An e-commerce platform had grown their data lake organically over years, resulting in inefficient table structures, excessive storage costs, and queries that took hours instead of minutes.
Approach
1
Analyzed query patterns and data access frequencies
2
Redesigned table schemas with proper partitioning strategies
3
Implemented Iceberg tables with optimized file sizing
4
Set up compaction jobs with automated scheduling
5
Added dbt tests for data quality validation
6
Created cost governance dashboards and alerts
Results
Query performance improved by 10x on average
Storage costs reduced by 60%
Data freshness SLAs met consistently
Predictable monthly costs with clear visibility
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