10 Operationalizing Apache Iceberg
This chapter covers
- Automating Iceberg maintenance
- Using metadata for health monitoring
- Enforcing retention and compliance
- Tracking changes for governance
- Planning for disaster recovery
Building an Apache Iceberg lakehouse is only the beginning. Once data is flowing and tables are live, the real challenge begins: keeping the system healthy, secure, compliant, and resilient amid constant change. Operationalization is what transforms a functional data platform into a sustainable one. It ensures that the architecture you designed in the earlier chapters and the maintenance workflows you implemented in chapter 9 continue to support business needs reliably over time.
Apache Iceberg is built for scale, but scale brings complexity. As snapshots accumulate, delete files grow, and ingestion patterns shift, your Iceberg tables evolve in ways that require regular intervention. Compaction, snapshot expiration, and orphan file cleanup are not just technical procedures. They are operational commitments that must be executed consistently and monitored for effectiveness. Without automation and visibility, even a well-designed table can silently degrade, leading to increased query latency, rising storage costs, or worse, compliance violations.