Foreword by Raghu Ramakrishnan
Apache Iceberg is one of the leading open formats for updatable Parquet-based tables, which are emerging as the new data storage standard for analytics. Historically, relational databases have stored data row-by-row, packed into physical pages for efficient I/O. Columnar table formats, however, have proven far more efficient for query-intensive workloads. Data lakes began by supporting queries over columnar formats such as Parquet, but of course, transactional updates must also be supported efficiently to address traditional warehouse scenarios. Iceberg is emerging as a popular choice of table format supporting scenarios that require query-intensive workloads mixed with updates, and bulk ingests.
This timely and well-written book does an excellent job of presenting Iceberg, starting from the basics and extending to the architecture and how to get the best performance for a wide range of workloads, including SQL queries in Apache Spark and Dremio, as well as stream processing in Apache Flink. It also includes a chapter examining Iceberg in production settings, including the use of metadata tables and features like branching, partitioning, and snapshots to handle complex scenarios at scale. It should prove valuable to readers interested in Iceberg system development and application developers using Iceberg (or a system based on Iceberg).
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