Unified Log Processing
- Integrating and Processing Event Streams
DESCRIPTION Unified Log Processing is a foundational book introducing the ULP paradigm and presenting techniques to use it effectively in data-rich environments. The book begins with an architectural overview, illustrating how ULP addresses the thorny issues associated with processing data from multiple sources. It then guides the reader through examples using the unified log technologies Apache Kafka and Amazon Kinesis and a variety of stream processing frameworks and analytics databases. Readers learn to aggregate events from multiple sources, store them in a unified log, and build data processing applications on the resulting event streams. As readers progress through the book, they learn how to validate, filter, enrich, and store event streams, master key stream processing approaches, and explore important patterns like the lambda architecture, stream aggregation, and event re-processing. The book also dives into the methods and tools usable for event modelling and event analytics, along with scaling, resiliency, and advanced stream patterns.KEY FEATURES * Building data-driven applications that are easier to design, deploy, and maintain * Uses real-world examples and techniques * Full of figures and diagrams * Hands-on code samples and walkthroughs AUDIENCE This book assumes that the reader has written some Java code. Some Scala or Python experience is helpful but not required. ABOUT THE TECHNOLOGY Unified Log Processing is a coherent data processing architecture that combines batch and near-real time stream data, event logging and aggregation, and data processing into a unified event stream. By efficiently creating a single log of events from multiple data sources, Unified Log Processing makes it possible to design large-scale data-driven applications that are easier to design, deploy, and maintain.
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- kr. 309,95
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