OLAP

OLAP (Online Analytical Processing) describes systems built for analytical workloads: scanning and aggregating huge volumes of historical data to answer questions like "revenue by region by quarter." They favor columnar storage, heavy compression, and parallel scans, trading slow single-row writes for fast wide reads. It contrasts with OLTP, which handles many small transactional reads and writes.

Worked example: an analyst runs a query summing billions of order rows grouped by month and category; a columnar OLAP warehouse like BigQuery or Snowflake reads only the needed columns and returns it in seconds, where a row-oriented OLTP database would grind. Gotcha: the two are not interchangeable — pointing a dashboard’s analytical queries at your production OLTP database can lock tables and starve live traffic, which is exactly why analytics data is replicated into a separate OLAP store.