The Serving Layer

Concepts: paColumnarVsRow, paPartitioning

The serving layer is where most candidates go thin. They spend 25 minutes on ingestion and transformation, then say 'and then analysts query it.' That's a missed opportunity. How data is consumed drives the entire upstream design - and interviewers know it. Consumer Archetypes Different consumers need different data shapes. Analysts writing SQL dashboards need pre-aggregated, denormalized gold tables with low query latency. Data scientists building ML features need wide tables with historical snapshots. Reverse ETL consumers (pushing data back to Salesforce, Braze, Iterable) need narrow, frequently-refreshed tables keyed on user_id. Name the consumer explicitly in your answer - it justifies your entire transformation design. Materialized Views vs. Pre-Computed Tables When dashboards ne