Event Streams in Action is a foundational book introducing the ULP paradigm and presenting techniques to use it effectively in data-rich environments.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Many high-profile applications, like LinkedIn and Netflix, deliver nimble, responsive performance by reacting to user and system events as they occur. In large-scale systems, this requires efficiently monitoring, managing, and reacting to multiple event streams. Tools like Kafka, along with innovative patterns like unified log processing, help create a coherent data processing architecture for event-based applications.
About the Book
Event Streams in Action teaches you techniques for aggregating, storing, and processing event streams using the unified log processing pattern. In this hands-on guide, you'll discover important application designs like the lambda architecture, stream aggregation, and event reprocessing. You'll also explore scaling, resiliency, advanced stream patterns, and much more! By the time you're finished, you'll be designing large-scale data-driven applications that are easier to build, deploy, and maintain.
Validating and monitoring event streams
Methods for event modeling
Examples using Apache Kafka and Amazon Kinesis
About the Reader
For readers with experience coding in Java, Scala, or Python.
About the Author
Alexander Dean developed Snowplow, an open source event processing and analytics platform. Valentin Crettaz is an independent IT consultant with 25 years of experience.
Valentin Crettaz is an independent IT consultant who's been working for the past 25 years on many different challenging projects across the globe. His expertise ranges from software engineering and architecture to data science and business intelligence. His daily job boils down to leveraging the latest and most cutting-edge web, data, and streaming technologies to implement IT solutions that will help reduce the cultural gap between IT and business people.