Lakehouse & Table Formats in ProductionIceberg, Hudi, Delta, and Paimon at company scale — compaction ops, format wars, catalogs, and the S3-native future.The freshest slice of the corpus: Adobe's Iceberg arc, Ancestry's 100-billion-row table, Kakao's operational takeaways, Whoop's schema-migration tooling — plus the deepest expert vein in the archive (Vanlightly's consistency-model series). This is also the natural partner-track cohort.Week 2: Operating Iceberg & Hudi at scaleCompaction, small files, snapshot hygiene, schema migrations, and MERGE economics — the maintenance loop that open formats hand to YOU.

Figure 1 — the maintenance loop. By the end of this week you can read this diagram as a prediction: which tables will degrade, when, and which knob restores them

Your team lands a CDC stream into an Iceberg table, committing every minute. The demo is flawless. Three weeks into production, dashboards that used to scan the table in 40 seconds take four minutes, the hourly MERGE bill has doubled — and total data size has barely moved. No query changed. No schema changed. Nothing about the *data* changed. So what did?This week is the discovery every lakehouse team makes in its first year: open table formats hand you powerful primitives and none of the scheduling. The answer to "what changed" is always in the files.After this week you canThe full week 2 brief is part of LeetData Pro.Case studies

Adobe — Iceberg at Adobe. After performing several successful… | by Jaemi Bremner

Ancestry — How Ancestry optimizes a 100-billion-row Iceberg table

Kakao Tech — Iceberg Operation Journey: Takeaways for DB & Server Logs

Miles Cole — Mastering Spark: The Art and Science of Table Compaction

Whoop — Glacierbase Managing Iceberg Schema Migrations at Scale

Vincent Daniel — Boost Iceberg Performance and Cut Compute Costs with Well-Scoped MERGE Statements

Exercises

A Million Small Files

Week 2 check: Table Operations

Ten Million Tiny Files

Partition Pruning, Broken