Author: Streamline

Big data does not always mean “non-relational.” Many organisations store billions of records in relational engines and Data Warehouses because SQL is dependable, auditable, and easy to operationalise. The catch is that scale exposes weak query design: a join that was “fine” on a sample can multiply rows, and an analytic calculation can force heavy sorts. Two skills matter most for insight extraction at scale: window functions and complex joins.1) Start with the data model: grain, pruning, and reductionFirst, confirm the grain (what one row represents). Fact tables capture events at high detail (order lines, page views, transactions). Dimension tables…