"Apache Hudi for Scalable Data Lakes"
"Apache Hudi for Scalable Data Lakes" is a comprehensive guide designed for data engineers, architects, and technical leaders seeking to harness the full potential of modern data lakes. The book opens with an exploration of the core concepts and motivations behind distributed data lake architectures, offering detailed insights into the evolution of Apache Hudi within the broader open-source ecosystem. Readers are guided through Hudi's foundational principles, comparative positioning alongside Delta Lake and Apache Iceberg, and the unique design goals that enable workloads such as incremental processing, change data capture (CDC), and transactional ingestion.
Delving deep into implementation, the book meticulously covers Hudi's innovative storage mechanisms, including Copy-on-Write and Merge-on-Read table types, schema evolution strategies, and metadata management. Successive chapters provide hands-on guidance for efficient data ingestion—both batch and streaming—while illuminating Hudi's transactional guarantees, scalable indexing, and best practices for tuning write and read performance. Integration with leading query engines such as Trino, Hive, Presto, and Spark SQL is addressed in detail, alongside advanced topics like time travel queries, file management, and robust failure recovery techniques.
Beyond technical architecture, the text provides pragmatic approaches to scaling Hudi deployments in cloud and hybrid environments, ensuring data reliability, consistency, and high performance even at petabyte scale. With dedicated discussions on security, governance, DevOps automation, and compliance—including audit logging, encryption, GDPR controls, and continuous data quality—the book empowers practitioners to build resilient, secure, and agile data lake platforms. The final chapters engage with cutting-edge developments, community-driven extensions, and the dynamic future of Apache Hudi, making this volume an essential resource for staying ahead in the rapidly evolving world of big data.