OLAP (Online Analytical Processing)

OLAP describes systems built for analytical queries: aggregating, slicing, and summarizing large volumes of historical data to answer questions like "what were sales by region and quarter last year?". These queries touch many rows but few columns, and they favor throughput over per-request latency. OLAP is the counterpart to OLTP (transactional processing), which handles many small, concurrent reads and writes for your live app. The two have opposite access patterns, so they usually run on different engines. Your product database serves OLTP; a data warehouse or engine like BigQuery, Snowflake, ClickHouse, or DuckDB serves OLAP. For SaaS builders, the practical lesson is separation. Running heavy analytical queries directly against your production OLTP database will slow down the app for real users and can cause lock contention. Instead, replicate or pipe data into an OLAP store (often columnar) and run reporting, dashboards, and BI there. This keeps transactions fast while giving analysts the freedom to run expensive scans without collateral damage.