Advertising analytics is a perfect stress test for databases. The data looks simple at first: date, campaign, ad group, keyword, spend, clicks, sales. Then you add thousands of advertisers, multiple marketplaces, placement breakdowns, search terms, hourly metrics, attribution windows, and suddenly every dashboard query has high-cardinality dimensions. ClickHouse is very good at this workload, but only when the table design respects how ClickHouse reads data. The first version of a schema can feel fast in development and become painful once the real cardinality arrives. Model Around Query Patterns For analytics tables, I start from the dashboards and API endpoints. Which dimensions are always filtered? Which dimensions are grouped? Which time ranges are common? The answers drive partitioning, ordering, and materialized views. CREATE TABLE campaign_daily_metrics ( event_date Date, company_id UInt64, profile_id UInt64, campaign_id UInt64, marketplace LowCardinalit...