Pro Spark Streaming ― The Zen of Real-time Analytics Using Apache Spark

Learn the right 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 atop Spark Streaming, uses real-world examples from multiple domains including telecommunications, digital marketing, and manufacturing to illustrate best practices. The author also delves deep under the hood to guide application and system optimization, gives details of integration with external data sources, including Apache Kafka, MQTT, Flume, and Twitter and discusses interaction with high-level development solutions and libraries, such as SparkSQL, SparkR, and MLlib

Real-time processing is critical for applications, services, and business operations, in a wide range of industry verticals, from telecommunications and digital marketing to web analytics and weather forecasting. This books aims at enabling practitioners from these various industries and general big data developers to use Spark Streaming to implement their real-time processing pipelines.

Apache Spark has very quickly established itself as the de facto big data processing engine. In recent years, it has moved beyond in-memory batch analytics to include graph processing, declarative queries, and streaming applications. Its micro batch-processing approach to stream processing enables low-latency application pipelines while retaining the familiar functional programming interface, fault-tolerance model, and in-memory operations of Spark.

Pro Spark Streaming by Zubair Nabi will include use cases with complete sample code, which will also be uploaded to GitHub. All in all, this book will act as the bible of Spark Streaming.

Zubair Nabi works at the intersection of Big Data systems and analytics at IBM Research, Ireland. His research interests broadly span data intensive computing, cloud computing, Internet accessibility, and technology for the developing world. On and off, he also likes to dabble in pervasive computing, P2P systems, VANETs, Internet anti-censorship, computer science education, and the semantic web. His current work revolves around data in motion and data at rest as well as cross-platform cluster management, with an emphasis on using the latter to bridge the gap between the former two. Furthermore, a key work strand is the use of Big Data systems and analytics to solve real-world problems in the telco, electricity, and urban dynamics space. In addition, he aids multiple teams within the Smarter Cities Technology Centre at IBM Research in solving their "Big Data" problems. Zubair's work has been featured in MIT Technology Review, SciDev, CNET, Asian Scientist, Swedish National Radio, among others.

Zubair has an MPhil in Advanced Computer Science from the University of Cambridge.