"Log Engineering" is a definitive, comprehensive guide to the concepts, design patterns, and real-world practices behind modern log systems. Grounded in the recognition of the log as a foundational data primitive, this book explores the historical evolution of logging-from simple application logs to highly sophisticated distributed system architectures. Readers are introduced to the append-only and immutable nature of logs, their centrality in event-driven and microservice environments, and a wide range of use cases that span observability, auditing, and real-time analytics.
With meticulous attention to both theory and practical application, the book delves into the architectural choices and trade-offs involved in deploying reliable log systems at scale. It covers essential topics such as partitioning, replication, consistency guarantees, schema evolution, and storage optimization. Dedicated chapters address the challenges of maintaining durability, ensuring data quality, and achieving compliance with stringent regulatory frameworks. Security, privacy, and operational excellence are woven throughout, equipping engineers to design robust, auditable, and secure log infrastructures.
Rounding out its coverage, "Log Engineering" examines the future of logging in the context of serverless, cloud-native, and IoT architectures, offering insights into emerging standards, machine learning-driven analytics, and real-world solutions from leading practitioners. Whether you are building large-scale data pipelines, optimizing observability tooling, or advancing your understanding of distributed systems, this book is a critical resource for engineers, architects, and technical leaders facing the growing complexity of modern data ecosystems.
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