Data observability and monitoring — pipeline health, table metrics, anomaly detection, alerting, and end-to-end visibility across your data stack.
2 articles
Your Iceberg lake is overcharging you from four directions at once — storage bloat, query compute waste, compaction overhead, and engineering time. This post breaks down exactly where each dollar goes and how autonomous table management eliminates the waste without touching your pipelines.
Iceberg tables degrade silently — small files pile up, snapshots bloat metadata, and query latency creeps higher. A breakdown of the nine components every production data lake needs to stay healthy — starting with observability and telemetry collection, through compaction, snapshot management, and fleet-wide policies, to multi-engine routing and agentic AI enablement.
