B2B Integration : A Practical Guide to Collaborative E-commerce

Integration Patterns 59
warehouse built using an ETL solution can work with a message broker
to provide integration with multiple applications.
3.2.3. Data warehouses and data marts
Data warehouse
A data warehouse is defined as a data source that consolidates operational
data from multiple sources and uses consistent physical attributes,
naming conventions and semantics. The data is extracted from multiple
data sources, including legacy systems in different formats, transformed,
cleansed and then finally loaded into data warehouse, using an ETLbased
solution. Thus, a data warehouse presents a single, consolidated
view of cleansed data. Since it actually holds the data physically, unlike
a virtual data warehouse, the performance and speed of database
operations are much faster. The applications only require information
about a single SQL dialect or database API with which they can
interface with the data warehouse.
Data warehouses should be application neutral, i.e., they should not
be based on application or process oriented modeling methods. They
should have the breadth to include all the data elements in internal and
external data sources, which makes them more flexible and usable by
multiple applications.
In the majority of cases, data provided by data warehouses to
applications is not real-time and is only refreshed and synchronized on
a monthly, weekly, daily, hourly or even on a minute-to-minute basis,
depending on the operational requirements.
Data warehouses pose implementation challenges if the data in them
is updateable, with the requirement that the changes have to be
propagated back to the effected data sources. In such scenarios, a
combination of data warehouses and integration brokers (message
brokers) may work very well (see Figure 3.10).
Data marts
A data mart is defined as a data source, which contains only a subset of
the contents of a data warehouse. It contains summary or detail level