Fundamentals of data lake architecture — storage layout, partitioning, format selection, and lifecycle management.
7 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.
AI agents issue SQL iteratively, repeat query templates at high frequency, and need sub-second responses from tables designed for batch workloads. This post covers what breaks when agents hit a production Iceberg lake — and the five infrastructure layers that fix it: MCP connectivity, guardrails, multi-engine routing, self-optimizing storage, and closed-loop feedback.
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.
See how a 350TB data lake shrank to 230TB in 10 minutes by removing stale data—saving 34% in AWS S3 costs and proving the need for a control plane.
Apache Iceberg delivers speed, but without a control plane snapshots pile up, costs surge, query take more time — starting with expiration.
ExternalUnlocking performance vs. optimizing storage — choosing the right compaction strategy for your data lake.
ExternalA detailed comparison between Delta Lake and Apache Iceberg, exploring their architectures, performance characteristics, and ideal use cases to help you make the right choice.
