OLAP is the workload of analysis — large reads, group-bys, cubes, dashboards. Its counterpart, OLTP, is the workload of transactions. Most modern warehouses are built for the former.
In plain language
In data work, this term tends to appear once an organisation outgrows ad-hoc spreadsheets and starts thinking in pipelines and warehouses. OLAP is the workload of analysis — large reads, group-bys, cubes, dashboards. Its counterpart, OLTP, is the workload of transactions. Most modern warehouses are built for the former. If you are new to the field, the simplest mental model is this: analytic queries: aggregate, slice, and roll up. Read it once with that frame in mind, then come back and read it again — that is usually enough for the rest of the entry to make sense.
An everyday picture
Think of OLAP as the basement of a building: large, quiet, and where almost everything ends up being kept. The room upstairs is what people use; the basement is what makes the room possible.
Where it shows up
OLAP lives behind dashboards, analytics tools, recommendation engines, and back-office reports. Most users never see it directly. The team that uses it is usually the one looking at numbers all day.
A small example
Imagine the scene above. The role OLAP plays is the one its blurb describes — Analytic queries: aggregate, slice, and roll up. When last night's sales numbers arrive in a dashboard this morning, ideas like this are part of the pipework that moved them.
Common misunderstanding
One line to take with you
OLAP is leverage on what you already have. Shape the data well and the rest gets easier on its own.