Data Compaction

The consolidation of many small data files or segments into fewer larger units to improve read efficiency and metadata overhead.

Why it matters

Data Compaction helps data teams design systems that are reliable, understandable, governable, and efficient at scale.

Design considerations

• Define ownership, contracts, and expected consumers.
• Make failure, replay, compatibility, and observability behavior explicit.
• Measure quality, freshness, cost, and operational burden rather than optimizing only throughput.