi is one of the very few computer scientists who have solved Big Data problems in all three domains: academia, research, and industry. He currently works at Qubit, a London-based start up backed by Goldman Sachs, Accel Partners, Salesforce Ventures, and Balderton Capital. Qubit helps retailers understand their customers and provide personalized customer experience, and which has a rapidly growing client base that includes Staples, Emirates, Thomas Cook, and Topshop. Prior to Qubit, he was a researcher at IBM Research, where he worked at the intersection of Big Data systems and analytics to solve real-world problems in the telecommunication, electricity, and urban dynamics space.
Zubair's work has been featured in MIT Technology Review, SciDev, CNET, and Asian Scientist, and on Swedish National Radio, among others. He has authored more than 20 research papers, published by some of the top publication venues in computer science including USENIX Middleware, ECML PKDD, and IEEE BigData; and he also has a number of patents to his credit.
Zubair has an MPhil in computer science with distinction from Cambridge.

Contenu
Chapter 1: The Hitchhiker's Guide to Big Data.- Chapter 2: Introduction to Spark.- Chapter 3: DStreams: Realtime 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.