Layered Aggregation Architecture

The Real World Pipeline: In production systems handling high event volumes, materialized views exist as part of a multi layered architecture between raw data ingestion and serving. Consider a clickstream analytics pipeline for a large consumer app generating around 1 million events per second globally. Events flow into Kafka or Pub/Sub, land in a data lake like S3, then ingest into a warehouse like BigQuery or Snowflake. The raw events table grows by multiple terabytes daily with tens of billions of rows. Why Layers Matter: You cannot just jump from raw events to final dashboard queries. The optimization happens in stages, each reducing data volume and query complexity. Platform Specific Approaches: Different vendors implement the maintenance differently. BigQuery incremental materialized views only recompute partitions that changed, effective when you partition by date with tens of TB of data. Snowflake uses change data capture via streams to apply only new or modified rows. ClickHouse runs materialized views as insert triggers, updating aggregate tables synchronously or asynchronously on every insert. Smart Read Paths: Production queries often combine layers. A dashboard covering 30 days might read daily aggregates for the first 29 days and hourly aggregates or even raw data for the current incomplete day, balancing freshness with performance.