Data warehousing and data marts

Although computer systems help solve many problems in business, they use
so many different kinds of programs that they can’t always communicate
easily with each other. A tremendous number of systems make up a modern
organization — payroll, accounting, expenses, time, inventory, sales, customer
relations, software licensing, and so on. Many of these systems have
their own databases and ways of storing data. Combining data from the
tangle of systems — let alone doing something useful with the combined
data — becomes extremely difficult.
Business intelligence creates a “big picture” by storing and organizing data
from many disparate systems in one usable format. The idea is to make the
data readily accessible for reporting, analysis, and planning. A data warehouse
is a central database created for just that purpose: making the data
from all those sources useful and accessible for the organization. The idea is
to give decision-makers the information they need for making critical business
decisions.
A data mart is a more specialized tool with a similar purpose; it’s a functional
database that pulls particular information out of the overall Data Warehouse
(or even directly from source systems depending on who you ask) to answer
specific queries. For example, a manufacturing location may need to compile
some specialized data unique to the process used to make a particular product.
The overall data warehouse is too big and complex do that job (or to modify
effectively to handle it), so a smaller version — in BI lingo, a data mart — can be
created for this one manufacturing location.
The Microsoft SQL Server Database Engine manages not only data warehouses,
but also data marts — and both types of data storage can become
massive. Fortunately, SQL Server addresses this problem by storing one
database across a cluster of many different servers. This approach accommodates
the enterprise as it grows in scale.