chapter ten

10 Maintaining an Iceberg lakehouse

 

This chapter covers

  • Identifying and resolving performance problems resulting from suboptimal data and metadata files
  • Running compaction jobs to optimize file layout and improve query speed
  • Managing snapshot retention to reduce the storage footprint and meet compliance needs
  • Using Iceberg metadata tables to monitor table health and guide maintenance

Designing and deploying a lakehouse is only the beginning. Long-term value comes from keeping the platform performant, governed, and resilient over time. Apache Iceberg provides powerful capabilities for data organization, schema evolution, and transaction isolation, but without proactive maintenance, those strengths can erode. As datasets grow, write patterns evolve, and business needs change, so the lakehouse must adapt.

10.1 Problem: Suboptimal data files

10.1.1 Small files

10.1.2 Poorly colocated data

10.1.3 Metadata sprawl

10.1.4 Merge-on-Read performance hits

10.2 Solution: Compaction

10.2.1 What is compaction?

10.2.2 Target file size

10.2.3 Files to be included

10.2.4 Using filters to scope compaction

10.3 Storage footprint management and data retention

10.3.1 Running snapshot expiration

10.3.2 COW vs. MOR: Implications for data retention

10.3.3 Regulatory considerations for data deletion

10.4 Exploring Apache Iceberg’s metadata tables

Summary