OLAP

OLAP, or Online Analytical Processing, is a style of data processing designed for interactive analysis across large historical datasets. It favors aggregations, filtering, slicing, drilling, and comparison rather than frequent small transactional updates.

OLAP and OLTP

OLTP systems execute day-to-day business transactions with low latency and strong consistency. OLAP systems answer analytical questions by scanning and aggregating many records. Operational data is commonly copied or transformed into a warehouse so analytical workloads do not interfere with transactional systems.

Hypercube

A hypercube, also called an OLAP cube or data cube, organizes measures across multiple dimensions. Revenue might be analyzed by time, region, product, and customer segment. The cube is conceptual: implementations may use relational tables, columnar storage, materialized aggregates, or specialized multidimensional engines.

Common operations include slicing one dimension, dicing several dimensions, drilling down into detail, and rolling up into broader summaries.

Trade-offs

OLAP improves analytical speed and consistency, but introduces data freshness delays, modeling work, storage costs, and governance concerns. Precomputed aggregates improve response time while reducing flexibility.