Introducing Icehouse LakeOps: Automated Maintenance, Full Observability, No Manual Ops

Apache Iceberg promised a simpler, more open lakehouse. That promise comes with a catch. Engineers still hand-roll compaction scripts. Storage costs creep up from unseen orphaned files. Dashboards slow down. The lakehouse meant to reduce operational burden has become another system to babysit.

Starburst built the Managed Icehouse to change that. Today, we’re introducing Icehouse LakeOps: a fully automated table maintenance and lake observability solution. It’s built on a dedicated engine purpose-built for Iceberg, not a generic extension bolted onto a query engine. LakeOps maintains tables regardless of where writes come from, whether Flink, Spark, dbt, or Starburst itself. No migration is required. The goal is hands-off maintenance, paired with clear visibility into table health: metadata control, storage reclamation, and query optimization.

In this session, Ahmed Niyaz and Lucas Lemos show how LakeOps solves the Iceberg Ops problem. Engineering teams can focus on getting value from the lakehouse instead of maintaining it.

Attendees will leave understanding:

• Where LakeOps fits in a modern Iceberg strategy
• Why observability matters for a healthy lakehouse, and how table health metrics surface degradation before it becomes a problem
• Common Iceberg maintenance issues caused by everyday write patterns, and why they compound over time
• How Starburst builds a comprehensive Iceberg lakehouse with a unified read/write engine and metastore optionality