Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You’ll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams

Today’s digital companies demand real-time insights and immediate action for everything from purchase to fulfillment, recommendation, and more. As a result, many organizations are adopting fast data applications to accelerate the value they extract from data as it flows into the system. With this practical ebook, you’ll learn the common architectural patterns that form the foundation of successful fast data deployments. Engineers from Lightbend identify the key characteristics of fast data architectures, separate them into functional blocks, and show you how to implement those functions using components like those in the SMACK stack—Spark, Mesos, Akka, Cassandra, and Kafka, as well as others. Architects will learn how to choose, combine, and run SMACK stack technologies to build resilient, scalable, and responsive systems that your company requires. This ebook examines: The anatomy of fast data applications: the application model, streaming data sources, processing engines, and data sinks Functional composition of the SMACK stack and extensions The event backbone that connects all the major components of a fast data platform together Compute engines for transforming data into valuable insights Storage systems that form the transition between the fast data domain and client applications Patterns you can use in the data serving layer, including data-driven microservices Container orchestrators in the substrate layer that provide resources to services, frameworks, and applications