Both data marts and data warehouses are excellent ways of following the movement of data through business. Data warehouses have as a nucleus a single large server or mainframe that provides a consolidation point for data from diverse production systems. A data warehouse protects production data sources and gathers terabytes of data into a single, unified data structure. Unfortunately, in practice, many data warehouses don't necessarily focus on or meet the needs of end users in the same way data marts do.
Data marts deal almost exclusively with servicing a specific community or department.
As a model, data marts ignore the practical difficulties of protecting production systems from the impact of extraction, focusing instead on the knowledge worker's need to have information from diverse production systems consolidated into a model that reflects the knowledge worker's understanding of the business. Because the data mart is smaller in scale, it has become a popular choice for many IT departments. Datamarts are, indeed, smaller environments designed to support the decision-making of particular workgroups. A little bit more needs to be said, however, about the overall architecture of DW and marts. It is possible to create multiple datamarts independently for each department or workgroup.