Description
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.
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.
What You’ll Learn
- Discover Spark Streaming application development and best practices
- Work with the low-level details of discretized streams
- Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios
- Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver
- Integrate and couple with HBase, Cassandra, and Redis
- Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model
- Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR
- Use streaming machine learning, predictive analytics, and recommendations
- Mesh batch processing with stream processing via the Lambda architecture
Who This Book Is For
Data scientists, big data experts, BI analysts, and data architects.
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