Cloud cost optimization for data lakes and lakehouses — storage waste, compute spend, query pricing, and strategies to cut lakehouse costs.
7 articles
AWS S3 bills for Iceberg lakehouses are inflated by small files, orphan data, retained snapshots, metadata overhead, and scan amplification. This guide quantifies each cost vector with S3 pricing mechanics and walks through five strategies — compaction, expiration, layout optimization, storage tiering, and engine routing — to cut storage and query spend.
A practical guide for senior data engineers expanding Snowflake into a multi-engine Iceberg lakehouse. Covers five production tools — LakeOps, managed Iceberg, Open Catalog sync, Spark, and AWS Glue — with migration patterns, operational trade-offs, and a phased rollout sequence.
State of Iceberg FinOps in 2026: where lakehouse spend leaks, what to measure, how autonomous management and optimization are replacing manual maintenance — and a practical survey of tools from cloud optimizers to control planes.
Iceberg lakehouses silently accumulate cost from small files, dead snapshots, orphan data, unoptimized layouts, and over-provisioned compute. Seven practical strategies — from deploying an autonomous control plane to leveraging partition evolution — that production data teams use to cut lakehouse spend by up to 80%.
Iceberg tables need continuous maintenance — compaction, snapshot expiration, manifest optimization, and orphan cleanup — but manual scripts break at scale. A deep look at what autonomous table maintenance means in practice: how telemetry-driven orchestration replaces reactive firefighting and keeps every table healthy without human intervention.
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 lake-wide policies, to multi-engine routing and agentic AI enablement.
