O'Reilly eBook: Optimizing Your Apache Iceberg Lakehouse
Most Apache Iceberg performance problems are not file format problems. They are metadata problems. Understanding the architecture beneath your Iceberg tables is what separates data engineers who tune their lakehouse effectively from those who struggle to do so at scale.
This chapter from Optimizing Your Apache Iceberg Lakehouse is written for data engineers and data architects who want a deep understanding of how Apache Iceberg manages metadata, and why it matters for production data lakehouse workloads built on platforms like Starburst and Trino.
You will learn how to:
- See how Iceberg snapshots, manifest files, and manifest lists work together to version your lakehouse tables.
- Leverage Iceberg metadata pruning to reduce query I/O across large Parquet data files.
- Choose the right Iceberg catalog for your environment, whether Apache Hive Metastore, AWS Glue, or Apache Polaris.
- Support open table format interoperability across Trino, Starburst, Apache Spark, Apache Flink, and Snowflake.
- Know when to run Iceberg table maintenance, including compaction, snapshot expiration, and manifest rewrites, before they become a performance problem.
If you manage an Iceberg data lakehouse at scale and want to understand what is happening below the surface, this chapter lays the foundation on which query optimization, partitioning strategy, and data governance depend.
Most Apache Iceberg performance problems are not file format problems. They are metadata problems. Understanding the architecture beneath your Iceberg tables is what separates data engineers who tune their lakehouse effectively from those who struggle to do so at scale.
This chapter from Optimizing Your Apache Iceberg Lakehouse is written for data engineers and data architects who want a deep understanding of how Apache Iceberg manages metadata, and why it matters for production data lakehouse workloads built on platforms like Starburst and Trino.
You will learn how to:
- See how Iceberg snapshots, manifest files, and manifest lists work together to version your lakehouse tables.
- Leverage Iceberg metadata pruning to reduce query I/O across large Parquet data files.
- Choose the right Iceberg catalog for your environment, whether Apache Hive Metastore, AWS Glue, or Apache Polaris.
- Support open table format interoperability across Trino, Starburst, Apache Spark, Apache Flink, and Snowflake.
- Know when to run Iceberg table maintenance, including compaction, snapshot expiration, and manifest rewrites, before they become a performance problem.
If you manage an Iceberg data lakehouse at scale and want to understand what is happening below the surface, this chapter lays the foundation on which query optimization, partitioning strategy, and data governance depend.
